31 changed files with 1088 additions and 12 deletions
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^.*\.Rproj$ |
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^\.Rproj\.user$ |
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^README\.Rmd$ |
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Package: scimple |
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Title: scimple TITLE |
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Title: Tidy Simultaneous Confidence Intervals for Multinomial Proportions |
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Version: 0.1.0 |
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Encoding: UTF-8 |
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Authors@R: c(person("Bob", "Rudis", role = c("aut", "cre"), email = "bob@rud.is")) |
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Authors@R: c( |
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person("Bob", "Rudis", role = c("aut", "cre"), email = "bob@rud.is"), |
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person("M", "Subbiah", role = c("aut"), comment = c("Original package author")) |
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) |
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Maintainer: Bob Rudis <bob@rud.is> |
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Description: scimple DESCRIPTION |
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Depends: R (>= 3.2.0) |
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License: AGPL |
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Description: Methods for obtaining simultaneous confidence intervals for multinomial |
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proportions have been proposed by many authors and the present study include a variety |
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of widely applicable procedures. Seven classical methods (Wilson, Quesenberry and |
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Hurst, Goodman, Wald with and without continuity correction, Fitzpatrick and Scott, |
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Sison and Glaz) and Bayesian Dirichlet models are included in the package. The |
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advantage of MCMC pack has been exploited to derive the Dirichlet posterior directly |
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and this also helps in handling the Dirichlet prior parameters. This package is |
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prepared to have equal and unequal values for the Dirichlet prior distribution that |
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will provide better scope for data analysis and associated sensitivity analysis. |
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Depends: R (>= 3.2.0), MCMCpack |
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License: GPL-2 |
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URL: https://github.com/hrbrmstr/scimple |
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BugReports: https://github.com/hrbrmstr/scimple/issues |
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LazyData: true |
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Suggests: testthat |
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Imports: purrr |
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Imports: dplyr, tibble, stats, purrr |
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RoxygenNote: 6.0.1 |
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|
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# Generated by roxygen2: do not edit by hand |
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|
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export(scimp_bmde) |
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export(scimp_bmdu) |
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export(scimp_fs) |
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export(scimp_goodman) |
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export(scimp_qh) |
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export(scimp_sg) |
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export(scimp_wald) |
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export(scimp_waldcc) |
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export(scimp_wilson) |
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export(scimple_ci) |
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import(MCMCpack) |
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import(stats) |
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import(tibble) |
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importFrom(dplyr,mutate) |
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importFrom(dplyr,select) |
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importFrom(purrr,map) |
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importFrom(purrr,map_df) |
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SG <- function(x,alpha) { |
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t1=proc.time() |
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|
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sgp=function(c) { |
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s=sum(x) ##SUM(Cell_Counts) |
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k=length(x) |
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b= x-c |
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a= x+c |
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###FINDING FACTORIAL MOMENTS-TRUNCATED POISSON |
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fm1=0 |
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fm2=0 |
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fm3=0 |
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fm4=0 |
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for (i in 1:k) |
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{ |
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fm1[i]=x[i]*(ppois(a[i]-1,x[i])-ppois(b[i]-2,x[i]))/(ppois(a[i],x[i])-ppois(b[i]-1,x[i])) |
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fm2[i]=x[i]^2*(ppois(a[i]-2,x[i])-ppois(b[i]-3,x[i]))/(ppois(a[i],x[i])-ppois(b[i]-1,x[i])) |
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fm3[i]=x[i]^3*(ppois(a[i]-3,x[i])-ppois(b[i]-4,x[i]))/(ppois(a[i],x[i])-ppois(b[i]-1,x[i])) |
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fm4[i]=x[i]^4*(ppois(a[i]-4,x[i])-ppois(b[i]-5,x[i]))/(ppois(a[i],x[i])-ppois(b[i]-1,x[i])) |
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} |
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|
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##FINDING CENTRAL MOMENTS-TRUNCATED POISSON |
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m1=0 |
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m2=0 |
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m3=0 |
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m4=0 |
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m4t=0 |
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for (i in 1:k) |
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{ |
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m1[i]=fm1[i] |
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m2[i]=fm2[i]+fm1[i]-(fm1[i]*fm1[i]) |
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m3[i]=fm3[i]+fm2[i]*(3-(3*fm1[i]))+(fm1[i]-(3*fm1[i]*fm1[i])+(2*fm1[i]^3)) |
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m4[i]=fm4[i]+fm3[i]*(6-(4*fm1[i]))+fm2[i]*(7-(12*fm1[i])+(6*fm1[i]^2))+fm1[i]-(4*fm1[i]^2)+(6*fm1[i]^3)-(3*fm1[i]^4) |
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m4t[i] = m4[i]-(3*m2[i]^2)#Temporary Variable for next step |
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} |
