From 459b457292bdc3aee3439292955260e87c4e70f4 Mon Sep 17 00:00:00 2001 From: Philipp Baumann Date: Mon, 4 Sep 2023 21:51:11 +0000 Subject: [PATCH] Deploy from Github Actions build 6077821158 [ci skip] Build URL: https://github.com/philipp-baumann/simplerspec/actions/runs/6077821158 Commit: 0817d73c18d112134bfa78fb6d5f621bdf74bedd --- .nojekyll | 1 + dev/.nojekyll | 1 + dev/404.html | 148 ------------ dev/LICENSE.html | 433 +++++++++++++--------------------- dev/authors.html | 139 ++++------- dev/index.html | 378 +++++++++++++++-------------- dev/news/index.html | 161 +++---------- dev/pkgdown.css | 87 ++++--- dev/pkgdown.js | 4 +- dev/pkgdown.yml | 8 +- dev/reference/assess_multimodels.html | 178 +++++--------- dev/reference/average_spc.html | 170 ++++--------- dev/reference/bind_lcols_dts.html | 170 ++++--------- dev/reference/create_vip_rects.html | 142 +++-------- dev/reference/evaluate_model.html | 183 +++++--------- dev/reference/extract_lcols2dts.html | 158 ++++--------- dev/reference/extract_pls_vip.html | 144 +++-------- dev/reference/fit_pls.html | 375 +++++++++++++---------------- dev/reference/fit_rf.html | 298 ++++++++++------------- dev/reference/gather_spc.html | 175 ++++---------- dev/reference/index.html | 252 ++++---------------- dev/reference/join_chem_spec.html | 160 ++++--------- dev/reference/join_spc_chem.html | 160 ++++--------- dev/reference/merge_dts.html | 191 +++++---------- dev/reference/merge_dts_l.html | 191 +++++---------- dev/reference/pipe.html | 115 ++------- dev/reference/plot_pls_vip.html | 217 ++++++----------- dev/reference/plot_spc.html | 272 ++++++++------------- dev/reference/plot_spc_ext.html | 360 ++++++++++++---------------- dev/reference/predict_from_spc.html | 162 ++++--------- dev/reference/preprocess_spc.html | 168 ++++--------- dev/reference/read_asd.html | 142 +++-------- dev/reference/read_asd_bin.html | 173 +++++--------- dev/reference/read_opus_bin_univ.html | 165 ++++--------- dev/reference/read_opus_univ.html | 172 ++++---------- dev/reference/remove_outliers.html | 182 +++++--------- dev/reference/resample_spc.html | 267 ++++++++------------- dev/reference/select_ref_spc.html | 163 ++++--------- dev/reference/select_spc_vars.html | 180 +++++--------- dev/reference/slice_xvalues.html | 184 +++++---------- dev/reference/split_df2l.html | 152 ++++-------- dev/sitemap.xml | 12 + 42 files changed, 2430 insertions(+), 4963 deletions(-) create mode 100644 .nojekyll create mode 100644 dev/.nojekyll delete mode 100644 dev/404.html diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/.nojekyll @@ -0,0 +1 @@ + diff --git a/dev/.nojekyll b/dev/.nojekyll new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/dev/.nojekyll @@ -0,0 +1 @@ + diff --git a/dev/404.html b/dev/404.html deleted file mode 100644 index c3629a4..0000000 --- a/dev/404.html +++ /dev/null @@ -1,148 +0,0 @@ - - - - - - - - -Page not found (404) • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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- - - - -
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-

Page not found (404)

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- -Content not found. Please use links in the navbar. - -
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- - - -
-
-

Developed by Philipp Baumann.

-
- -
-

Site built with pkgdown 1.5.1.

-
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-
- - - - - - - - diff --git a/dev/LICENSE.html b/dev/LICENSE.html index 430df06..6dfc472 100644 --- a/dev/LICENSE.html +++ b/dev/LICENSE.html @@ -1,66 +1,12 @@ - - - - - - - -GNU GENERAL PUBLIC LICENSE • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -GNU GENERAL PUBLIC LICENSE • simplerspec - + + - - - -
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GNU GENERAL PUBLIC LICENSE

@@ -117,35 +50,30 @@
-
-

-Version 3, 29 June 2007

+
+

Version 3, 29 June 2007

-

Copyright (C) 2007 Free Software Foundation, Inc. http://fsf.org/
+

Copyright (C) 2007 Free Software Foundation, Inc. http://fsf.org/
Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

-
-

-Preamble

+
+

Preamble

The GNU General Public License is a free, copyleft license for software and other kinds of works.

The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too.

When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.

To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.

For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights.

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Developers that use the GNU GPL protect your rights with two steps:

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(1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.

+

Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.

For the developers’ and authors’ protection, the GPL clearly explains that there is no warranty for this free software. For both users’ and authors’ sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.

Some devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users’ freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.

Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free.

The precise terms and conditions for copying, distribution and modification follow.

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-TERMS AND CONDITIONS

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-0. Definitions.

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TERMS AND CONDITIONS

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0. Definitions.

“This License” refers to version 3 of the GNU General Public License.

“Copyright” also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.

“The Program” refers to any copyrightable work licensed under this License. Each licensee is addressed as “you”. “Licensees” and “recipients” may be individuals or organizations.

@@ -155,9 +83,8 @@ Everyone is permitted to copy and distribute verbatim copies of this license doc

To “convey” a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying.

An interactive user interface displays “Appropriate Legal Notices” to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion.

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1. Source Code.

The “source code” for a work means the preferred form of the work for making modifications to it. “Object code” means any non-source form of a work.

A “Standard Interface” means an interface that either is an official standard defined by a recognized standards body, or, in the case of interfaces specified for a particular programming language, one that is widely used among developers working in that language.

The “System Libraries” of an executable work include anything, other than the work as a whole, that (a) is included in the normal form of packaging a Major Component, but which is not part of that Major Component, and (b) serves only to enable use of the work with that Major Component, or to implement a Standard Interface for which an implementation is available to the public in source code form. A “Major Component”, in this context, means a major essential component (kernel, window system, and so on) of the specific operating system (if any) on which the executable work runs, or a compiler used to produce the work, or an object code interpreter used to run it.

@@ -165,95 +92,90 @@ Everyone is permitted to copy and distribute verbatim copies of this license doc

The Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source.

The Corresponding Source for a work in source code form is that same work.

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-2. Basic Permissions.

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2. Basic Permissions.

All rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law.

You may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. You may convey covered works to others for the sole purpose of having them make modifications exclusively for you, or provide you with facilities for running those works, provided that you comply with the terms of this License in conveying all material for which you do not control copyright. Those thus making or running the covered works for you must do so exclusively on your behalf, under your direction and control, on terms that prohibit them from making any copies of your copyrighted material outside their relationship with you.

Conveying under any other circumstances is permitted solely under the conditions stated below. Sublicensing is not allowed; section 10 makes it unnecessary.

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No covered work shall be deemed part of an effective technological measure under any applicable law fulfilling obligations under article 11 of the WIPO copyright treaty adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention of such measures.

When you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work’s users, your or third parties’ legal rights to forbid circumvention of technological measures.

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4. Conveying Verbatim Copies.

You may convey verbatim copies of the Program’s source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program.

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You may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions:

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You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways:

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a) Convey the object code in, or embodied in, a physical product
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+long as you offer spare parts or customer support for that product
+model, to give anyone who possesses the object code either (1) a
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+that supports equivalent copying facilities, provided you maintain
+clear directions next to the object code saying where to find the
+Corresponding Source.  Regardless of what server hosts the
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+
+e) Convey the object code using peer-to-peer transmission, provided
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A separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work.

A “User Product” is either (1) a “consumer product”, which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, “normally used” refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product.

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@@ -261,61 +183,56 @@ Everyone is permitted to copy and distribute verbatim copies of this license doc

The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network.

Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying.

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-7. Additional Terms.

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+

7. Additional Terms.

“Additional permissions” are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.

When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.

Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:

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All other non-permissive additional terms are considered “further restrictions” within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.

If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms.

Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.

-
-

-8. Termination.

+
+

8. Termination.

You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).

However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.

Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.

Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.

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-

-9. Acceptance Not Required for Having Copies.

+
+

9. Acceptance Not Required for Having Copies.

You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.

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-10. Automatic Licensing of Downstream Recipients.

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10. Automatic Licensing of Downstream Recipients.

Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.

An “entity transaction” is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party’s predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts.

You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.

-
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-11. Patents.

+
+

11. Patents.

A “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor’s “contributor version”.

A contributor’s “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.

Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor’s essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.

@@ -325,104 +242,92 @@ Everyone is permitted to copy and distribute verbatim copies of this license doc

A patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.

Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.

-
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-12. No Surrender of Others’ Freedom.

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12. No Surrender of Others’ Freedom.

If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.

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-13. Use with the GNU Affero General Public License.

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13. Use with the GNU Affero General Public License.

Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.

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-14. Revised Versions of this License.

+
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14. Revised Versions of this License.

The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.

Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.

If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program.

Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.

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-15. Disclaimer of Warranty.

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15. Disclaimer of Warranty.

THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.

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-16. Limitation of Liability.

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+

16. Limitation of Liability.

IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

-
-

-17. Interpretation of Sections 15 and 16.

+
+

17. Interpretation of Sections 15 and 16.

If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.

-
-

-# END OF TERMS AND CONDITIONS

+
+

# END OF TERMS AND CONDITIONS

-
-

-How to Apply These Terms to Your New Programs

+
+

How to Apply These Terms to Your New Programs

If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.

To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.

- +
<one line to give the program's name and a brief idea of what it does.>
+Copyright (C) <year>  <name of author>
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.

Also add information on how to contact you by electronic and paper mail.

If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:

- +
<program>  Copyright (C) <year>  <name of author>
+This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
+This is free software, and you are welcome to redistribute it
+under certain conditions; type 'show c' for details.

The hypothetical commands ‘show w’ and ‘show c’ should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.

-

You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see http://www.gnu.org/licenses/.

-

The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read http://www.gnu.org/philosophy/why-not-lgpl.html.

+

You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see http://www.gnu.org/licenses/.

+

The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read http://www.gnu.org/philosophy/why-not-lgpl.html.

+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/authors.html b/dev/authors.html index 4249106..db8c778 100644 --- a/dev/authors.html +++ b/dev/authors.html @@ -1,66 +1,12 @@ - - - - - - - -Authors • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Authors and Citation • simplerspec - - + + - - - -
-
-
- -
+
-
-

Authors

+
+
+

Authors

+
+ + +
  • +

    Philipp Baumann. Author, maintainer. +

    +
    • +
+
+
+

Citation

+ Source: DESCRIPTION +
-
    -
  • -

    Philipp Baumann. Author, maintainer. -

    -
    • -
+ +

Baumann P (2023). +simplerspec: Soil and plant spectroscopic model building and prediction. +R package version 0.1.0.9001, https://github.com/philipp-baumann/simplerspec. +

+ 
@Manual{,
+  title = {simplerspec: Soil and plant spectroscopic model building and prediction},
+  author = {Philipp Baumann},
+  year = {2023},
+  note = {R package version 0.1.0.9001},
+  url = {https://github.com/philipp-baumann/simplerspec},
+}
@@ -126,22 +79,20 @@ -
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/index.html b/dev/index.html index 164944a..caa7bee 100644 --- a/dev/index.html +++ b/dev/index.html @@ -23,6 +23,8 @@ + +
-
-

-simplerspec +
+

simplerspec

-
-

-Short description

-

The simplerspec package aims to facilitate spectra and additional data handling and model development for spectroscopy applications such as infrared soil spectroscopy. Different helper functions are designed to create a data and modeling workflow. Data inputs and outputs are stored in common S3 R objects (lists and data frames), using in addition data.table and tibble extensions. The functions are built to work in a pipeline and cover commonly used procedures for spectral model development and application.

+
+

Short description +

+

The simplerspec package aims to facilitate spectral and additional data handling and model development for spectroscopy applications such as infrared soil spectroscopy. Different helper functions are designed to create a data and modeling workflow. Data inputs and outputs are stored in common S3 R objects (lists and data frames), using in addition data.table and tibble extensions. The functions are built to work in a pipeline and cover commonly used procedures for spectral model development and application.

-
-

-Installation

-

The newest version of the package is available on this GitHub repository. Note that the package is still under development. If you find bugs you are highly welcome to report issues (write me an email or create an issue). You can install simplerspec using the remotes package.

-
if (!require("remotes")) install.packages("remotes")
-remotes::install_github("philipp-baumann/simplerspec")
+
+

Installation +

+

The newest version of the package is available on this GitHub repository. If you find bugs you are highly welcome to report issues (write me an email or create an issue). You can install {simplerspec} from GitHub or directly from the r-universe.

+
+# option 1
+if (!require("remotes")) install.packages("remotes")
+remotes::install_github("philipp-baumann/simplerspec")
+# option 2
+install.packages("simplerspec",
+  repos = c("https://philipp-baumann.r-universe.dev", "https://cloud.r-project.org"))
-
-

-Key features

+
+

Key features +

The current version of the package features among others the following functions:

-
    +
    1. -read_opus_univ(): Read spectra and metadata from Bruker OPUS binary files into R list
      2. +read_opus_univ(): Read spectra and metadata from Bruker OPUS binary files into R list
    2. -gather_spc(): Gather spectra and metadata from list into a tibble object (list-columns)
      3. +gather_spc(): Gather spectra and metadata from list into a tibble object (list-columns)
    3. -resample_spc(): Resample spectra to new wavenumber intervals
      4. +resample_spc(): Resample spectra to new wavenumber intervals
    4. -average_spc(): Average spectra for replicate scans
      5. +average_spc(): Average spectra for replicate scans
    5. -preprocess_spc(): Perform pre-processing of spectra
      6. +preprocess_spc(): Perform pre-processing of spectra
    6. -select_spc_vars(): Select every n-th spectral variable and corresponding X-unit values.
      7. +select_spc_vars(): Select every n-th spectral variable and corresponding X-unit values.
    7. -join_spc_chem(): Join chemical and spectral data sets by sample_id +join_spc_chem(): Join chemical and spectral data sets by sample_id
    8. -plot_spc_ext(): Extended spectral plotting; e.g. group spectra using different panels or color spectra based on chemical reference values to explore trends.
      9. +plot_spc_ext(): Extended spectral plotting; e.g. group spectra using different panels or color spectra based on chemical reference values to explore trends.
    9. -fit_pls(): Perform model tuning and evaluation based on Partial Least Squares (PLS) regression
      10. +fit_pls(): Perform model tuning and evaluation based on Partial Least Squares (PLS) regression
    10. -select_ref_spc(): Select a set of reference samples to measured by traditional analysis methods when no a priori sample data except spectra are available (based on Kennard-Stones sampling)
      11. +select_ref_spc(): Select a set of reference samples to measured by traditional analysis methods when no a priori sample data except spectra are available (based on Kennard-Stones sampling)
    11. -predict_from_spc(): Predict multiple chemical properties from a list of calibrated models and new soil spectra
      12. +predict_from_spc(): Predict multiple chemical properties from a list of calibrated models and new soil spectra
    12. -assess_multimodels(): Assess model performance given multiple pairs of predicted and measured variables.
      13. +assess_multimodels(): Assess model performance given multiple pairs of predicted and measured variables.
-
-

-Cheatsheet

-

+
+

Cheatsheet +

+

-
-

-Motivation and key concepts

+
+

Motivation and key concepts +

Many R packages are available to do tasks in spectral modeling such as pre-processing of spectral data. The motivation to create this package was:

-
    +
    1. Avoid repetition of code in model development (common source of errors).
    2. Provide a reproducible data analysis workflow for FT-IR spectroscopy.
    3. R packages are an ideal way to organize and share R code.
      4. @@ -142,144 +142,151 @@

      This package builds mainly upon functions from the following R packages:

      • -prospectr: Various utilities for pre-processing and sample selection based on spectroscopic data. An introduction to the package with examples can be found here.
        • +prospectr: Various utilities for pre-processing and sample selection based on spectroscopic data. An introduction to the package with examples can be found here.
      • -plyr and dplyr: Fast data manipulation tools with an unified interface. See here for details.
        • +plyr and dplyr: Fast data manipulation tools with an unified interface.
      • -ggplot2: Alternative plotting system for R, based on the grammar of graphics. See here.
        • +ggplot2: Alternative plotting system for R, based on the grammar of graphics. See here.
      • -caret: Classification and regression training. A set of functions that attempt to streamline the process for creating predictive models. See here for details.
        • +caret: Classification and regression training. A set of functions that attempt to streamline the process for creating predictive models. See here for details.

