philipp-baumann
/
simplerspec
mirror of https://github.com/philipp-baumann/simplerspec
1
0
Fork 0
Code Issues Releases Wiki Activity
Browse Source

Do not export a list of package-internal functions for model training and evaluation

pull/3/head
Philipp Baumann 7 years ago
parent
7b20bce7d7
commit
0bc82e52bc
11 changed files with 0 additions and 277 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 16
      NAMESPACE
  2. 23
      man/control_train.Rd
  3. 23
      man/control_train_loocv_q.Rd
  4. 23
      man/control_train_none_q.Rd
  5. 23
      man/control_train_q.Rd
  6. 23
      man/control_train_rcv_q.Rd
  7. 37
      man/evaluate_model_q.Rd
  8. 27
      man/split_data_q.Rd
  9. 24
      man/train_pls.Rd
  10. 29
      man/train_pls_q.Rd
  11. 29
      man/train_rf_q.Rd

16
NAMESPACE
View File

@ -1,14 +1,6 @@
# Generated by roxygen2: do not edit by hand
export(VIP)
export(average_spc)
export(control_train)
export(control_train_loocv_q)
export(control_train_none_q)
export(control_train_q)
export(control_train_rcv_q)
export(create_vip_rects)
export(evaluate_model_q)
export(extract_pls_vip)
export(fit_pls)
export(fit_rf)
@ -29,15 +21,8 @@ export(read_opus_univ)
export(read_spectra)
export(remove_outliers)
export(resample_spc)
export(se)
export(select_ref_spc)
export(sem_ci)
export(split_data_q)
export(summary_df)
export(train_pls)
export(train_pls_q)
export(train_rf_q)
export(transform_cvpredictions)
import(stats)
importFrom(data.table,":=")
importFrom(data.table,.SD)
@ -45,4 +30,3 @@ importFrom(data.table,data.table)
importFrom(data.table,rbindlist)
importFrom(data.table,setDT)
importFrom(data.table,setkey)
importFrom(magrittr,"%>%")

23
man/control_train.Rd
View File

@ -1,23 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{control_train}
\alias{control_train}
\title{Perform model tuning}
\usage{
control_train(x, response, env = parent.frame())
}
\arguments{
\item{x}{list from calibration sampling}
\item{response}{response variable for PLS regression, supplied
as character expression}
\item{env}{Environment where function is evaluated}
\item{validation}{Logical expression weather an independent
validation is performed.}
}
\description{
Uses function from caret to to model tuning
for PLS regression.
}

23
man/control_train_loocv_q.Rd
View File

@ -1,23 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{control_train_loocv_q}
\alias{control_train_loocv_q}
\title{Perform model tuning}
\usage{
control_train_loocv_q(x, response, env = parent.frame())
}
\arguments{
\item{x}{list from calibration sampling}
\item{response}{response variable for PLS regression, supplied
as character expression}
\item{env}{Environment where function is evaluated}
\item{validation}{Logical expression weather an independent
validation is performed.}
}
\description{
Uses function from caret to to model tuning
for PLS regression.
}

23
man/control_train_none_q.Rd
View File

@ -1,23 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{control_train_none_q}
\alias{control_train_none_q}
\title{Perform model fitting without parameter tuning}
\usage{
control_train_none_q(x, response, env = parent.frame())
}
\arguments{
\item{x}{list from calibration sampling}
\item{response}{response variable for PLS regression, supplied
as character expression}
\item{env}{Environment where function is evaluated}
\item{validation}{Logical expression weather an independent
validation is performed.}
}
\description{
Uses function from caret to set model tuning to none
for PLS regression.
}

23
man/control_train_q.Rd
View File

@ -1,23 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{control_train_q}
\alias{control_train_q}
\title{Perform model tuning}
\usage{
control_train_q(x, response, env = parent.frame())
}
\arguments{
\item{x}{list from calibration sampling}
\item{response}{response variable for PLS regression, supplied
as character expression}
\item{env}{Environment where function is evaluated}
\item{validation}{Logical expression weather an independent
validation is performed.}
}
\description{
Uses function from caret to to model tuning
for PLS regression.
}