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s1=sum(m1) |
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s2=sum(m2) |
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s3=sum(m3) |
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s4=sum(m4t) |
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##FINDING GAMMAS ---> EDGEWORTH EXPANSION |
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g1=s3/(s2^(3/2)) |
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g2=s4/(s2^2) |
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|
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##FINDING CHEBYSHEV-HERMITE POLYNOMIALS ---> EDGEWORTH EXPANSION |
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z=(s-s1)/sqrt(s2) |
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z2=z^2 |
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z3=z^3 |
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z4=z^4 |
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z6=z^6 |
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poly=1+g1*(z3-(3*z))/6+g2*(z4-(6*z2)+3)/24+(g1^2)*(z6-(15*z4)+(45*z2)-15)/72 |
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f=poly*exp(-z2/2)/sqrt(2*pi) |
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|
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##FINDING PROBABILITY FUNCTION BASED ON 'c' |
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pc=0 |
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for (i in 1:k) pc[i] <- ppois(a[i],x[i])-ppois(b[i]-1,x[i]) |
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pcp = prod(pc) #PRODUCT OF pc THAT HAS k ELEMENTS |
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pps = 1/dpois(s,s)#POISSON PROB FOR s WITH PARAMETER AS s |
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rp=pps*pcp*f/sqrt(s2)##REQUIRED PROBABILITY |
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rp |
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} |
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proc.time()-t1 |
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t=proc.time() |
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y=0 |
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s=sum(x) |
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M1=1 |
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M2=s |
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c=M1:M2 |
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M=length(c) |
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for (i in 1:M) y[i] <- round(sgp(c[i]), 4) |
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j=1 |
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vc=0 |
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while(j<=M){ |
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if (y[j]<1-alpha && 1-alpha < y[j+1]) |
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vc=j else |
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vc=vc |
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j = j+1 |
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} |
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|
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# vc##REQUIRED VALUE OF C |
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delta <- ((1-alpha)-y[vc])/(y[vc+1]-y[vc]) |
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|
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##FINDING LIMITS |
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sp <- x/s#SAMPLE PROPORTION |
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LL <- round(sp-(vc/s),4)#LOWER LIMIT |
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UL <- round(sp+(vc/s)+(2*delta/s),4)#UPPER LIMIT |
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LLA <- ULA <- 0 |
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for (r in 1:length(x)) { |
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if ( LL [r]< 0) LLA[r] = 0 else LLA[r]=LL[r] |
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if (UL[r] > 1) ULA[r] = 1 else ULA[r]=UL[r] |
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} |
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diA <- ULA-LLA##FIND LENGTH OF CIs |
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VOL <- round(prod(diA),8)##PRODUCT OF LENGTH OF CIs |
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data_frame( |
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method = "sg", |
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lower_limit = LL, |
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upper_limit = UL, |
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adj_ll = LLA, |
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adj_ul = ULA, |
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volume = VOL |
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) |
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} |
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BMDU <- function(x, d, seed=1492) { |
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set.seed(seed) |
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k <- length(x) |
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for(m in 1:k) { |
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if(x[m]<0) { warning('Arguments must be non-negative integers') } |
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} |
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if(d>=1 && d<=k) { |
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m=0 |
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l=0 |
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u=0 |
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diff=0 |
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s=sum(x) |
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s1=floor(k/d) |
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d1=runif(s1,0,1)###First half of the vector |
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d2=runif(k-s1,1,2)###Second half of the vector |
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a=c(d1,d2) |
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p=x+a###Prior for Dirichlet |
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dr=rdirichlet(10000, p)###Posterior |
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for(j in 1:k) { |
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l[j]=round(quantile(dr[,j],0.025),4)###Lower Limit |
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u[j]=round(quantile(dr[,j],0.975),4)###Upper Limit |
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m[j]=round(mean(dr[,j]),4)###Point Estimate |
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diff[j]=u[j]-l[j] |
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} |
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|
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data_frame( |
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method = "bmdu", |
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lower_limit = l, |
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upper_limit = m, |
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volume = prod(diff), |
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mean = m |
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) |
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} else { |
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warning('Size of the division should be less than the size of the input matrix') |
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data_frame( |
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method = "bmdu", |
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lower_limit = l, |
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upper_limit = m, |