      Consistent and reproducible data and metadata management is an important prerequisite for spectral model development. Therefore, simplerspec functions are based on storing spectral data and related data in R data structures which keep related data in rows. Every row representing an observation contains data related to a single spectral measurement. Simplerspec functions uses tibble data frames as principal data structures because they allow to store lists within the well-known data frame structures. Lists are flexible data structures and can e.g. contain other lists, vectors, data.frames, or matrices.

      -

      List-columns features provided within the tibble framework are an excellent base to work with functional programming tools in R, which allows to efficiently write code. Simplerspec internally uses popular functional programming extension tools provided by the purrr package for processing and transforming spectra. For learning more, I would recommend this nice purrr list-column tutorial provided by Jenny Brian. Further, simplerspec well integrates with the data processing API provided by the dplyr package, which makes spectroscopic analysis tidy and easy to understand.

      +

      List-columns features provided within the tibble framework are an excellent base to work with functional programming tools in R, which allows to efficiently write code. Simplerspec internally uses popular functional programming extension tools provided by the purrr package for processing and transforming spectra. For learning more, I would recommend this nice purrr list-column tutorial provided by Jenny Brian. Further, simplerspec well integrates with the data processing API provided by the dplyr package, which makes spectroscopic analysis tidy and easy to understand.

-
-

-Example workflow

+
+

Example workflow +

Bruker FTIR spectrometers produce binary files in the OPUS format that can contain different types of spectra and many parameters such as instrument type and settings that were used at the time of data acquisition and internal processing (e.g. Fourier transform operations). Basically, the entire set of setup measurement parameters, selected spectra, supplementary metadata such as the time of measurement are written into OPUS binary files. In contrast to simple text files that contain only plain text with a defined character encoding, binary files can contain any type of data represented as sequences of bytes (a single byte is sequence of 8 bits and 1 bit either represents 0 or 1).

Simplerspec comes with reader function read_opus_univ() that is intended to be a universal Bruker OPUS file reader that extracts spectra and key metadata from files. Usually, one is mostly interested to extract the final absorbance spectra (shown as AB in the OPUS viewer software).

-
# Load simplerspec package for spectral model development wrapper functions
-library(simplerspec)
-# Load tidyverse packages: loads packages frequently used for data manipulation,
-# data tidying, import, and plotting
-library(tidyverse)
-
-################################################################################
-## Part 1: Read and pre-process spectra, read chemical data, and join
-## spectral and chemical data sets
-################################################################################
-
-## Read spectra in list ========================================================
-
-# List of OPUS binary spectra files
-lf <- dir("data/spectra/soilspec_eth_bin", full.names = TRUE)
-
-# Read spectra from files into R list
-spc_list <- read_opus_univ(fnames = lf, extract = c("spc"))
-# Returns messages:
-#> Extracted spectra data from file: <BF_lo_01_soil_cal.0>
-#> Extracted spectra data from file: <BF_lo_01_soil_cal.1>
-#> Extracted spectra data from file: <BF_lo_01_soil_cal.2>
-#> Extracted spectra data from file: <BF_lo_02_soil_cal.0>
-#> ...
-

Pipes can make R code more readable and allows step-wise data processing when developing spectral models. The pipe operator (%>%, called “then”) is a new operator in R that was introduced with the magrittr package. It facilitates readability of code and avoids to type intermediate objects. The basic behavior of the pipe operator is that the object on the left hand side is passed as the first argument to the function on the right hand side. When loading the tidyverse packages, the pipe operator is attached to the current R session. More details can be found here.

+
+# Load simplerspec package for spectral model development wrapper functions
+library(simplerspec)
+# Load tidyverse packages: loads packages frequently used for data manipulation,
+# data tidying, import, and plotting
+library(tidyverse)
+
+################################################################################
+## Part 1: Read and pre-process spectra, read chemical data, and join
+## spectral and chemical data sets
+################################################################################
+
+## Read spectra in list ========================================================
+
+# List of OPUS binary spectra files
+lf <- dir("data/spectra/soilspec_eth_bin", full.names = TRUE)
+
+# Read spectra from files into R list
+spc_list <- read_opus_univ(fnames = lf, extract = c("spc"))
+# Returns messages:
+#> Extracted spectra data from file: <BF_lo_01_soil_cal.0>
+#> Extracted spectra data from file: <BF_lo_01_soil_cal.1>
+#> Extracted spectra data from file: <BF_lo_01_soil_cal.2>
+#> Extracted spectra data from file: <BF_lo_02_soil_cal.0>
+#> ...
+

Pipes can make R code more readable and allows step-wise data processing when developing spectral models. The pipe operator (%>%, called “then”) is a new operator in R that was introduced with the magrittr package. It facilitates readability of code and avoids to type intermediate objects. The basic behavior of the pipe operator is that the object on the left hand side is passed as the first argument to the function on the right hand side. When loading the tidyverse packages, the pipe operator is attached to the current R session. More details can be found here.

The model development process can be quickly coded as the example below illustrates:

-
## Spectral data processing pipe ===============================================
-
-soilspec_tbl <- spc_list %>%
-  # Gather list of spectra data into tibble data frame
-  gather_spc() %>%
-  # Resample spectra to new wavenumber interval
-  resample_spc(wn_lower = 500, wn_upper = 3996, wn_interval = 2) %>%
-  # Average replicate scans per sample_id
-  average_spc() %>%
-  # Preprocess spectra using Savitzky-Golay first derivative with a window size
-  # of 21 points
-  preprocess_spc(select = "sg_1_w21")
-
-soilspec_tbl
-# A tibble: 284 x 11
-#>                                 unique_id             file_id         sample_id
-#>                                     <chr>               <chr>             <chr>
-#> 1 BF_lo_01_soil_cal.0_2015-11-06 14:34:10 BF_lo_01_soil_cal.0 BF_lo_01_soil_cal
-#> 2 BF_lo_01_soil_cal.1_2015-11-06 14:38:14 BF_lo_01_soil_cal.1 BF_lo_01_soil_cal
-#> 3 BF_lo_01_soil_cal.2_2015-11-06 14:40:55 BF_lo_01_soil_cal.2 BF_lo_01_soil_cal
-#> 4 BF_lo_02_soil_cal.0_2015-11-06 17:27:55 BF_lo_02_soil_cal.0 BF_lo_02_soil_cal
-#> 5 BF_lo_02_soil_cal.1_2015-11-06 17:30:19 BF_lo_02_soil_cal.1 BF_lo_02_soil_cal
-#> 6 BF_lo_02_soil_cal.2_2015-11-06 17:32:47 BF_lo_02_soil_cal.2 BF_lo_02_soil_cal
-#> 7 BF_lo_03_soil_cal.0_2015-11-09 11:32:55 BF_lo_03_soil_cal.0 BF_lo_03_soil_cal
-#> 8 BF_lo_03_soil_cal.1_2015-11-09 11:35:26 BF_lo_03_soil_cal.1 BF_lo_03_soil_cal
-#> 9 BF_lo_03_soil_cal.2_2015-11-09 11:38:08 BF_lo_03_soil_cal.2 BF_lo_03_soil_cal
-#> 10 BF_lo_04_soil_cal.0_2015-11-06 10:36:13 BF_lo_04_soil_cal.0 BF_lo_04_soil_cal
-#> # ... with 274 more rows, and 8 more variables: spc <list>, wavenumbers <list>,
-#> #   metadata <list>, spc_rs <list>, wavenumbers_rs <list>, spc_mean <list>,
-#> #   spc_pre <list>, xvalues_pre <list>
-
-
-## Read chemical reference data and join with spectral data ====================
-
-# Read chemical reference analysis data
-soilchem_tbl <- read_csv(file = "data/soilchem/soilchem_yamsys.csv")
-#> Parsed with column specification:
-#> cols(
-#>   .default = col_double(),
-#>   sample_ID = col_character(),
-#>   country = col_character(),
-#>   site = col_character(),
-#>   material = col_character(),
-#>   site_comb = col_character()
-#> )
-#> See spec(...) for full column specifications.
-
-# Join spectra tibble and chemical reference analysis tibble
-spec_chem <- join_spc_chem(
-  spc_tbl = soilspec_tbl, chem_tbl = soilchem_tbl, by = "sample_id")
-#> Joining, by = "sample_id"
-
-################################################################################
-## Part 2: Run PLS regression models for different soil variables
-################################################################################
-
-# Example Partial Least Squares (PLS) Regression model for total Carbon (C)
-# Use repeated k-fold cross-validation to tune the model (choose optimal 
-# number of PLS components) and estimate model performance on hold-out 
-# predictions of the finally chosen model (model assessment).
-# This allows to use the entire set for both model building and evaluation;
-# recommended for small data sets
-pls_C <- fit_pls(
-  # remove rows with NA in the data
-  spec_chem = spec_chem[!is.na(spec_chem$C), ],
-  response = C,
-  evaluation_method = "resampling",
-  tuning_method = "resampling",
-  resampling_method = "rep_kfold_cv",
-  pls_ncomp_max = 7 # maximum number of PLS components tested during tuning
-)
+
+## Spectral data processing pipe ===============================================
+
+soilspec_tbl <- spc_list %>%
+  # Gather list of spectra data into tibble data frame
+  gather_spc() %>% 
+  # Resample spectra to new wavenumber interval
+  resample_spc(wn_lower = 500, wn_upper = 3996, wn_interval = 2) %>%
+  # Average replicate scans per sample_id
+  average_spc() %>%
+  # Preprocess spectra using Savitzky-Golay first derivative with a window size
+  # of 21 points
+  preprocess_spc(select = "sg_1_w21")
+  
+soilspec_tbl
+# A tibble: 284 x 11
+#>                                 unique_id             file_id         sample_id
+#>                                     <chr>               <chr>             <chr>
+#> 1 BF_lo_01_soil_cal.0_2015-11-06 14:34:10 BF_lo_01_soil_cal.0 BF_lo_01_soil_cal
+#> 2 BF_lo_01_soil_cal.1_2015-11-06 14:38:14 BF_lo_01_soil_cal.1 BF_lo_01_soil_cal
+#> 3 BF_lo_01_soil_cal.2_2015-11-06 14:40:55 BF_lo_01_soil_cal.2 BF_lo_01_soil_cal
+#> 4 BF_lo_02_soil_cal.0_2015-11-06 17:27:55 BF_lo_02_soil_cal.0 BF_lo_02_soil_cal
+#> 5 BF_lo_02_soil_cal.1_2015-11-06 17:30:19 BF_lo_02_soil_cal.1 BF_lo_02_soil_cal
+#> 6 BF_lo_02_soil_cal.2_2015-11-06 17:32:47 BF_lo_02_soil_cal.2 BF_lo_02_soil_cal
+#> 7 BF_lo_03_soil_cal.0_2015-11-09 11:32:55 BF_lo_03_soil_cal.0 BF_lo_03_soil_cal
+#> 8 BF_lo_03_soil_cal.1_2015-11-09 11:35:26 BF_lo_03_soil_cal.1 BF_lo_03_soil_cal
+#> 9 BF_lo_03_soil_cal.2_2015-11-09 11:38:08 BF_lo_03_soil_cal.2 BF_lo_03_soil_cal
+#> 10 BF_lo_04_soil_cal.0_2015-11-06 10:36:13 BF_lo_04_soil_cal.0 BF_lo_04_soil_cal
+#> # ... with 274 more rows, and 8 more variables: spc <list>, wavenumbers <list>,
+#> #   metadata <list>, spc_rs <list>, wavenumbers_rs <list>, spc_mean <list>,
+#> #   spc_pre <list>, xvalues_pre <list>
+  
+
+## Read chemical reference data and join with spectral data ====================
+
+# Read chemical reference analysis data
+soilchem_tbl <- read_csv(file = "data/soilchem/soilchem_yamsys.csv")
+#> Parsed with column specification:
+#> cols(
+#>   .default = col_double(),
+#>   sample_ID = col_character(),
+#>   country = col_character(),
+#>   site = col_character(),
+#>   material = col_character(),
+#>   site_comb = col_character()
+#> )
+#> See spec(...) for full column specifications.
+
+# Join spectra tibble and chemical reference analysis tibble
+spec_chem <- join_spc_chem(
+  spc_tbl = soilspec_tbl, chem_tbl = soilchem_tbl, by = "sample_id")
+#> Joining, by = "sample_id"
+
+################################################################################
+## Part 2: Run PLS regression models for different soil variables
+################################################################################
+
+# Example Partial Least Squares (PLS) Regression model for total Carbon (C)
+# Use repeated k-fold cross-validation to tune the model (choose optimal 
+# number of PLS components) and estimate model performance on hold-out 
+# predictions of the finally chosen model (model assessment).
+# This allows to use the entire set for both model building and evaluation;
+# recommended for small data sets
+pls_C <- fit_pls(
+  # remove rows with NA in the data
+  spec_chem = spec_chem[!is.na(spec_chem$C), ],
+  response = C,
+  evaluation_method = "resampling",
+  tuning_method = "resampling",
+  resampling_method = "rep_kfold_cv",
+  pls_ncomp_max = 7 # maximum number of PLS components tested during tuning
+)
-
-

-Projects using simplerspec

+
+

Projects using simplerspec +

-
-

-Package help

+
+

Package help +

After successfully installing simplerspec, you can use the R build-in help using ?simplerspec::<fun_name>

-
-

-Credits

+
+

Like it? +

+

“Buy Me A Coffee”

+
+
+

Credits +

I would like to thank the following people for the inspiration by concepts, code and packages:

    -
  • Antoine Stevens and Leonardo Ramirez-Lopez for their contributions to the prospectr package and the Guide to Diffuse Reflectance Spectroscopy & Multivariate Calibration +
  • Antoine Stevens and Leonardo Ramirez-Lopez for their contributions to the prospectr package and the Guide to Diffuse Reflectance Spectroscopy & Multivariate Calibration
    • -
  • Andrew Sila, Tomislav Hengl, and Thomas Terhoeven-Urselmans for the read.opus() function from the soil.spec package developed at ICRAF.
    • +
  • Andrew Sila, Tomislav Hengl, and Thomas Terhoeven-Urselmans for the read.opus() function from the soil.spec package developed at ICRAF.
  • -Hadley Wickham for his work and concepts on data science within R
    • +Hadley Wickham for his work and concepts on data science within R
  • -Max Kuhn for the creation of the caret package and for his excellent teaching materials on applied predictive modeling +Max Kuhn for the creation of the caret package and for his excellent teaching materials on applied predictive modeling
@@ -288,46 +295,57 @@
-

Developed by Philipp Baumann.

+

+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

+

Site built with pkgdown 2.0.7.