23
man/control_train_rcv_q.Rd
View File

@ -1,23 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{control_train_rcv_q}
\alias{control_train_rcv_q}
\title{Perform model tuning}
\usage{
control_train_rcv_q(x, response, env = parent.frame())
}
\arguments{
\item{x}{list from calibration sampling}
\item{response}{response variable for PLS regression, supplied
as character expression}
\item{env}{Environment where function is evaluated}
\item{validation}{Logical expression weather an independent
validation is performed.}
}
\description{
Uses function from caret to to model tuning
for PLS regression.
}

37
man/evaluate_model_q.Rd
View File

@ -1,37 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{evaluate_model_q}
\alias{evaluate_model_q}
\title{Evaluate PLS performance}
\usage{
evaluate_model_q(x, model, response, evaluation_method, tuning_method,
print = TRUE, env = parent.frame())
}
\arguments{
\item{x}{List that contains calibration and validation data
frame with combined spectral and chemical data}
\item{model}{List with PLS regression model output from
the caret package}
\item{response}{Response variable (e.g. chemical property) to be
modelled (needs to be non-quoted expression). \code{response}
needs to be a column name in the \code{validation} data.frame
(element of \code{x})}
\item{print}{Print observed vs. predicted for calibration
and validation. Default is \code{TRUE}.}
\item{env}{Specifiy the environment in which the function is
called. Default argument of \code{env} is
\code{parent.frame()}}
\item{validation}{Logical expression if independent validation
is performed (split data set into calibration set and
validation set)}
}
\description{
Calculate model performance indices based
on observed and predicted values of validation and calibration
set, and internal cross-validation
}

27
man/split_data_q.Rd
View File

@ -1,27 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{split_data_q}
\alias{split_data_q}
\title{Split spectra into calibration and validation sets}
\usage{
split_data_q(spec_chem, evaluation_method, ratio_val, ken_sto_pc,
print = TRUE)
}
\arguments{
\item{spec_chem}{data.frame that contains chemical
and IR spectroscopy data}
\item{ratio_val}{Ratio of number of validation and all samples.}
\item{ken_sto_pc}{Number of principal components (numeric)}
\item{print}{logical expression weather calibration}
\item{validation}{Logical expression weather
calibration sampling is performed
(\code{TRUE} or \code{FALSE}).}
}
\description{
Perform calibration sampling based on
the Kennard-Stones algorithm.
}

24
man/train_pls.Rd
View File

@ -1,24 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{train_pls}
\alias{train_pls}
\title{Fit a PLS regression model}
\usage{
train_pls(x, response, evaluation_method = "resampling",
env = parent.frame())
}
\arguments{
\item{x}{List that contains calibration
set, validation set, and model tuning options}
\item{response}{Response variable to be modeled}
\item{env}{Environment where function is evaluated}
\item{validation}{Logical expression weather independent
validation is performed}
}
\description{
Uses the caret package to perform PLS modeling.
Spectra are centered and scaled prior to modeling.
}

29
man/train_pls_q.Rd
View File

@ -1,29 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{train_pls_q}
\alias{train_pls_q}
\title{Train a PLS regression model
(quoted version of the function)}
\usage{
train_pls_q(x, evaluation_method = "test_resampling", response, tr_control,
env = parent.frame(), pls_ncomp_max = 20, ncomp_fixed = 5, center,
scale, tuning_method = "resampling")
}
\arguments{
\item{x}{List that contains calibration
set, validation set, and model tuning options}
\item{response}{Response variable to be modeled}
\item{tr_control}{Object that defines controlling parameters
of the desired internal validation framework}
\item{env}{Environment where function is evaluated}
\item{validation}{Logical expression weather independent
validation is performed}
}
\description{
Uses the caret package to perform PLS modeling.
Spectra are centered and scaled prior to modeling.
}

29
man/train_rf_q.Rd
View File

@ -1,29 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pls-modeling.R
\name{train_rf_q}
\alias{train_rf_q}
\title{Fit a random forest model
(quoted version of the function)}
\usage{
train_rf_q(x, validation = TRUE, evaluation_method = "resampling", response,
tr_control, ntree_max = 500, env = parent.frame())
}
\arguments{
\item{x}{List that contains calibration
set, validation set, and model tuning options}
\item{validation}{Logical expression weather independent
validation is performed}
\item{response}{Response variable to be modeled}
\item{tr_control}{Object that defines controlling parameters
of the desired internal validation framework}
\item{env}{Environment where function is evaluated}
}
\description{
Uses the caret package to perform random forest
modeling.
Spectra are centered and scaled prior to modeling.
}
Write Preview
Loading…
Cancel
Save