@@ -336,5 +354,7 @@ + + diff --git a/dev/news/index.html b/dev/news/index.html index 201e1de..f6abf2a 100644 --- a/dev/news/index.html +++ b/dev/news/index.html @@ -1,66 +1,12 @@ - - - - - - - -Changelog • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Changelog • simplerspec + + - - - - -
-
- -
- -
+

Changelog

- Source: NEWS.md + Source: NEWS.md
-
-

-simplerspec 0.1.0.9001

-
    -
  • -resample_spc() now supports flexible spectra and x-axis types as inputs. Its interface has been carefully augmented without breaking previous functionality (#9 -
      -
    • New argument column_in specifies the string or name (unquoting support) of the input column that contains the list of spectra. The following spectrum types, which are automatically matched against the list-column that contains the corresponding x-unit value vectors, are currently supported: spc (raw or unprocessed spectra), spc_rs (resampled spectra), spc_mean (mean spectra), spc_nocomp (spectra prior atmospheric compensation), sc_sm (single channel sample spectra), sc_rf (single channel reference spectra), spc_pre (preprocessed spectra).
      • +
      +

      simplerspec 0.1.0.9001

      +
      • +resample_spc() now supports flexible spectra and x-axis types as inputs. Its interface has been carefully augmented without breaking previous functionality (#9 +
        • New argument column_in specifies the string or name (unquoting support) of the input column that contains the list of spectra. The following spectrum types, which are automatically matched against the list-column that contains the corresponding x-unit value vectors, are currently supported: spc (raw or unprocessed spectra), spc_rs (resampled spectra), spc_mean (mean spectra), spc_nocomp (spectra prior atmospheric compensation), sc_sm (single channel sample spectra), sc_rf (single channel reference spectra), spc_pre (preprocessed spectra).
        • New argument interpol_method specifying the interpolation method is introduced. Default is "linear" to achieve identical results with both prospectr v0.1.0 and v0.2.0. The current CRAN prospectr v0.2.0 has changed the default of interpol to "spline". The previous resample_spc() unfortunatelty did not explicitly state the method internally, and relied on the default instead. The measures taken ensure downward compatibility of resample_spc() with previous versions of prospectr and simplerspec.
        • The arguments gain more defensive checks inside the function (supplied types and presence of objects in spectra).
        • The function components and the help are updated accordingly. Clearer vocabulary to describe the functionality and more consistent terminology for physical quantities and R objects are used.
          • -
        -
        • +
    • Add UTF-8 support to DESCRIPTION because roxygen2 version 7.1.0 requires it.
      • -
    -
-
-

-simplerspec 0.1.0.9000

-
    -
  • Start using Kirill’s {fledge} for tracking and communicating the simplerspec development process in NEWS.
    • -
-
-
-

-simplerspec 0.1.0

-
    -
  • Added a NEWS.md file to track changes to the package
    • -
-
-
-

-simplerspec 0.1.0.1

-
    -
  • +
+
+

simplerspec 0.1.0.9000

+
  • Start using Kirill’s fledge for tracking and communicating the simplerspec development process in NEWS.
    • +
+
+

simplerspec 0.1.0

+
  • Added a NEWS.md file to track changes to the package
    • +
+
+

simplerspec 0.1.0.1

+ -
+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/pkgdown.css b/dev/pkgdown.css index c01e592..80ea5b8 100644 --- a/dev/pkgdown.css +++ b/dev/pkgdown.css @@ -56,8 +56,10 @@ img.icon { float: right; } -img { +/* Ensure in-page images don't run outside their container */ +.contents img { max-width: 100%; + height: auto; } /* Fix bug in bootstrap (only seen in firefox) */ @@ -78,11 +80,10 @@ dd { /* Section anchors ---------------------------------*/ a.anchor { - margin-left: -30px; - display:inline-block; - width: 30px; - height: 30px; - visibility: hidden; + display: none; + margin-left: 5px; + width: 20px; + height: 20px; background-image: url(./link.svg); background-repeat: no-repeat; @@ -90,17 +91,15 @@ a.anchor { background-position: center center; } -.hasAnchor:hover a.anchor { - visibility: visible; -} - -@media (max-width: 767px) { - .hasAnchor:hover a.anchor { - visibility: hidden; - } +h1:hover .anchor, +h2:hover .anchor, +h3:hover .anchor, +h4:hover .anchor, +h5:hover .anchor, +h6:hover .anchor { + display: inline-block; } - /* Fixes for fixed navbar --------------------------*/ .contents h1, .contents h2, .contents h3, .contents h4 { @@ -244,14 +243,14 @@ nav[data-toggle='toc'] .nav .nav > .active:focus > a { .ref-index th {font-weight: normal;} -.ref-index td {vertical-align: top;} +.ref-index td {vertical-align: top; min-width: 100px} .ref-index .icon {width: 40px;} .ref-index .alias {width: 40%;} .ref-index-icons .alias {width: calc(40% - 40px);} .ref-index .title {width: 60%;} .ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top;} +.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px} .ref-arguments .name {width: 20%;} .ref-arguments .desc {width: 80%;} @@ -264,31 +263,26 @@ table { /* Syntax highlighting ---------------------------------------------------- */ -pre { - word-wrap: normal; - word-break: normal; - border: 1px solid #eee; -} - -pre, code { +pre, code, pre code { background-color: #f8f8f8; color: #333; } +pre, pre code { + white-space: pre-wrap; + word-break: break-all; + overflow-wrap: break-word; +} -pre code { - overflow: auto; - word-wrap: normal; - white-space: pre; +pre { + border: 1px solid #eee; } -pre .img { +pre .img, pre .r-plt { margin: 5px 0; } -pre .img img { +pre .img img, pre .r-plt img { background-color: #fff; - display: block; - height: auto; } code a, pre a { @@ -305,9 +299,8 @@ a.sourceLine:hover { .kw {color: #264D66;} /* keyword */ .co {color: #888888;} /* comment */ -.message { color: black; font-weight: bolder;} -.error { color: orange; font-weight: bolder;} -.warning { color: #6A0366; font-weight: bolder;} +.error {font-weight: bolder;} +.warning {font-weight: bolder;} /* Clipboard --------------------------*/ @@ -365,3 +358,27 @@ mark { content: ""; } } + +/* Section anchors --------------------------------- + Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 +*/ + +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} diff --git a/dev/pkgdown.js b/dev/pkgdown.js index 7e7048f..6f0eee4 100644 --- a/dev/pkgdown.js +++ b/dev/pkgdown.js @@ -80,7 +80,7 @@ $(document).ready(function() { var copyButton = ""; - $(".examples, div.sourceCode").addClass("hasCopyButton"); + $("div.sourceCode").addClass("hasCopyButton"); // Insert copy buttons: $(copyButton).prependTo(".hasCopyButton"); @@ -91,7 +91,7 @@ // Initialize clipboard: var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { text: function(trigger) { - return trigger.parentNode.textContent; + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); } }); diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml index ade5f16..e94538e 100644 --- a/dev/pkgdown.yml +++ b/dev/pkgdown.yml @@ -1,8 +1,8 @@ -pandoc: '2.2' -pkgdown: 1.5.1 +pandoc: 2.19.2 +pkgdown: 2.0.7 pkgdown_sha: ~ -articles: [] -last_built: 2020-07-26T22:16Z +articles: {} +last_built: 2023-09-04T21:50Z urls: reference: https://philipp-baumann.github.io/simplerspec/reference article: https://philipp-baumann.github.io/simplerspec/articles diff --git a/dev/reference/assess_multimodels.html b/dev/reference/assess_multimodels.html index 141bdd2..86b09d7 100644 --- a/dev/reference/assess_multimodels.html +++ b/dev/reference/assess_multimodels.html @@ -1,68 +1,13 @@ - - - - - - - -Assess multiple pairs of measured and predicted values — assess_multimodels • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Assess multiple pairs of measured and predicted values — assess_multimodels • simplerspec - - + + - - -
-
- -
- -
+

Assess multiple pairs of measured and predicted values

- Source: R/utils-stats.R + Source: R/utils-stats.R
@@ -122,65 +54,67 @@ measured values, e.g. for different model outcome variables." /> measured values, e.g. for different model outcome variables.

- 
assess_multimodels(data, ..., .metrics = c("simplerspec", "yardstick"),
-  .model_name = "model")
- -

Arguments

- - - - - - - - - - - - - - - - - - -
data

Data frame with all measured (observed) and predicted variables.

...

Multiple arguments with observed (measured)-predicted pairs, +

+ 
assess_multimodels(
+  data,
+  ...,
+  .metrics = c("simplerspec", "yardstick"),
+  .model_name = "model"
+)
+
+ +
+

Arguments

+
data
+

Data frame with all measured (observed) and predicted variables.

+ + +
...
+

Multiple arguments with observed (measured)-predicted pairs, specified with dplyr::vars(o = <column_name>, p = <column_name>). -Column names can strings or symbols. The arguments in `...` need to be named.

.metrics

Character vector with package used for metrics calculation. +Column names can strings or symbols. The arguments in ... need to be named.

+ + +
.metrics
+

Character vector with package used for metrics calculation. Default is "simplerspec", which uses -simplerspec::evaluate_model().

.model_name

String with name for the new column that specifies the -model or the outcome variable. Default is "model".

- -

Value

- -

Data frame with with summary statistics for measured values and +simplerspec::evaluate_model().

+ + +
.model_name
+

String with name for the new column that specifies the +model or the outcome variable. Default is "model".

+ +
+
+

Value

+ + +

Data frame with with summary statistics for measured values and performance metrics for the pairs of measured and predicted values.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/average_spc.html b/dev/reference/average_spc.html index 831a226..ade33c5 100644 --- a/dev/reference/average_spc.html +++ b/dev/reference/average_spc.html @@ -1,68 +1,13 @@ - - - - - - - -Average spectra in list-column by entries in grouping column — average_spc • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Average spectra in list-column by entries in grouping column — average_spc • simplerspec - + + - - - -
-
- -
- -
+

Average spectra in list-column by entries in grouping column

- Source: R/average-spc.R + Source: R/average-spc.R
@@ -122,70 +54,66 @@ groups given in group column." /> groups given in group column.

- 
average_spc(spc_tbl, by = "sample_id", column_in = "spc_rs")
- -

Arguments

- - - - - - - - - - - - - - -
spc_tbl

Tibble data frame containing at least the grouping column -given in argument by and input spectra given in list-column column_in.

by

Character vector of length 1L or name/symbol that specifies the -column by which groups of spectra are averaged. Default is "sample_id".

column_in

Character vector of length 1L or or name/symbol that +

+ 
average_spc(spc_tbl, by = "sample_id", column_in = "spc_rs")
+
+ +
+

Arguments

+
spc_tbl
+

Tibble data frame containing at least the grouping column +given in argument by and input spectra given in list-column column_in.

+ + +
by
+

Character vector of length 1L or name/symbol that specifies the +column by which groups of spectra are averaged. Default is "sample_id".

+ + +
column_in
+

Character vector of length 1L or or name/symbol that specifies the list-column that contains the inputs spectra to be averaged. Default is "spc_rs", which are resampled spectra (i.e., resulting after -preceding resample_spc() step).

+preceding resample_spc() step).

-

Value

+
+
+

Value

+ -

Spectra tibble data frame (class "tbl_df", "tbl", "data.frame") +

Spectra tibble data frame (class "tbl_df", "tbl", "data.frame") with a new list-column of column name "spc_mean" at the last position, containing mean spectra with identical row replicates within the same by-group.

-

Details

- +
+
+

Details

For memory efficiency and subsequent modeling, consider slicing the -extra row copies of spc_mean resulting from average_spc() for example by

    -

  • split(x = spc_tbl, f = spc_tbl$<by>) %>% lapply(., function(x) x x[1, ]) %>% do.call(., rbind)

    • +extra row copies of spc_mean resulting from average_spc() for example by

    • split(x = spc_tbl, f = spc_tbl$<by>) %>% lapply(., function(x) x x[1, ]) %>% do.call(., rbind)

    • dplyr::group_by(spc_tbl, <by>) %>% dplyr::slice(1L)

      • -
    - +
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/bind_lcols_dts.html b/dev/reference/bind_lcols_dts.html index 2074950..5b3cf60 100644 --- a/dev/reference/bind_lcols_dts.html +++ b/dev/reference/bind_lcols_dts.html @@ -1,68 +1,13 @@ - - - - - - - -Bind list-columns within a tibble into a list of data.tables — bind_lcols_dts • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Bind list-columns within a tibble into a list of data.tables — bind_lcols_dts • simplerspec - + + - - - -
-
- -
- -
+

Bind list-columns within a tibble into a list of data.tables

- Source: R/plot-spc-extended.R + Source: R/plot-spc-extended.R
@@ -122,65 +54,63 @@ of data.tables." /> of data.tables.

- 
bind_lcols_dts(spc_tbl, lcols, spc_id = "unique_id", group_id = "sample_id")
- -

Arguments

- - - - - - - - - - - - - - - - - - -
spc_tbl

Spectral data in a tibble data frame (classes "tibble_df", -"tbl" and "data.frame").

lcols

Character vector of column names of list-columns to be bound -into a list of data.tables

spc_id

Character vector denoting column name for a unique spectrum ID. -Default is "unique_id".

group_id

Character vector denoting column name for the spectrum group +

+ 
bind_lcols_dts(spc_tbl, lcols, spc_id = "unique_id", group_id = "sample_id")
+
+ +
+

Arguments

+
spc_tbl
+

Spectral data in a tibble data frame (classes "tibble_df", +"tbl" and "data.frame").

+ + +
lcols
+

Character vector of column names of list-columns to be bound +into a list of data.tables

+ + +
spc_id
+

Character vector denoting column name for a unique spectrum ID. +Default is "unique_id".

+ + +
group_id
+

Character vector denoting column name for the spectrum group ID. Default is "sample_id". The group ID can later be used for -plotting spectra by group (e.g. by using different colors or panels).

+plotting spectra by group (e.g. by using different colors or panels).

-

Value

+
+
+

Value

+ -

A list of data.tables. Elements contain data from list-columns +

A list of data.tables. Elements contain data from list-columns specified in lcols argument as data.tables. All data.tables contain in addition spc_id and group_id columns.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/create_vip_rects.html b/dev/reference/create_vip_rects.html index a28b0ef..2de36eb 100644 --- a/dev/reference/create_vip_rects.html +++ b/dev/reference/create_vip_rects.html @@ -1,72 +1,17 @@ - - - - - - - -Create a data frame containing start and end positions (wavenumbers) -where variable importance in projection (VIP) is > 1 — create_vip_rects • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Create a data frame containing start and end positions (wavenumbers) +where variable importance in projection (VIP) is > 1 — create_vip_rects • simplerspec - - - - - - - - - - - + + - - -
-
- -
- -
+

Create a data frame containing start and end positions (wavenumbers) where variable importance in projection (VIP) is > 1

- Source: R/pls-vip.R + Source: R/pls-vip.R
@@ -129,49 +61,47 @@ are returned as data frame. The functions can be used as helper function for plotting VIP.

- 
create_vip_rects(df_vip)
+
+ 
create_vip_rects(df_vip)
+
-

Arguments

- - - - - - -
df_vip

Data frame containing wavenumber and vip columns -(numeric)

+
+

Arguments

+
df_vip
+

Data frame containing wavenumber and vip columns +(numeric)

-

Value

+
+
+

Value

+ -

Data.frame containing vectors start (numeric; wavenumber), +

Data.frame containing vectors start (numeric; wavenumber), end (numeric; wavenumber) and group (integer; values are 1:length(start)).

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/evaluate_model.html b/dev/reference/evaluate_model.html index 3719868..8533867 100644 --- a/dev/reference/evaluate_model.html +++ b/dev/reference/evaluate_model.html @@ -1,69 +1,14 @@ - - - - - - - -Calculate model evaluation metrics — evaluate_model • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calculate model evaluation metrics — evaluate_model • simplerspec - - - - - - - - - - - - - + + -
-
- -
- -
+

Calculate model evaluation metrics

- Source: R/utils-stats.R + Source: R/utils-stats.R
@@ -124,69 +56,64 @@ evaluation statistics for assessing agreement between observed (obs predicted (pred) values.

- 
evaluate_model(data, obs, pred)
-
-summary_df(df, x, y)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - -
data

data.frame with predicted and observed data in columns.

obs

Column that contains observed values, symbol/name or -character (wrapped in "").

pred

Column that contains predicted values, symbol/name or -character (wrapped in "").

df

data.frame with predicted and observed data in columns.

x

Column that contains observed values, symbol/name or -character (wrapped in "").

y

Column that contains predicted values, symbol/name or -character (wrapped in "").

+
+ 
evaluate_model(data, obs, pred)
+
+summary_df(df, x, y)
+
+ +
+

Arguments

+
data
+

data.frame with predicted and observed data in columns.

+ + +
obs
+

Column that contains observed values, symbol/name or +character (wrapped in "").

+
pred
+

Column that contains predicted values, symbol/name or +character (wrapped in "").

+ + +
df
+

data.frame with predicted and observed data in columns.

+ + +
x
+

Column that contains observed values, symbol/name or +character (wrapped in "").

+ + +
y
+

Column that contains predicted values, symbol/name or +character (wrapped in "").

+ +
+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/extract_lcols2dts.html b/dev/reference/extract_lcols2dts.html index 47f9ba5..6967ac0 100644 --- a/dev/reference/extract_lcols2dts.html +++ b/dev/reference/extract_lcols2dts.html @@ -1,70 +1,15 @@ - - - - - - - -Extract multiple tibble list-columns and return data as list of -data.tables — extract_lcols2dts • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Extract multiple tibble list-columns and return data as list of +data.tables — extract_lcols2dts • simplerspec + + - - - - -
-
- -
- -
+

Extract multiple tibble list-columns and return data as list of data.tables

- Source: R/tbl-lcol-map-funprog.R + Source: R/tbl-lcol-map-funprog.R
@@ -125,53 +57,51 @@ data.tables

into single data tables and return a list of data.tables.

- 
extract_lcols2dts(spc_tbl, lcols)
- -

Arguments

- - - - - - - - - - -
spc_tbl

Spectral tibble (data frame) with spectral data contained -in list-columns

lcols

Character vector containing names of list-columns to be -extracted into a list of data.tables

- -

Value

- -

List of data.tables. Each element is a data.table derivied from a +

+ 
extract_lcols2dts(spc_tbl, lcols)
+
+ +
+

Arguments

+
spc_tbl
+

Spectral tibble (data frame) with spectral data contained +in list-columns

+ + +
lcols
+

Character vector containing names of list-columns to be +extracted into a list of data.tables

+ +
+
+

Value

+ + +

List of data.tables. Each element is a data.table derivied from a list-column specified in the lcols argument.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/extract_pls_vip.html b/dev/reference/extract_pls_vip.html index c7c29e5..f09d52b 100644 --- a/dev/reference/extract_pls_vip.html +++ b/dev/reference/extract_pls_vip.html @@ -1,74 +1,19 @@ - - - - - - - -Extract VIPs (variable importance in the projection) for a PLS +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Extract VIPs (variable importance in the projection) for a PLS regression model output returned from model fitting with -<code>simplerspec::fit_pls()</code> — extract_pls_vip • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + - - -
-
- -
- -
+

Extract VIPs (variable importance in the projection) for a PLS regression model output returned from model fitting with simplerspec::fit_pls()

- Source: R/pls-vip.R + Source: R/pls-vip.R

VIPs are extracted based on the finalModel sublist -in the caret::train output contained in the model element -of the simplerspec::fit_pls() model output list. The VIPs for +in the caret::train output contained in the model element +of the simplerspec::fit_pls() model output list. The VIPs for derived number of PLS components in the finalModel are computed.

- 
extract_pls_vip(mout)
+
+ 
extract_pls_vip(mout)
+
-

Arguments

- - - - - - -
mout

Model output list returned from simplerspec::fit_pls().

+
+

Arguments

+
mout
+

Model output list returned from simplerspec::fit_pls().

-

Value

+
+
+

Value

+ -

A tibble data frame with columns wavenumber and correponding +

A tibble data frame with columns wavenumber and correponding VIP values in the column vip for the finally chosen PLS regression model at the final number of PLS components.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/fit_pls.html b/dev/reference/fit_pls.html index 0dc9080..c0c96c7 100644 --- a/dev/reference/fit_pls.html +++ b/dev/reference/fit_pls.html @@ -1,68 +1,13 @@ - - - - - - - -Calibration sampling, model tuning, and PLS regression — fit_pls • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calibration sampling, model tuning, and PLS regression — fit_pls • simplerspec + + - - - - -
-
- -
- -
+

Calibration sampling, model tuning, and PLS regression

- Source: R/pls-modeling.R + Source: R/pls-modeling.R
@@ -122,100 +54,130 @@ calibration set for model tuning" /> calibration set for model tuning

- 
fit_pls(spec_chem, response, variable = NULL, center = TRUE, scale = TRUE,
-  evaluation_method = "test_set", validation = TRUE,
-  split_method = "ken_stone", ratio_val = 1/3, ken_sto_pc = 2, pc,
-  invert = TRUE, tuning_method = "resampling",
-  resampling_method = "kfold_cv", cv = NULL, resampling_seed = 123,
-  pls_ncomp_max = 20, ncomp_fixed = 5, print = TRUE,
-  env = parent.frame())
-
-pls_ken_stone(spec_chem, response, variable = NULL, center = TRUE,
-  scale = TRUE, evaluation_method = "test_set", validation = TRUE,
-  split_method = "ken_stone", ratio_val = 1/3, ken_sto_pc = 2, pc,
-  invert = TRUE, tuning_method = "resampling",
-  resampling_method = "kfold_cv", cv = NULL, resampling_seed = 123,
-  pls_ncomp_max = 20, ncomp_fixed = 5, print = TRUE,
-  env = parent.frame())
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
spec_chem

Tibble that contains spectra, metadata and chemical +

+ 
fit_pls(
+  spec_chem,
+  response,
+  variable = NULL,
+  center = TRUE,
+  scale = TRUE,
+  evaluation_method = "test_set",
+  validation = TRUE,
+  split_method = "ken_stone",
+  ratio_val = 1/3,
+  ken_sto_pc = 2,
+  pc,
+  invert = TRUE,
+  tuning_method = "resampling",
+  resampling_method = "kfold_cv",
+  cv = NULL,
+  resampling_seed = 123,
+  pls_ncomp_max = 20,
+  ncomp_fixed = 5,
+  print = TRUE,
+  env = parent.frame()
+)
+
+pls_ken_stone(
+  spec_chem,
+  response,
+  variable = NULL,
+  center = TRUE,
+  scale = TRUE,
+  evaluation_method = "test_set",
+  validation = TRUE,
+  split_method = "ken_stone",
+  ratio_val = 1/3,
+  ken_sto_pc = 2,
+  pc,
+  invert = TRUE,
+  tuning_method = "resampling",
+  resampling_method = "kfold_cv",
+  cv = NULL,
+  resampling_seed = 123,
+  pls_ncomp_max = 20,
+  ncomp_fixed = 5,
+  print = TRUE,
+  env = parent.frame()
+)
+
+ +
+

Arguments

+
spec_chem
+

Tibble that contains spectra, metadata and chemical reference as list-columns. The tibble to be supplied to spec_chem can -be generated by the `join_chem_spc() function`

response

Response variable as symbol or name +be generated by the join_chem_spc() function

+ + +
response
+

Response variable as symbol or name (without quotes, no character string). The provided response symbol needs to be -a column name in the spec_chem tibble.

variable

Depreciated and replaced by `response`

center

Logical whether to perform mean centering of each spectrum column +a column name in the spec_chem tibble.

+ + +
variable
+

Depreciated and replaced by response

+ + +
center
+

Logical whether to perform mean centering of each spectrum column (e.g. wavenumber or wavelength) after common spectrum preprocessing. Default is -center = TRUE

scale

Logical whether to perform standard deviation scaling +center = TRUE

+ + +
scale
+

Logical whether to perform standard deviation scaling of each spectrum column (e.g. wavenumber or wavelength) after common -spectrum preprocessing. Default is scale = TRUE

evaluation_method

Character string stating evaluation method. +spectrum preprocessing. Default is scale = TRUE

+ + +
evaluation_method
+

Character string stating evaluation method. Either "test_set" (default) or "resampling". "test_set" will split the data into a calibration (training) and validation (test) set, and evaluate the final model by predicting on the validation set. If "resampling", the finally selected model will be evaluated based -on the cross-validation hold-out predictions.

validation

Depreciated and replaced by evaluation_method. -Default is TRUE.

split_method

Method how to to split the data into a independent test +on the cross-validation hold-out predictions.

+ + +
validation
+

Depreciated and replaced by evaluation_method. +Default is TRUE.

+ + +
split_method
+

Method how to to split the data into a independent test set. Default is "ken_sto", which will select samples for calibration based on Kennard-Stone sampling algorithm of preprocessed spectra. The proportion of validation to the total number of samples can be specified in the argument ratio_val. -split_method = "random" will create a single random split.

ratio_val

Ratio of validation (test) samples to -total number of samples (calibration (training) and validation (test)).

ken_sto_pc

Number of component used +split_method = "random" will create a single random split.

+ + +
ratio_val
+

Ratio of validation (test) samples to +total number of samples (calibration (training) and validation (test)).

+ + +
ken_sto_pc
+

Number of component used for calculating mahalanobsis distance on PCA scores for computing Kennard-Stone algorithm. Default is ken_sto_pc = 2, which will use the first two PCA -components.

pc

Depreciated; renamed argument is `ken_sto_pc`.

invert

Logical

tuning_method

Character specifying tuning method. Tuning method +components.

+ + +
pc
+

Depreciated; renamed argument is ken_sto_pc.

+ + +
invert
+

Logical

+ + +
tuning_method
+

Character specifying tuning method. Tuning method affects how caret selects a final tuning value set from a list of candidate values. Possible values are "resampling", which will use a specified resampling method such as repeated k-fold cross-validation (see @@ -225,77 +187,72 @@ that lead to optimal model performance. The value "none" will force caret to compute a final model for a predefined canditate PLS tuning parameter number of PLS components. In this case, the value supplied by ncomp_fixed` is used to set model complexity at -a fixed number of components.

resampling_method

Character specifying resampling method. Currently, +a fixed number of components.

+ + +
resampling_method
+

Character specifying resampling method. Currently, "kfold_cv" (default, performs 10-fold cross-validation), "rep_kfold_cv" (performs 5-times repeated 10-fold cross-validation), "loocv" (performs leave-one-out cross-validation), and "none" -(if resampling_method = "none") are supported.

cv

Depreciated. Use resampling_method instead.

resampling_seed

Random seed (integer) that will be used for generating -resampling indices, which will be supplied to caret::trainControl. +(if resampling_method = "none") are supported.

+ + +
cv
+

Depreciated. Use resampling_method instead.

+ + +
resampling_seed
+

Random seed (integer) that will be used for generating +resampling indices, which will be supplied to caret::trainControl. This makes sure that modeling results are constant when re-fitting. -Default is resampling_seed = 123.

pls_ncomp_max

Maximum number of PLS components that are evaluated +Default is resampling_seed = 123.

+ + +
pls_ncomp_max
+

Maximum number of PLS components that are evaluated by caret::train. Caret will aggregate a performance profile using resampling -for an integer sequence from 1 to pls_ncomp_max

ncomp_fixed

Integer of fixed number of PLS components. Will only be +for an integer sequence from 1 to pls_ncomp_max

+ + +
ncomp_fixed
+

Integer of fixed number of PLS components. Will only be used when tuning_method = "none" and resampling_method = "none" -are used.

print

Logical expression whether model evaluation graphs shall be -printed

env

Environment where function is evaluated. Default is -parent.frame.

+are used.

+ + +
print
+

Logical expression whether model evaluation graphs shall be +printed

+
env
+

Environment where function is evaluated. Default is +parent.frame.

+ +
+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/fit_rf.html b/dev/reference/fit_rf.html index 8090d13..27f3a0a 100644 --- a/dev/reference/fit_rf.html +++ b/dev/reference/fit_rf.html @@ -1,68 +1,13 @@ - - - - - - - -Calibration sampling, and random forest model tuning and evaluation — fit_rf • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Calibration sampling, and random forest model tuning and evaluation — fit_rf • simplerspec - - + + - - -
-
- -
- -
+

Calibration sampling, and random forest model tuning and evaluation

- Source: R/pls-modeling.R + Source: R/pls-modeling.R
@@ -122,78 +54,91 @@ calibration set for model tuning" /> calibration set for model tuning

- 
fit_rf(spec_chem, response, variable = NULL, evaluation_method = "test_set",
-  validation = NULL, split_method = "ken_stone", ratio_val,
-  ken_sto_pc = 2, pc = NULL, invert = TRUE,
-  tuning_method = "resampling", resampling_seed = 123, cv = NULL,
-  ntree_max = 500, print = TRUE, env = parent.frame())
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
spec_chem

Tibble that contains spectra, metadata and chemical +

+ 
fit_rf(
+  spec_chem,
+  response,
+  variable = NULL,
+  evaluation_method = "test_set",
+  validation = NULL,
+  split_method = "ken_stone",
+  ratio_val,
+  ken_sto_pc = 2,
+  pc = NULL,
+  invert = TRUE,
+  tuning_method = "resampling",
+  resampling_seed = 123,
+  cv = NULL,
+  ntree_max = 500,
+  print = TRUE,
+  env = parent.frame()
+)
+
+ +
+

Arguments

+
spec_chem
+

Tibble that contains spectra, metadata and chemical reference as list-columns. The tibble to be supplied to spec_chem can -be generated by the `join_chem_spc() function`

response

Response variable as symbol or name +be generated by the join_chem_spc() function

+ + +
response
+

Response variable as symbol or name (without quotes, no character string). The provided response symbol needs to be -a column name in the spec_chem tibble.

variable

Depreciated and replaced by `response`

evaluation_method

Character string stating evaluation method. +a column name in the spec_chem tibble.

+ + +
variable
+

Depreciated and replaced by response

+ + +
evaluation_method
+

Character string stating evaluation method. Either "test_set" (default) or "resampling". "test_set" will split the data into a calibration (training) and validation (test) set, and evaluate the final model by predicting on the validation set. If "resampling", the finally selected model will be evaluated based -on the cross-validation hold-out predictions.

validation

Depreciated and replaced by evaluation_method. -Default is TRUE.

split_method

Method how to to split the data into a independent test +on the cross-validation hold-out predictions.

+ + +
validation
+

Depreciated and replaced by evaluation_method. +Default is TRUE.

+ + +
split_method
+

Method how to to split the data into a independent test set. Default is "ken_sto", which will select samples for calibration based on Kennard-Stone sampling algorithm of preprocessed spectra. The proportion of validation to the total number of samples can be specified in the argument ratio_val. -split_method = "random" will create a single random split.

ratio_val

Ratio of validation (test) samples to -total number of samples (calibration (training) and validation (test)).

ken_sto_pc

Number of component used +split_method = "random" will create a single random split.

+ + +
ratio_val
+

Ratio of validation (test) samples to +total number of samples (calibration (training) and validation (test)).

+ + +
ken_sto_pc
+

Number of component used for calculating mahalanobsis distance on PCA scores for computing Kennard-Stone algorithm. Default is ken_sto_pc = 2, which will use the first two PCA -components.

pc

Depreciated; renamed argument is `ken_sto_pc`.

invert

Logical

tuning_method

Character specifying tuning method. Tuning method +components.

+ + +
pc
+

Depreciated; renamed argument is ken_sto_pc.

+ + +
invert
+

Logical

+ + +
tuning_method
+

Character specifying tuning method. Tuning method affects how caret selects a final tuning value set from a list of candidate values. Possible values are "resampling", which will use a specified resampling method such as repeated k-fold cross-validation (see @@ -203,63 +148,58 @@ that lead to optimal model performance. The value "none" will force caret to compute a final model for a predefined canditate PLS tuning parameter number of PLS components. In this case, the value supplied by ncomp_fixed` is used to set model complexity at -a fixed number of components.

resampling_seed

Random seed (integer) that will be used for generating -resampling indices, which will be supplied to caret::trainControl. +a fixed number of components.

+ + +
resampling_seed
+

Random seed (integer) that will be used for generating +resampling indices, which will be supplied to caret::trainControl. This makes sure that modeling results are constant when re-fitting. -Default is resampling_seed = 123.

cv

Depreciated. Use resampling_method instead.

ntree_max

Maximum random forest trees +Default is resampling_seed = 123.

+ + +
cv
+

Depreciated. Use resampling_method instead.

+ + +
ntree_max
+

Maximum random forest trees by caret::train. Caret will aggregate a performance profile using resampling -for an integer sequence from 1 to ntree_max trees.

print

Logical expression whether model evaluation graphs shall be -printed

env

Environment where function is evaluated. Default is -parent.frame.

+for an integer sequence from 1 to ntree_max trees.

+ + +
print
+

Logical expression whether model evaluation graphs shall be +printed

+ +
env
+

Environment where function is evaluated. Default is +parent.frame.

+ +
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/gather_spc.html b/dev/reference/gather_spc.html index 4d0cb34..637bddd 100644 --- a/dev/reference/gather_spc.html +++ b/dev/reference/gather_spc.html @@ -1,48 +1,7 @@ - - - - - - - -Gather measurements of different spectra types, corresponding -x-axis values and metadata from nested list. — gather_spc • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Gather measurements of different spectra types, corresponding +x-axis values and metadata from nested list. — gather_spc • simplerspec - - - - + + -
-
- -
- -
+

Gather measurements of different spectra types, corresponding x-axis values and metadata from nested list.

- Source: R/gather-spc.R + Source: R/gather-spc.R
@@ -147,29 +79,28 @@ subsequent transformations, for example using the standardized functions of the simplerspec data processing pipeline.

- 
gather_spc(data, spc_types = "spc")
+
+ 
gather_spc(data, spc_types = "spc")
+
-

Arguments

- - - - - - - - - - -
data

Recursive list named with filename (file_id) at first level +

+

Arguments

+
data
+

Recursive list named with filename (file_id) at first level entries, where each element containing a sample measurement has nested metadata ("metadata"), spectra types (see spc_types), corresponding x-axis values (see section "Details on spectra data checks and matching"). The data list is a structural convention to organize spectra and their metadata. It follows for example the list structure returned from the Bruker -OPUS binary reader simplerspec::read_opus_univ().

spc_types

Character vector with the spectra types to be extracted +OPUS binary reader simplerspec::read_opus_univ().

+ + +
spc_types
+

Character vector with the spectra types to be extracted from data list and gathered into list-columns. The spectra type names need to exactly follow the naming conventions, and the element names and contents need to be present at the second list hierarchy of data. These values are -allowed:

    -

  • "spc" (default): final raw spectra after atmospheric compensation, if +allowed:

    • "spc" (default): final raw spectra after atmospheric compensation, if performed (named AB in Bruker OPUS software; results from referencing sample to reference single channel reflectance and transforming to absorbance).

      • @@ -181,15 +112,15 @@ spectra)

      without x-axis list-columns are matched and returned)

    • "ig_rf": Interferograms of the reference spectra (currently only spectra without x-axis list-columns are matched and returned)

      • -
+ -

Value

+
+
+

Value

+ -

Spectra tibble (spc_tbl with classes "tbl_df", "tbl", and -"data.frame") with the following (list-)columns:

    -

  • "unique_id": Character vector with unique measurement identifier, likely +

    Spectra tibble (spc_tbl with classes "tbl_df", "tbl", and +"data.frame") with the following (list-)columns:

    • "unique_id": Character vector with unique measurement identifier, likely a string with file names in combination with date and time (extracted from each "metadata" data frame column).

    • "file_id" : Character vector with file name including the extension @@ -204,15 +135,13 @@ List(s) of data.table's containing spectra type(s).

      • "wavenumbers_sc_rf", "wavelengths_sc_rf", or "x_values_sc_rf": List(s) of numeric vectors with matched x-axis values (see "Details on spectra data checks and matching" below).

      -
    - -

    Details on spectra data checks and matching

    - +
+
+

Details on spectra data checks and matching

gather_spc() checks whether these conditions are met for each measurement -in the list data:

    -

  1. Make sure that the first level data elements are named (assumed to be +in the list data:

    1. Make sure that the first level data elements are named (assumed to be the file name the data originate from), and remove missing measurements with an informative message.

    2. Remove any duplicated file names and raise a message if there are @@ -223,8 +152,7 @@ all data elements for incomplete spectral measurements.

    3. Match spectra types and possible corresponding x-axis types from a lookup list. For each selected spectrum type (left), at least one of the element names of the x-axis type (right) needs to be present for each -measurement in the list data:

        -

      • "spc" : "wavenumbers", "wavelengths", or "x_values"

        • +measurement in the list data:

        • "spc" : "wavenumbers", "wavelengths", or "x_values"

        • "spc_nocomp" : "wavenumbers", "wavelengths", or "x_values"

        • "sc_sm" : "wavenumbers_sc_sm", "wavelengths_sc_sm", or "x_values_sc_sm"

          • @@ -234,34 +162,29 @@ measurement in the list data:

          • Check if "metadata" elements are present and remove data elements for measurements with missing or incorrectly named metadata elements (message).

            • -
    - +
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/index.html b/dev/reference/index.html index 5ca00ce..dc99cca 100644 --- a/dev/reference/index.html +++ b/dev/reference/index.html @@ -1,66 +1,12 @@ - - - - - - - -Function reference • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Function reference • simplerspec + + - - - - -
-
- -
- -
+

Reference

- - - - - - - - - - -
-

All functions

+ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+

All functions

+

assess_multimodels()

Assess multiple pairs of measured and predicted values

+

average_spc()

Average spectra in list-column by entries in grouping column

+

bind_lcols_dts()

Bind list-columns within a tibble into a list of data.tables

+

create_vip_rects()

Create a data frame containing start and end positions (wavenumbers) where variable importance in projection (VIP) is > 1

+

evaluate_model() summary_df()

Calculate model evaluation metrics

+

extract_lcols2dts()

Extract multiple tibble list-columns and return data as list of data.tables

+

extract_pls_vip()

Extract VIPs (variable importance in the projection) for a PLS regression model output returned from model fitting with -simplerspec::fit_pls()

+simplerspec::fit_pls()

fit_pls() pls_ken_stone()

Calibration sampling, model tuning, and PLS regression

+

fit_rf()

Calibration sampling, and random forest model tuning and evaluation

+

gather_spc()

Gather measurements of different spectra types, corresponding x-axis values and metadata from nested list.

+

join_chem_spec()

Join chemical and spectral data frames

+

join_spc_chem()

Join spectra data and chemical data tibbles

+

merge_dts()

Merge list-columns of spectra, x-axis values, metadata and additional measured variables into a single long form data.table

+

merge_dts_l()

Wrapper function around merge_dts() for list of tibbles to +

Wrapper function around merge_dts() for list of tibbles to aggregate data for plotting.

+

plot_pls_vip()

Plot stacked ggplot2 graphs with the Variable Importance for the Projection (VIP) scores, mean replicate spectra (absorbance) per sample_id, and the preprocessed spectra.

+

plot_spc()

Plot tibble spectra

+

plot_spc_ext()

ggplot2 wrapper for extended spectra plotting

+

predict_from_spc()

Predict soil properties of new spectra based on a list of calibration models

+

preprocess_spc()

Preprocess spectra

+

read_asd()

Read ASD fieldspec spectrometer data export into into simplerspec spectra tibble.

+

read_asd_bin()

Read ASD binary files and gather spectra and metadata in tibble data frame.

+

read_opus_bin_univ()

Read a Bruker OPUS spectrum binary file

+

read_opus_univ()

Read a list of Bruker OPUS spectrum binary files.

+

remove_outliers()

Remove outlier spectra

+

resample_spc()

Resample spectra in list-column to new x-axis interval

+

select_ref_spc()

Select a set of reference spectra to be measured by reference analysis methods

+

select_spc_vars()

Select every n-th spectral variable for all spectra and x-values in spectral tibble (spc_tbl)

+

slice_xvalues()

Slice spectra into defined x-axis ranges

+

split_df2l()

Split a tibble data frame into a list of tibbles by a group column

- +
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/join_chem_spec.html b/dev/reference/join_chem_spec.html index 7f9f1c6..806ca0b 100644 --- a/dev/reference/join_chem_spec.html +++ b/dev/reference/join_chem_spec.html @@ -1,68 +1,13 @@ - - - - - - - -Join chemical and spectral data frames — join_chem_spec • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Join chemical and spectral data frames — join_chem_spec • simplerspec - + + - - - -
-
- -
- -
+

Join chemical and spectral data frames

- Source: R/join-chem-spectra.R + Source: R/join-chem-spectra.R
@@ -122,55 +54,53 @@ data (data.frame)." /> data (data.frame).

- 
join_chem_spec(dat_chem, dat_spec, by = "sample_ID")
- -

Arguments

- - - - - - - - - - - - - - -
dat_chem

data.frame that contains chemical values of -the sample

dat_spec

List that contains spectral data

by

character of column name that defines sample_ID

- -

Value

- -

List: xxx

+
+ 
join_chem_spec(dat_chem, dat_spec, by = "sample_ID")
+
+ +
+

Arguments

+
dat_chem
+

data.frame that contains chemical values of +the sample

+ + +
dat_spec
+

List that contains spectral data

+ + +
by
+

character of column name that defines sample_ID

+ +
+
+

Value

+ + +

List: xxx

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/join_spc_chem.html b/dev/reference/join_spc_chem.html index a53b0b9..133a6d2 100644 --- a/dev/reference/join_spc_chem.html +++ b/dev/reference/join_spc_chem.html @@ -1,68 +1,13 @@ - - - - - - - -Join spectra data and chemical data tibbles — join_spc_chem • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Join spectra data and chemical data tibbles — join_spc_chem • simplerspec - + + - - - -
-
- -
- -
+

Join spectra data and chemical data tibbles

- Source: R/join-chem-spectra.R + Source: R/join-chem-spectra.R
@@ -122,55 +54,53 @@ data (tibble class)." /> data (tibble class).

- 
join_spc_chem(spc_tbl, chem_tbl, by = "sample_id")
- -

Arguments

- - - - - - - - - - - - - - -
spc_tbl

Tibble that contains spectral data

chem_tbl

Tibble that contains chemical reference values of -the samples

by

character of column name that defines sample_ID

- -

Value

- -

Tibble joined by sample_id

+
+ 
join_spc_chem(spc_tbl, chem_tbl, by = "sample_id")
+
+ +
+

Arguments

+
spc_tbl
+

Tibble that contains spectral data

+ + +
chem_tbl
+

Tibble that contains chemical reference values of +the samples

+ + +
by
+

character of column name that defines sample_ID

+ +
+
+

Value

+ + +

Tibble joined by sample_id

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/merge_dts.html b/dev/reference/merge_dts.html index 0c68cd4..7719f91 100644 --- a/dev/reference/merge_dts.html +++ b/dev/reference/merge_dts.html @@ -1,70 +1,15 @@ - - - - - - - -Merge list-columns of spectra, x-axis values, metadata and additional -measured variables into a single long form data.table — merge_dts • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Merge list-columns of spectra, x-axis values, metadata and additional +measured variables into a single long form data.table — merge_dts • simplerspec - - + + - - -
-
- -
- -
+

Merge list-columns of spectra, x-axis values, metadata and additional measured variables into a single long form data.table

- Source: R/plot-spc-extended.R + Source: R/plot-spc-extended.R
@@ -125,72 +57,75 @@ measured variables into a single long form data.table a single long form data.table than can subsequently be used for plotting.

- 
merge_dts(spc_tbl, lcols_spc = c("spc", "spc_pre"), lcol_measure = NULL,
-  spc_id = "unique_id", group_id = "sample_id")
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - -
spc_tbl

Tibble data frame containing spectra, x-axis values, metadata -and eventual measured variables as list-columns.

lcols_spc

Character vector of spectral list-columns to be extracted. -Default is c("spc", "spc_pre") (raw and preprocessed spectra).

lcol_measure

Character vector of length 1 denoting the column name +

+ 
merge_dts(
+  spc_tbl,
+  lcols_spc = c("spc", "spc_pre"),
+  lcol_measure = NULL,
+  spc_id = "unique_id",
+  group_id = "sample_id"
+)
+
+ +
+

Arguments

+
spc_tbl
+

Tibble data frame containing spectra, x-axis values, metadata +and eventual measured variables as list-columns.

+ + +
lcols_spc
+

Character vector of spectral list-columns to be extracted. +Default is c("spc", "spc_pre") (raw and preprocessed spectra).

+ + +
lcol_measure
+

Character vector of length 1 denoting the column name of the measure columns. This argument is optional. Default is NULL, -which does not extract an additional measure column.

spc_id

Character vector of column that contains a unique spectral -identifier for all spectra. Default is "unique_id".

group_id

Character vector of columns that is used assigning spectra +which does not extract an additional measure column.

+ + +
spc_id
+

Character vector of column that contains a unique spectral +identifier for all spectra. Default is "unique_id".

+ + +
group_id
+

Character vector of columns that is used assigning spectra into groups. Default is "sample_id". The group_id can be used for later plotting and thereby visually separating spectral groups into -using different colors or panels.

+using different colors or panels.

-

Value

+
+
+

Value

+ -

A single data.table containing long form aggregated data of +

A single data.table containing long form aggregated data of spectra, x-axis values, metadata and an additionally measured variable.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/merge_dts_l.html b/dev/reference/merge_dts_l.html index 3487203..5a3b773 100644 --- a/dev/reference/merge_dts_l.html +++ b/dev/reference/merge_dts_l.html @@ -1,72 +1,17 @@ - - - - - - - -Wrapper function around <code>merge_dts()</code> for list of tibbles to -aggregate data for plotting. — merge_dts_l • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Wrapper function around merge_dts() for list of tibbles to +aggregate data for plotting. — merge_dts_l • simplerspec - - - - - - - - - - - + + - - -
-
- -
- -
+

Wrapper function around merge_dts() for list of tibbles to aggregate data for plotting.

- Source: R/plot-spc-extended.R + Source: R/plot-spc-extended.R
@@ -126,76 +58,79 @@ aggregate data for plotting.

Instead of a single spectral tibble (data frame) multiple spectral tibbles can be merged into a long-form data.table for plotting spectra and related data. For details, see -merge_dts.

+merge_dts.

- 
merge_dts_l(spc_tbl_l, lcols_spc = c("spc", "spc_pre"), lcol_measure = NULL,
-  spc_id = "unique_id", group_id = "sample_id")
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - -
spc_tbl_l

List of spectral tibbles (data frames).

lcols_spc

Character vector of spectral list-columns to be extracted. -Default is c("spc", "spc_pre") (raw and preprocessed spectra).

lcol_measure

Character vector of length 1 denoting the column name +

+ 
merge_dts_l(
+  spc_tbl_l,
+  lcols_spc = c("spc", "spc_pre"),
+  lcol_measure = NULL,
+  spc_id = "unique_id",
+  group_id = "sample_id"
+)
+
+ +
+

Arguments

+
spc_tbl_l
+

List of spectral tibbles (data frames).

+ + +
lcols_spc
+

Character vector of spectral list-columns to be extracted. +Default is c("spc", "spc_pre") (raw and preprocessed spectra).

+ + +
lcol_measure
+

Character vector of length 1 denoting the column name of the measure columns. This argument is optional. Default is NULL, -which does not extract an additional measure column.

spc_id

Character vector of column that contains a unique spectral -identifier for all spectra. Default is "unique_id".

group_id

Character vector of columns that is used assigning spectra +which does not extract an additional measure column.

+ + +
spc_id
+

Character vector of column that contains a unique spectral +identifier for all spectra. Default is "unique_id".

+ + +
group_id
+

Character vector of columns that is used assigning spectra into groups. Default is "sample_id". The group_id can be used for later plotting and thereby visually separating spectral groups into -using different colors or panels.

+using different colors or panels.

-

Value

+
+
+

Value

+ -

A single data.table containing long form aggregated data of +

A single data.table containing long form aggregated data of spectra, x-axis values, metadata and an additionally measured variable. An additional column called group_id_tbl is appended. It denotes the name of the spectral tibble supplied with the list spc_tbl_l.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/pipe.html b/dev/reference/pipe.html index e992149..1db680a 100644 --- a/dev/reference/pipe.html +++ b/dev/reference/pipe.html @@ -1,67 +1,12 @@ - - - - - - - -Pipe operator — %>% • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Pipe operator — %>% • simplerspec - - + + - - -
-
- -
- -
+

Pipe operator

- Source: R/utils-pipe.R + Source: R/utils-pipe.R
@@ -120,35 +52,32 @@

See magrittr::%>% for details.

- 
lhs %>% rhs
- +
+ 
lhs %>% rhs
+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/plot_pls_vip.html b/dev/reference/plot_pls_vip.html index af79825..28675ef 100644 --- a/dev/reference/plot_pls_vip.html +++ b/dev/reference/plot_pls_vip.html @@ -1,78 +1,23 @@ - - - - - - - -Plot stacked ggplot2 graphs with the Variable Importance for the +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot stacked ggplot2 graphs with the Variable Importance for the Projection (VIP) scores, mean replicate spectra (absorbance) per sample_id, -and the preprocessed spectra. — plot_pls_vip • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + - - - - -
-
- -
- -
+

Plot stacked ggplot2 graphs with the Variable Importance for the Projection (VIP) scores, mean replicate spectra (absorbance) per sample_id, and the preprocessed spectra.

- Source: R/pls-vip.R + Source: R/pls-vip.R
@@ -140,89 +72,84 @@ methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, 78(1--2), 103--112. https://doi.org/10.1016/j.chemolab.2004.12.011

- 
plot_pls_vip(mout, y1 = "spc_mean", y2 = "spc_pre",
-  by = "sample_id",
-  xlab = expression(paste("Wavenumber [", cm^-1, "]")),
-  ylab1 = "Absorbance", ylab2 = "Preprocessed Abs.",
-  alpha = 0.2)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
mout

Model output list that is returned from -simplerspec::fit_pls(). This object contains a nested list with -the caret::train() object (class train), based on which -VIPs at finally selected number of PLS components are computed.

y1

Character vector of list-column name in +

+ 
plot_pls_vip(mout, y1 = "spc_mean", y2 = "spc_pre",
+  by = "sample_id",
+  xlab = expression(paste("Wavenumber [", cm^-1, "]")),
+  ylab1 = "Absorbance", ylab2 = "Preprocessed Abs.",
+  alpha = 0.2)
+
+ +
+

Arguments

+
mout
+

Model output list that is returned from +simplerspec::fit_pls(). This object contains a nested list with +the caret::train() object (class train), based on which +VIPs at finally selected number of PLS components are computed.

+ + +
y1
+

Character vector of list-column name in mout$data$calibration, where spectra for bottom graph are extracted. Default is "spc_mean", which plots the mean calibration spectra after -resampling.

y2

Character string of list-column name in +resampling.

+ + +
y2
+

Character string of list-column name in mout$data$calibration, where spectra for bottom graph are extracted. Default is "spc_pre", which plots the preprocessed calibration -spectra after resampling.

by

Character string that is used to assign spectra to the same group +spectra after resampling.

+ + +
by
+

Character string that is used to assign spectra to the same group and therefore ensures that all spectra are plotted with the same colour. -Default is "sample_id"

xlab

Character string of X axis title for shared x axis of stacked -graphs. Default is expression(paste("Wavenumber [", cm^-1, "]"))

ylab1

Y axis title of bottom spectrum. Default is "Absorbance".

ylab2

Y axis title of bottom spectrum. Default is -"Preprocessed Abs.".

alpha

Double between 0 and 1 that defines transparency of spectra -lines in returned graph (ggplot plot object).

+Default is "sample_id"

+ + +
xlab
+

Character string of X axis title for shared x axis of stacked +graphs. Default is expression(paste("Wavenumber [", cm^-1, "]"))

+ + +
ylab1
+

Y axis title of bottom spectrum. Default is "Absorbance".

+ +
ylab2
+

Y axis title of bottom spectrum. Default is +"Preprocessed Abs.".

+ + +
alpha
+

Double between 0 and 1 that defines transparency of spectra +lines in returned graph (ggplot plot object).

+ +
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/plot_spc.html b/dev/reference/plot_spc.html index 95d7f6c..dd7b1bd 100644 --- a/dev/reference/plot_spc.html +++ b/dev/reference/plot_spc.html @@ -1,67 +1,12 @@ - - - - - - - -Plot tibble spectra — plot_spc • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Plot tibble spectra — plot_spc • simplerspec - - - - + + -
-
- -
- -
+

Plot tibble spectra

- Source: R/plot_spc.R + Source: R/plot_spc.R
@@ -120,129 +52,117 @@

Plot spectra from tibble spectra objects.

- 
plot_spc(spc_tbl, spc_tbl_2 = NULL,
-  x_unit = "wavenumber",
-  y = "spc", by = "unique_id",
-  graph_id_1 = "Set 1", graph_id_2 = "Set 2",
-  graph_id_1_col = "black", graph_id_2_col = "red",
-  xlab = expression(paste("Wavenumber [", cm^-1, "]")),
-  ylab = "Absorbance",
-  slice = TRUE, alpha = 0.2, legend = TRUE)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
spc_tbl

Tibble that contains the first set of spectra to plot as -list-column

spc_tbl_2

Tibble that contains the second set of spectra (optional) -to plot as list-column.

x_unit

Character string describing the x axis unit. Default is +

+ 
plot_spc(spc_tbl, spc_tbl_2 = NULL,
+  x_unit = "wavenumber",
+  y = "spc", by = "unique_id",
+  graph_id_1 = "Set 1", graph_id_2 = "Set 2",
+  graph_id_1_col = "black", graph_id_2_col = "red",
+  xlab = expression(paste("Wavenumber [", cm^-1, "]")),
+  ylab = "Absorbance",
+  alpha = 0.2, legend = TRUE)
+
+ +
+

Arguments

+
spc_tbl
+

Tibble that contains the first set of spectra to plot as +list-column

+ + +
spc_tbl_2
+

Tibble that contains the second set of spectra (optional) +to plot as list-column.

+ + +
x_unit
+

Character string describing the x axis unit. Default is "wavenumber", which will produce a graph with wavenumbers on the x axis with reversed number. If x_unit = "wavelength", the axis will be in regular order (lower wavelengths in nm on the left and higher -on the right side of the axis).

y

Character string of list-column name in tibble where spectra of -desired type are extracted to plot.

by

Character string of column that is used to group the spectra. +on the right side of the axis).

+ + +
y
+

Character string of list-column name in tibble where spectra of +desired type are extracted to plot.

+ + +
by
+

Character string of column that is used to group the spectra. Default is "unique_id". If replica spectra are present in the file and processed spectra resulting after averaging need to be plotted, it is recommend to use "sample_id" as argument to group according the sample_id column in the tibble(s) containing the spectra (spc_tbl -and spc_tbl_2).

graph_id_1

Character string used for grouping the first spectra set +and spc_tbl_2).

+ + +
graph_id_1
+

Character string used for grouping the first spectra set (spc_tbl) and producing -the label text accordingly. Default is "Set 1".

graph_id_2

Character string used for grouping the second spectra set +the label text accordingly. Default is "Set 1".

+ + +
graph_id_2
+

Character string used for grouping the second spectra set (spc_tbl_2) and producing the label text accordingly. Default is -"Set 2"

graph_id_1_col

Character string for the colour of the first spectra -set. Default is "black".

graph_id_2_col

Character string for the colour of the first spectra -set. Default is "red".

xlab

Character string or mathematical expression +"Set 2"

+ + +
graph_id_1_col
+

Character string for the colour of the first spectra +set. Default is "black".

+ + +
graph_id_2_col
+

Character string for the colour of the first spectra +set. Default is "red".

+ + +
xlab
+

Character string or mathematical expression (use expression) for the x axis title. Default is -expression(paste("Wavenumber [", cm^-1, "]")).

ylab

Character string or mathematical expression -(use expression) for the y axis title. Default is "absorbance".

slice

Logical whether to slice the data sets (select rows by position). -Default is TRUE. -This argument will soon be supported to group spectra based id variables present -in spc_tbl such as the `sample_id` and slicing the data set prior to plotting.

alpha

Double in between 0 and 1. Sets the transparency for the plotted -spectra lines.

legend

Logical whether to plot a legend for the spectra describing +expression(paste("Wavenumber [", cm^-1, "]")).

+ + +
ylab
+

Character string or mathematical expression +(use expression) for the y axis title. Default is "absorbance".

+ + +
alpha
+

Double in between 0 and 1. Sets the transparency for the plotted +spectra lines.

+ + +
legend
+

Logical whether to plot a legend for the spectra describing its name selected in arguments graph_id_1 and graph_id_2. -Default is TRUE.

+Default is TRUE.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/plot_spc_ext.html b/dev/reference/plot_spc_ext.html index 1057f25..a79880e 100644 --- a/dev/reference/plot_spc_ext.html +++ b/dev/reference/plot_spc_ext.html @@ -1,72 +1,17 @@ - - - - - - - -ggplot2 wrapper for extended spectra plotting — plot_spc_ext • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -ggplot2 wrapper for extended spectra plotting — plot_spc_ext • simplerspec - - + + - - -
-
- -
- -
+

ggplot2 wrapper for extended spectra plotting

- Source: R/plot-spc-extended.R + Source: R/plot-spc-extended.R
@@ -130,155 +62,167 @@ and groups can be differentiated by different colors or by using panels additional measure variable, e.g. determined by chemical reference analysis.

- 
plot_spc_ext(spc_tbl, spc_tbl_l = NULL, lcols_spc = "spc",
-  lcol_measure = NULL, lcol_measure_col_palette = "Spectral",
-  lcol_measure_col_direction = -1, spc_id = "unique_id",
-  group_id = "sample_id", group_id_order = TRUE, group_color = TRUE,
-  group_color_palette = NULL, group_panel = TRUE, group_legend = FALSE,
-  ncol = NULL, relabel_spc = TRUE, ylab = "Spectrum value", alpha = 0.5,
-  line_width = 0.2, ...)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
spc_tbl

Tibble data frame containing spectra, x-axis values, metadata -and eventual measured variables as list-columns.

spc_tbl_l

List of spectral tibbles (data frames). Default is -NULL (argument is not used).

lcols_spc

Character vector of spectral list-columns to be extracted. -Default is "spc" (raw spectra).

lcol_measure

Character vector of length 1 denoting the column name +

+ 
plot_spc_ext(
+  spc_tbl,
+  spc_tbl_l = NULL,
+  lcols_spc = "spc",
+  lcol_measure = NULL,
+  lcol_measure_col_palette = "Spectral",
+  lcol_measure_col_direction = -1,
+  spc_id = "unique_id",
+  group_id = "sample_id",
+  group_id_order = TRUE,
+  group_color = TRUE,
+  group_color_palette = NULL,
+  group_panel = TRUE,
+  group_legend = FALSE,
+  ncol = NULL,
+  relabel_spc = TRUE,
+  ylab = "Spectrum value",
+  alpha = 0.5,
+  line_width = 0.2,
+  ...
+)
+
+ +
+

Arguments

+
spc_tbl
+

Tibble data frame containing spectra, x-axis values, metadata +and eventual measured variables as list-columns.

+ + +
spc_tbl_l
+

List of spectral tibbles (data frames). Default is +NULL (argument is not used).

+ + +
lcols_spc
+

Character vector of spectral list-columns to be extracted. +Default is "spc" (raw spectra).

+ + +
lcol_measure
+

Character vector of length 1 denoting the column name of the measure columns. This argument is optional. Default is NULL, -which does not extract an additional measure column.

lcol_measure_col_palette

Palette value supplied to -`ggplot::scale_colour_brewer()`. Default is `"Spectral"`, but you can set -it to the default argument `1` (will use -`scale_colour_brewer(..., palette = 1)`).

lcol_measure_col_direction

Sets the the order of colours in the scale +which does not extract an additional measure column.

+ + +
lcol_measure_col_palette
+

Palette value supplied to +ggplot::scale_colour_brewer(). Default is "Spectral", but you can set +it to the default argument 1 (will use +scale_colour_brewer(..., palette = 1)).

+ + +
lcol_measure_col_direction
+

Sets the the order of colours in the scale that is based on a measure column. Default is -1 which reverses the -scale. Argument is passed on to the function `ggplot2::sclae_colour_brewer()` -as argument `direction`.

spc_id

Character vector denoting column name for a unique spectrum ID. -Default is "unique_id".

group_id

Character vector denoting column name for the spectrum group +scale. Argument is passed on to the function ggplot2::sclae_colour_brewer() +as argument direction.

+ + +
spc_id
+

Character vector denoting column name for a unique spectrum ID. +Default is "unique_id".

+ + +
group_id
+

Character vector denoting column name for the spectrum group ID. Default is "sample_id". The group ID is used for -plotting spectra by group (e.g. by using different colors or panels).

group_id_order

Logical that specifies whether the panel names +plotting spectra by group (e.g. by using different colors or panels).

+ + +
group_id_order
+

Logical that specifies whether the panel names derived from a numeric group_id column are reordered using ascending -numbers. Default is TRUE.

group_color

Logical defining whether spectra are colored by the column -specified by group_id.

group_color_palette

Character (1L) defining the diverging colour -scales from colorbrewer.org; see `?scale_colour_brewer` for supported -diverging colur types (`palette` argument).

group_panel

Logical defining whether spectra are arranged into panels -by groups specified in group_id. Default is TRUE.

group_legend

Logical defining whether a legend for the group_id -is plotted. Default is FALSE.

ncol

Integer vector of length 1. Defines number of columns when -plotting panels (facets). Default is NULL (argument not used).

relabel_spc

Logical defining whether panels are relabeled with custom +numbers. Default is TRUE.

+ + +
group_color
+

Logical defining whether spectra are colored by the column +specified by group_id.

+ + +
group_color_palette
+

Character (1L) defining the diverging colour +scales from colorbrewer.org; see ?scale_colour_brewer for supported +diverging colur types (palette argument).

+ + +
group_panel
+

Logical defining whether spectra are arranged into panels +by groups specified in group_id. Default is TRUE.

+ + +
group_legend
+

Logical defining whether a legend for the group_id +is plotted. Default is FALSE.

+ + +
ncol
+

Integer vector of length 1. Defines number of columns when +plotting panels (facets). Default is NULL (argument not used).

+ + +
relabel_spc
+

Logical defining whether panels are relabeled with custom names for spectra types. Default is TRUE. When TRUE, arguments from relabel_spc_types can be passed to plot_spc_ext -(supported via the ... (ellipsis) argument)

ylab

Character vector or vector of type "expression" created by +(supported via the ... (ellipsis) argument)

+ + +
ylab
+

Character vector or vector of type "expression" created by mathematical expression created by expression. Custom annotation for -y-axis of spectra

alpha

Integer of length 1, from 0 to 1. Defines transparency of +y-axis of spectra

+ + +
alpha
+

Integer of length 1, from 0 to 1. Defines transparency of spectral lines. Default is 0.5 (0 is completely transparent and -1 is no transparency).

line_width

Numeric vector of length 1 specifying the width of the -spectral lines. Default is 0.2.

...

Further arguments to be passed to plot_spc_ext. Currently, -arguments of relabel_spc_types are supported.

- -

Value

- -

Object of class "ggplot" (ggplot2 graph).

+1 is no transparency).

+ + +
line_width
+

Numeric vector of length 1 specifying the width of the +spectral lines. Default is 0.2.

+ + +
...
+

Further arguments to be passed to plot_spc_ext. Currently, +arguments of relabel_spc_types are supported.

+ +
+
+

Value

+ + +

Object of class "ggplot" (ggplot2 graph).

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/predict_from_spc.html b/dev/reference/predict_from_spc.html index 552da95..15efd4d 100644 --- a/dev/reference/predict_from_spc.html +++ b/dev/reference/predict_from_spc.html @@ -1,69 +1,14 @@ - - - - - - - -Predict soil properties of new spectra based on a list of calibration models — predict_from_spc • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Predict soil properties of new spectra based on a list of calibration models — predict_from_spc • simplerspec - - - - - - - - - - + + - - - -
-
- -
- -
+

Predict soil properties of new spectra based on a list of calibration models

- Source: R/predict-spc.R + Source: R/predict-spc.R
@@ -124,57 +56,55 @@ of calibration models and a tibble containing preprocessed spectra as list-columns.

- 
predict_from_spc(model_list, spc_tbl, slice = TRUE)
- -

Arguments

- - - - - - - - - - - - - - -
model_list

List of model output generated from calibration step -(pls_ken_stone()

spc_tbl

Tibble of spectra after preprocessing -(preprocess_spc())

slice

Logical expression wheather only one row per sample_id returned.

- -

Value

- -

tibble with new columns model, and predicted values with +

+ 
predict_from_spc(model_list, spc_tbl, slice = TRUE)
+
+ +
+

Arguments

+
model_list
+

List of model output generated from calibration step +(pls_ken_stone()

+ + +
spc_tbl
+

Tibble of spectra after preprocessing +(preprocess_spc())

+ + +
slice
+

Logical expression wheather only one row per sample_id returned.

+ +
+
+

Value

+ + +

tibble with new columns model, and predicted values with column names of model list.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/preprocess_spc.html b/dev/reference/preprocess_spc.html index 88522b6..522962e 100644 --- a/dev/reference/preprocess_spc.html +++ b/dev/reference/preprocess_spc.html @@ -1,68 +1,13 @@ - - - - - - - -Preprocess spectra — preprocess_spc • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Preprocess spectra — preprocess_spc • simplerspec - + + - - - -
-
- -
- -
+

Preprocess spectra

- Source: R/preprocess-spc.R + Source: R/preprocess-spc.R

Preprocesses spectra in tibble column by sample_id after -averaging spectra by simplerspec::average_spc().

+averaging spectra by simplerspec::average_spc().

+
+ +
+ 
preprocess_spc(spc_tbl, select, column_in = "spc_mean", custom_function = NULL)
- 
preprocess_spc(spc_tbl, select, column_in = "spc_mean",
-  custom_function = NULL)
- -

Arguments

- - - - - - - - - - - - - - - - - - -
spc_tbl

Tibble that contains spectra to be preprocessed within -a list-column.

select

Character vector of predefined preprocessing options to be +

+

Arguments

+
spc_tbl
+

Tibble that contains spectra to be preprocessed within +a list-column.

+ + +
select
+

Character vector of predefined preprocessing options to be applied to the spectra list-column specified in column_in. Common prefined values are stated as abbreviated preprocessing methods and options such as "sg_1_w21", where "sg" stands for Savitzky-Golay and 1 for first derivative and "w21" -for a window size of 21 points.

column_in

Character vector of single list-column in spc_tbl that +for a window size of 21 points.

+ + +
column_in
+

Character vector of single list-column in spc_tbl that contain list of spectra (1 row matrix) to be processed by function supplied -in select.

custom_function

A character string of a custom processing function +in select.

+ + +
custom_function
+

A character string of a custom processing function that is later parsed (produces expression in a list) and evaluated within - the function preprocess_spc. +the function preprocess_spc. The character vector argument of custom_function needs to contain "spc_raw", which is the single data table of spectra that results from binding a list of data.tables (spectra to preprocess) from the spectra list-column specified in column_in. An example for a value is "prospectr::savitzkyGolay(X = spc_raw, m = 0, p = 3, w = 9)". -Optional argument. Default is NULL.

+Optional argument. Default is NULL.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/read_asd.html b/dev/reference/read_asd.html index a7abdcf..6650b80 100644 --- a/dev/reference/read_asd.html +++ b/dev/reference/read_asd.html @@ -1,74 +1,19 @@ - - - - - - - -Read ASD fieldspec spectrometer data export into into simplerspec -spectra tibble. — read_asd • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Read ASD fieldspec spectrometer data export into into simplerspec +spectra tibble. — read_asd • simplerspec - - - - - - - - - - - + + - - -
-
- -
- -
+

Read ASD fieldspec spectrometer data export into into simplerspec spectra tibble.

- Source: R/read-asd.R + Source: R/read-asd.R
@@ -133,53 +65,51 @@ the name 'Wavelength' for the first column and the file names for the remaining columns.

- 
read_asd(file)
+
+ 
read_asd(file)
+
-

Arguments

- - - - - - -
file

Tab delmited file from ASD software export where the first +

+

Arguments

+
file
+

Tab delmited file from ASD software export where the first column called Wavelength contais wavelengths in nanometer and the remaining columns are sample spectra referred by an ID name provided in the -first row of these columns.

+first row of these columns.

-

Value

+
+
+

Value

+ -

Spectra data in tibble data frame (class `tbl_df`) that contains +

Spectra data in tibble data frame (class tbl_df) that contains columns sample_id (derived from 2nd and following column names of tab delimited ASD exported text file), spc (list-column of spectral matrices) and wavelengths (list-column containing wavelength vectors).

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/read_asd_bin.html b/dev/reference/read_asd_bin.html index bff6d59..dabb27f 100644 --- a/dev/reference/read_asd_bin.html +++ b/dev/reference/read_asd_bin.html @@ -1,72 +1,17 @@ - - - - - - - -Read ASD binary files and gather spectra and metadata in tibble data -frame. — read_asd_bin • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Read ASD binary files and gather spectra and metadata in tibble data +frame. — read_asd_bin • simplerspec - - - - - - - - - - + + - - - -
-
- -
- -
+

Read ASD binary files and gather spectra and metadata in tibble data frame.

- Source: R/read-asd.R + Source: R/read-asd.R
@@ -129,61 +61,74 @@ tibble is compatible with the simplerspec spectra processing and modeling framework.

- 
read_asd_bin(fnames)
+
+ 
read_asd_bin(fnames)
+
-

Arguments

- - - - - - -
fnames

Character vector containing full paths of ASD binary files -to be read

+
+

Arguments

+
fnames
+

Character vector containing full paths of ASD binary files +to be read

-

Value

+
+
+

Value

+ -

A spectral tibble (data frame) containing the follwing columns:

-
unique_id

Character vector. Unique identifier containing file name +

A spectral tibble (data frame) containing the follwing columns:

+
unique_id
+

Character vector. Unique identifier containing file name pasted with date and time.

-
file_id

Character vector containing file names and exension

-
sample_id

Character vector containing files names without extension

-
metadata

List-column. List of data frames containing spectral + +

file_id
+

Character vector containing file names and exension

+ +
sample_id
+

Character vector containing files names without extension

+ +
metadata
+

List-column. List of data frames containing spectral metadata

-
wavelengths

List-column. List of wavelengths vectors (numeric).

-
spc_radiance

List-column. List of data.tables containing + +

wavelengths
+

List-column. List of wavelengths vectors (numeric).

+ +
spc_radiance
+

List-column. List of data.tables containing radiance sample spectra.

-
spc_reference

List-column. List of data.tables containing + +

spc_reference
+

List-column. List of data.tables containing reference reflectance spectra.

-
spc

List-column. List of data.tables containing final reflectance + +

spc
+

List-column. List of data.tables containing final reflectance spectra.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/read_opus_bin_univ.html b/dev/reference/read_opus_bin_univ.html index 8d5cf2f..cb7e1fd 100644 --- a/dev/reference/read_opus_bin_univ.html +++ b/dev/reference/read_opus_bin_univ.html @@ -1,68 +1,13 @@ - - - - - - - -Read a Bruker OPUS spectrum binary file — read_opus_bin_univ • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Read a Bruker OPUS spectrum binary file — read_opus_bin_univ • simplerspec - + + - - - -
-
- -
- -
+

Read a Bruker OPUS spectrum binary file

- Source: R/read-opus-universal.R + Source: R/read-opus-universal.R
@@ -122,66 +54,61 @@ Bruker Vertex FTIR Instrument" /> Bruker Vertex FTIR Instrument

- 
read_opus_bin_univ(file_path, extract = c("spc"),
-print_progress = TRUE, atm_comp_minus4offset = FALSE)
- -

Arguments

- - - - - - - - - - - - - - - - - - -
file_path

Character vector with path to file

extract

Character vector of spectra types to extract from OPUS binary -file. Default is c("spc"), which will extract the final spectra, e.g. +

+ 
read_opus_bin_univ(file_path, extract = c("spc"),
+print_progress = TRUE, atm_comp_minus4offset = FALSE)
+
+ +
+

Arguments

+
file_path
+

Character vector with path to file

+ + +
extract
+

Character vector of spectra types to extract from OPUS binary +file. Default is c("spc"), which will extract the final spectra, e.g. expressed in absorbance (named AB in Bruker OPUS programs). Possible additional values for the character vector supplied to extract are "ScSm" (single channel spectrum of the sample measurement), \ code"ScRf" (single channel spectrum of the reference measurment), "IgSm" (interferogram of the sample measurment) and "IgRf" -(interferogram of the reference measurement).

print_progress

Logical (default TRUE) whether a message is -printed when an OPUS binary file is parsed into an R list entry.

atm_comp_minus4offset

Logical whether spectra after atmospheric +(interferogram of the reference measurement).

+ + +
print_progress
+

Logical (default TRUE) whether a message is +printed when an OPUS binary file is parsed into an R list entry.

+ + +
atm_comp_minus4offset
+

Logical whether spectra after atmospheric compensation are read with an offset of -4 bites from Bruker OPUS -files. Default is FALSE.

+files. Default is FALSE.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/read_opus_univ.html b/dev/reference/read_opus_univ.html index 78c883f..e72b910 100644 --- a/dev/reference/read_opus_univ.html +++ b/dev/reference/read_opus_univ.html @@ -1,69 +1,14 @@ - - - - - - - -Read a list of Bruker OPUS spectrum binary files. — read_opus_univ • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Read a list of Bruker OPUS spectrum binary files. — read_opus_univ • simplerspec - - - - - - - - - - + + - - - -
-
- -
- -
+

Read a list of Bruker OPUS spectrum binary files.

- Source: R/read-opus-universal.R + Source: R/read-opus-universal.R

Read multiple spectral files measured with a Bruker FTIR Instrument. Files containing spectra are in OPUS binary format. -read_opus_univ is a wrapper for read_opus_bin_univ())

+read_opus_univ is a wrapper for read_opus_bin_univ())

+
+ +
+ 
read_opus_univ(fnames, extract = c("spc"), parallel = FALSE,
+atm_comp_minus4offset = FALSE)
- 
read_opus_univ(fnames, extract = c("spc"), parallel = FALSE,
-atm_comp_minus4offset = FALSE)
- -

Arguments

- - - - - - - - - - - - - - - - - - -
fnames

List of character vectors containing full path names of spectra

extract

Character vector of spectra types to extract from file. +

+

Arguments

+
fnames
+

List of character vectors containing full path names of spectra

+ + +
extract
+

Character vector of spectra types to extract from file. Possible values are: "spc" (AB block in Bruker Opus software), "spc_nocomp" (Spectra before final atmospheric compensation; only present if background correction has been set in Opus), "ScSm" (Single channel spectrum of the sample), "ScRf" (Single channel spectrum of the sample), "IgSm" (Interferogram of the sample), "IgRf" (Interferogram of the reference). Default is - extract = c("spc").

parallel

Logical (TRUE or FALSE indicating whether +extract = c("spc").

+ + +
parallel
+

Logical (TRUE or FALSE indicating whether files are read in parallel (multiple processors or multiple cores)). Default is parallel = FALSE. If TRUE a parallel backend needs -to be registered, e.g. by using the doParallel package.

atm_comp_minus4offset

Logical whether spectra after atmospheric +to be registered, e.g. by using the doParallel package.

+ + +
atm_comp_minus4offset
+

Logical whether spectra after atmospheric compensation are read with an offset of -4 bites from Bruker OPUS -files. Default is FALSE.

+files. Default is FALSE.

-

Value

+
+
+

Value

+ -

out List spectra and metadata (parameters) extracted from +

out List spectra and metadata (parameters) extracted from Bruker OPUS spectrometer files. List names are the names of the OPUS files whose spectral data were extracted.

+
+
-
-
-

Developed by Philipp Baumann.

+
+

Developed by Philipp Baumann.

-

Site built with pkgdown 1.5.1.

+

Site built with pkgdown 2.0.7.

-
-
+
- - + + diff --git a/dev/reference/remove_outliers.html b/dev/reference/remove_outliers.html index 56d0c92..8544273 100644 --- a/dev/reference/remove_outliers.html +++ b/dev/reference/remove_outliers.html @@ -1,68 +1,13 @@ - - - - - - - -Remove outlier spectra — remove_outliers • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Remove outlier spectra — remove_outliers • simplerspec + + - - - - -
-
- -
- -
+

Remove outlier spectra

- Source: R/remove-outl-spectra.R + Source: R/remove-outl-spectra.R
@@ -122,75 +54,71 @@ pcout() function of the mvoutlier package." /> pcout() function of the mvoutlier package.

- 
remove_outliers(list_spectra, remove = TRUE)
+
+ 
remove_outliers(list_spectra, remove = TRUE)
+
-

Arguments

- - - - - - - - - - -
list_spectra

List that contains averaged +

+

Arguments

+
list_spectra
+

List that contains averaged spectral information in list element MIR_mean (data.frame) and metadata in -data_meta (data.frame).

remove

logical expression (TRUE or FALSE) +data_meta (data.frame).

+ + +
remove
+

logical expression (TRUE or FALSE) that specifies weather spectra shall be removed. -If rm = FALSE, there will be no outlier removal

- -

Value

- -

Returns list spectra_out that contains:

    -

  • MIR_mean: Outlier removed MIR spectra as - data.frame object. If remove = FALSE, - the function will - return almost identical list identical to list_spectra, - except that the first indices column of the spectral - data frame MIR_mean is removed - (This is done for both options - remove = TRUE and remove = FALSE).

    • +If rm = FALSE, there will be no outlier removal

    + +
+
+

Value

+ + +

Returns list spectra_out that contains:

  • MIR_mean: Outlier removed MIR spectra as +data.frame object. If remove = FALSE, +the function will +return almost identical list identical to list_spectra, +except that the first indices column of the spectral +data frame MIR_mean is removed +(This is done for both options +remove = TRUE and remove = FALSE).

  • data_meta: metadata data.frame, identical - as in the list_spectra input list.

    • +as in the list_spectra input list.

  • plot_out: (optional) ggplot2 graph - that shows all spectra (absorbance on x-axis and wavenumber - on y-axis) with outlier marked, if - remove = TRUE.

    • -
- -

Details

- +that shows all spectra (absorbance on x-axis and wavenumber +on y-axis) with outlier marked, if +remove = TRUE.

+
+
+

Details

This is an optional function if one wants to remove outliers.

+
+
- - - + + diff --git a/dev/reference/resample_spc.html b/dev/reference/resample_spc.html index 2cf5321..0f67ba7 100644 --- a/dev/reference/resample_spc.html +++ b/dev/reference/resample_spc.html @@ -1,74 +1,19 @@ - - - - - - - -Resample spectra in list-column to new x-axis interval — resample_spc • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Resample spectra in list-column to new x-axis interval — resample_spc • simplerspec - - - - - - - - - - - - - + + -
-
- -
- -
+

Resample spectra in list-column to new x-axis interval

- Source: R/resample-spc.R + Source: R/resample-spc.R
@@ -128,119 +60,117 @@ spectrometer devices and/or measurement settings)." /> corresponding x-axis values that are both stored in list-columns of a spectra tibble. A spectra tibble hosts spectra, x-axis vectors, metadata, and further linked data with standardized naming conventions. Data input for -resampling can for example be generated with simplerspec::gather_spc(). +resampling can for example be generated with simplerspec::gather_spc(). Resampling is a key harmonizing step to process and later model spectra measured at different resolutions and spectral ranges (i.e., different spectrometer devices and/or measurement settings).

- 
resample_spc(
-  spc_tbl,
-  column_in = "spc",
-  x_unit = c("wavenumber", "wavelength"),
-  wn_lower = 500,
-  wn_upper = 4000,
-  wn_interval = 2,
-  wl_lower = 350,
-  wl_upper = 2500,
-  wl_interval = 1,
-  interpol_method = c("linear", "spline")
-)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
spc_tbl

Spectra data embedded in a tibble object (classes +

+ 
resample_spc(
+  spc_tbl,
+  column_in = "spc",
+  x_unit = c("wavenumber", "wavelength"),
+  wn_lower = 500,
+  wn_upper = 4000,
+  wn_interval = 2,
+  wl_lower = 350,
+  wl_upper = 2500,
+  wl_interval = 1,
+  interpol_method = c("linear", "spline")
+)
+
+ +
+

Arguments

+
spc_tbl
+

Spectra data embedded in a tibble object (classes "tbl_df", "tbl", "data.frame"). The spectra tibble needs to contain at least of one of the the spectra columns spc, spc_rs, spc_mean, spc_nocomp, sc_sm, sc_rf, or spc_pre (list-columns with spectral data.tables), and wavenumbers or wavelengths (list-column with vectors of x-axis values corresponding to each spectrum). The help section "Matching spectrum type and corresponding x-axis type" describes the spectra types -and corresponding x-axis types.

column_in

Character vector of length 1L or symbol/name -specifying the name of list-column that contains the spectra to be resampled.

x_unit

Character vector of length 1L specifying the measurement unit +and corresponding x-axis types.

+ + +
column_in
+

Character vector of length 1L or symbol/name +specifying the name of list-column that contains the spectra to be resampled.

+ + +
x_unit
+

Character vector of length 1L specifying the measurement unit of the x-axis values (list-column) of the input spectra in spc_tbl. Possible values are "wavenumber" (default) or "wavelength". Wavenumber is a convenient unit of frequency in the mid-infrared spectral range, where wavelength is often used as spatial period for the visible and -near-infrared range.

wn_lower

Numeric value of lowest wavenumber. This argument will only +near-infrared range.

+ + +
wn_lower
+

Numeric value of lowest wavenumber. This argument will only be used if x_unit = "wavenumber". The value serves as starting value for the new wavenumber sequence that the spectra will be resampled upon. Default -value is 500 (i.e., in reciprocal centimeters).

wn_upper

Numeric value of highest wavenumber. This argument will only +value is 500 (i.e., in reciprocal centimeters).

+ + +
wn_upper
+

Numeric value of highest wavenumber. This argument will only be used if x_unit = "wavenumber. The value will be used as last value of the new wavenumber sequence that the spectra will be resampled upon. Default -value is 4000 (i.e., in reciprocal centimeters).

wn_interval

Numeric value of the wavenumber increment for the new +value is 4000 (i.e., in reciprocal centimeters).

+ + +
wn_interval
+

Numeric value of the wavenumber increment for the new wavenumber sequence that the spectra will be resampled upon. Default value -is 2 (i.e., in reciprocal centimeters).

wl_lower

Numeric value of lowest wavelength. This argument will only +is 2 (i.e., in reciprocal centimeters).

+ + +
wl_lower
+

Numeric value of lowest wavelength. This argument will only be used if x_unit = "wavelength". The value serves as starting value of the new wavenumber sequence that the spectra will be resampled upon. -Default value is 350 (i.e. in nanometers).

wl_upper

Numeric value of highest wavelength. This argument will only +Default value is 350 (i.e. in nanometers).

+ + +
wl_upper
+

Numeric value of highest wavelength. This argument will only be used if x_unit = "wavelength". The value will be used as last value of the new wavenumber sequence that the spectra will be resampled upon. Default -value is 2500 (i.e., in nanometers).

wl_interval

Numeric value of the wavelength increment for the new +value is 2500 (i.e., in nanometers).

+ + +
wl_interval
+

Numeric value of the wavelength increment for the new wavenumber sequence that the spectra will be resampled upon. This argument will only be used if x_unit = "wavelength". Default value is 1 (i.e., in -nanometers).

interpol_method

Character of "linear" (default) or "spline" with +nanometers).

+ + +
interpol_method
+

Character of "linear" (default) or "spline" with the interpolation method. "spline" uses a cubic spline to interpolate the -input spectra at given x-axis values to new equispaced x-axis intervals.

+input spectra at given x-axis values to new equispaced x-axis intervals.

-

Value

+
+
+

Value

+ -

A spectra tibble (spc_tbl) containing two added list-columns:

    -

  • spc_rs: Resampled spectra as list of data.tables

    • +

    A spectra tibble (spc_tbl) containing two added list-columns:

    • spc_rs: Resampled spectra as list of data.tables

    • wavenumbers_rs or wavelengths_rs: Resampled x-axis values as list of numeric vectors

      • -
    - -

    Matching spectrum type and corresponding x-axis type

    - +
+
+

Matching spectrum type and corresponding x-axis type

The combinations of input spectrum types (column_in) and corresponding x-axis types are generated from a simple lookup list. The following key-value(s) pairs can be matched at given key, which is the column -name from column_in containing the spectra.

    -

  • "spc" : "wavenumbers" or "wavelengths" (raw spectra)

    • +name from column_in containing the spectra.

    • "spc" : "wavenumbers" or "wavelengths" (raw spectra)

    • "spc_rs" : "wavenumbers_rs" or "wavelengths_rs") (resampled spectra)

    • "spc_mean" : "wavenumbers_rs" or "wavelengths_rs" (mean spectra)

    • "spc_nocomp" "wavenumbers" or "wavelengths" (spectra prior @@ -250,34 +180,29 @@ sample spectra)

    • "sc_rf" : c("wavenumbers_sc_rf", "wavelengths_sc_rf") (single channel reference spectra)

    • "spc_pre" : "xvalues_pre" (preprocessed spectra)

      • -
    - +
+
- - - + + diff --git a/dev/reference/select_ref_spc.html b/dev/reference/select_ref_spc.html index eaec137..173b574 100644 --- a/dev/reference/select_ref_spc.html +++ b/dev/reference/select_ref_spc.html @@ -1,70 +1,15 @@ - - - - - - - -Select a set of reference spectra to be measured by reference analysis -methods — select_ref_spc • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Select a set of reference spectra to be measured by reference analysis +methods — select_ref_spc • simplerspec - + + - - - -
-
- -
- -
+

Select a set of reference spectra to be measured by reference analysis methods

- Source: R/select-ref-spectra.R + Source: R/select-ref-spectra.R
@@ -125,59 +57,54 @@ methods Samples in this list will be analyzed using laboratory reference methods.

- 
select_ref_spc(spc_tbl, ratio_ref, pc, print = TRUE)
- -

Arguments

- - - - - - - - - - - - - - - - - - -
spc_tbl

Spectra as tibble objects that contain preprocessed spectra

ratio_ref

Ratio of desired reference samples to total sample number

pc

Number of principal components (numeric). If pc < 1, the number +

+ 
select_ref_spc(spc_tbl, ratio_ref, pc, print = TRUE)
+
+ +
+

Arguments

+
spc_tbl
+

Spectra as tibble objects that contain preprocessed spectra

+ + +
ratio_ref
+

Ratio of desired reference samples to total sample number

+ + +
pc
+

Number of principal components (numeric). If pc < 1, the number of principal components kept corresponds to the number of components -explaining at least (pc * 100) percent of the total variance.

print

logical expression whether a plot (ggplot2) of sample selection +explaining at least (pc * 100) percent of the total variance.

+ + +
print
+

logical expression whether a plot (ggplot2) of sample selection for reference analysis is shown in PCA space -(TRUE or FALSE).

+(TRUE or FALSE).

+
+
- - - + + diff --git a/dev/reference/select_spc_vars.html b/dev/reference/select_spc_vars.html index b10c054..cffc51e 100644 --- a/dev/reference/select_spc_vars.html +++ b/dev/reference/select_spc_vars.html @@ -1,70 +1,15 @@ - - - - - - - -Select every n-th spectral variable for all spectra and x-values in spectral -tibble (<code>spc_tbl</code>) — select_spc_vars • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Select every n-th spectral variable for all spectra and x-values in spectral +tibble (spc_tbl) — select_spc_vars • simplerspec - - - - + + -
-
- -
- -
+

Select every n-th spectral variable for all spectra and x-values in spectral tibble (spc_tbl)

- Source: R/select-spc.R + Source: R/select-spc.R
@@ -125,67 +57,63 @@ tibble (spc_tbl) tibble (spc_tbl)

- 
select_spc_vars(
-  spc_tbl,
-  lcol_spc = "spc_pre",
-  lcol_xvalues = "xvalues_pre",
-  every = NULL
-)
- -

Arguments

- - - - - - - - - - - - - - - - - - -
spc_tbl

Tibble data.frame containing spectra in list-column

lcol_spc

List-column containing spectra, specified with column -name as symbols or 1L character vector.

lcol_xvalues

List-column containing x-values, specified with -column name as symbols or 1L character vector.

every

Every n-th spectral positions to keep as 1L integer vector.

- -

Value

+
+ 
select_spc_vars(
+  spc_tbl,
+  lcol_spc = "spc_pre",
+  lcol_xvalues = "xvalues_pre",
+  every = NULL
+)
+
- +
+

Arguments

+
spc_tbl
+

Tibble data.frame containing spectra in list-column

+
lcol_spc
+

List-column containing spectra, specified with column +name as symbols or 1L character vector.

+ + +
lcol_xvalues
+

List-column containing x-values, specified with +column name as symbols or 1L character vector.

+ + +
every
+

Every n-th spectral positions to keep as 1L integer vector.

+ +
+
+

Value

+ + +

a spectral tibble

+
+
- - - + + diff --git a/dev/reference/slice_xvalues.html b/dev/reference/slice_xvalues.html index a87a6ab..f833213 100644 --- a/dev/reference/slice_xvalues.html +++ b/dev/reference/slice_xvalues.html @@ -1,69 +1,14 @@ - - - - - - - -Slice spectra into defined x-axis ranges — slice_xvalues • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Slice spectra into defined x-axis ranges — slice_xvalues • simplerspec - - - - - - - - - - - - - + + -
-
- -
- -
+

Slice spectra into defined x-axis ranges

- Source: R/slice-spc.R + Source: R/slice-spc.R
@@ -124,69 +56,73 @@ cut based on these ranges." /> cut based on these ranges.

- 
slice_xvalues(spc_tbl, xunit_lcol = "wavenumbers", spc_lcol = "spc",
-  xvalues_cut = NULL)
+
+ 
slice_xvalues(
+  spc_tbl,
+  xunit_lcol = "wavenumbers",
+  spc_lcol = "spc",
+  xvalues_cut = NULL
+)
+
-

Arguments

- - - - - - - - - - - - - - - - - - -
spc_tbl

Spectral data in a tibble object (classes "tibble_df", "tbl" +

+

Arguments

+
spc_tbl
+

Spectral data in a tibble object (classes "tibble_df", "tbl" and "data.frame"). The spectra tibble is expected to contain at least the column spc (list-column with spectral matrices stored in a list) and wavenumbers or wavelengths (list-column that contains list -of x-axis values).

xunit_lcol

Character vector that specifies column name where x-axis -axis units are stored within spc_tbl. Default is "wavenumber".

spc_lcol

Character vector that specifies which column (list-column) -contains spectra to be sliced. Default is "spc".

xvalues_cut

List of numeric vectors that contains upper and lower bounds of respective regions to keep in spectra. The spectral regions outside -the xvalues_cut intervals will be cut out in the output spectra.

- -

Value

- -

Spectral tibble (data frame with list-columns) with sliced x-axis +of x-axis values).

+ + +
xunit_lcol
+

Character vector that specifies column name where x-axis +axis units are stored within spc_tbl. Default is "wavenumber".

+ + +
spc_lcol
+

Character vector that specifies which column (list-column) +contains spectra to be sliced. Default is "spc".

+ + +
xvalues_cut
+

List of numeric vectors that contains upper and lower bounds of respective regions to keep in spectra. The spectral regions outside +the xvalues_cut intervals will be cut out in the output spectra.

+ +
+
+

Value

+ + +

Spectral tibble (data frame with list-columns) with sliced x-axis column and spectral column. Both the x-axis list-column and the spectral -tibble list-column only contain data specified within the xvalues_cut -argument (list of numeric vectors).

+tibble list-column only contain data specified within the xvalues_cut

+ + +

argument (list of numeric vectors).

+
+
- - - + + diff --git a/dev/reference/split_df2l.html b/dev/reference/split_df2l.html index c5c8485..62e1c09 100644 --- a/dev/reference/split_df2l.html +++ b/dev/reference/split_df2l.html @@ -1,68 +1,13 @@ - - - - - - - -Split a tibble data frame into a list of tibbles by a group column — split_df2l • simplerspec - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Split a tibble data frame into a list of tibbles by a group column — split_df2l • simplerspec + + - - - - -
-
- -
- -
+

Split a tibble data frame into a list of tibbles by a group column

- Source: R/tbl-lcol-map-funprog.R + Source: R/tbl-lcol-map-funprog.R
@@ -122,52 +54,50 @@ grouping column within tibble." /> grouping column within tibble.

- 
split_df2l(tbl_df, group)
- -

Arguments

- - - - - - - - - - -
tbl_df

Tibble data frame

group

Character vector with name of column based on which tibble -is split into a list of tibbles

- -

Value

- -

List of tibbles. Each tibble contains data split by +

+ 
split_df2l(tbl_df, group)
+
+ +
+

Arguments

+
tbl_df
+

Tibble data frame

+ + +
group
+

Character vector with name of column based on which tibble +is split into a list of tibbles

+ +
+
+

Value

+ + +

List of tibbles. Each tibble contains data split by a group column within tbl_df.

+
+
- - - + + diff --git a/dev/sitemap.xml b/dev/sitemap.xml index 8e9c782..6024f58 100644 --- a/dev/sitemap.xml +++ b/dev/sitemap.xml @@ -1,9 +1,18 @@ + https://philipp-baumann.github.io/simplerspec/LICENSE.html + + + https://philipp-baumann.github.io/simplerspec/authors.html + + https://philipp-baumann.github.io/simplerspec/index.html + https://philipp-baumann.github.io/simplerspec/news/index.html + + https://philipp-baumann.github.io/simplerspec/reference/assess_multimodels.html @@ -34,6 +43,9 @@ https://philipp-baumann.github.io/simplerspec/reference/gather_spc.html + https://philipp-baumann.github.io/simplerspec/reference/index.html + + https://philipp-baumann.github.io/simplerspec/reference/join_chem_spec.html