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volume = prod(diff), |
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mean = m |
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) |
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} |
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|
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} |
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#' Bayesian Multinomial Dirichlet Model (Equal Prior) |
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#' |
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#' This method provides 95 percent simultaneous confidence interval for multinomial proportions based on Bayesian Multinomial Dirichlet model. However, it provides a mechanism through which user can split the Dirichlet prior parameter vector and suitable distributions can be incorporated for each of two groups. |
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#' |
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#' @md |
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#' @param x cell counts of given contingency table corresponding to a categorical data - non negative integers |
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#' @param p equal value for the Dirichlet prior parameter - positive real number |
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#' @param seed random seed for reproducible results |
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#' @return `tibble` with original limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals and the mean |
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#' @author Dr M Subbiah |
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#' @references Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2002). Bayesian Data Analysis. Chapman & Hall, London. |
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#' @export |
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#' @examples |
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#' y <- c(44, 55, 43, 32, 67, 78) |
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#' z <- 1 |
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#' scimp_bmde(y, z) |
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scimp_bmde <- function(x, p, seed=1492) { |
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|
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k <- length(x) |
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n_r <- 10000 |
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for(m in 1:k) { |
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if(x[m]<0) {warning('Arguments must be non-negative integers') } |
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} |
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set.seed(seed) |
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|
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po <- x+p |
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dr <- rdirichlet(n_r,po) |
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a <- l <- u <- dif <- 0 |
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for(j in 1:k) { |
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a[j] <- round(mean(dr[,j]), 4) |
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l[j] <- round(quantile(dr[,j], 0.025),4) |
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u[j] <- round(quantile(dr[,j], 0.975),4) |
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dif[j] <- u[j] - l[j] |
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} |
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|
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data_frame( |
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method = "bmde", |
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lower_limit = l, |
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upper_limit = u, |
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volume = prod(dif), |
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mean = a |
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) |
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} |
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#' Bayesian Multinomial Dirichlet Model (Unequal Prior) |
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#' |
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#' This method provides 95 percent simultaneous confidence interval for multinomial proportions based on Bayesian Multinomial Dirichlet model. However, it provides a mechanism through which user can split the Dirichlet prior parameter vector and suitable distributions can be incorporated for each of two groups. |
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#' |
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#' @md |
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#' @param x cell counts of given contingency table corresponding to a categorical data - non negative integers |
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#' @param d number of divisions required to split the prior vector of Dirichlet distribution to assign unequal values from U(0,1) and U(1,2) |
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#' @param seed random seed for reproducible results |
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#' @return `tibble` with original limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals and the mean |
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#' @author Dr M Subbiah |
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#' @references Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2002). Bayesian Data Analysis. Chapman & Hall, London. |
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#' @export |
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#' @examples |
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#' y <- c(44, 55, 43, 32, 67, 78) |
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#' z <- 2 |
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#' scimp_bmdu(y, z) |
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scimp_bmdu <- function(x, d, seed=1492) { |
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return(BMDU(x, d, seed)) |
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} |
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#' Fitzpatrick and Scott Confidence Interval |
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#' |
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#' The simultaneous confidence interval for multinomial proportions based on the method proposed in Fitzpatrick and Scott (1987) |
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#' |
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#' @md |
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#' @param inpmat the cell counts of given contingency tables corresponding to categorical data |
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#' @param alpha a number in `[0..1]` to get the upper 100(1-`alpha`) percentage point of the chi square distribution |
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#' @return `tibble` with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
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#' @author Dr M Subbiah |
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#' @references Fitzpatrick, S. and Scott, A. (1987). Quick simultaneous confidence interval for multinomial proportions. Journal of American Statistical Association 82(399): 875-878. |
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#' @export |
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#' @examples |
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#' y <- c(44, 55, 43, 32, 67, 78) |
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#' z <- 0.05 |
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#' scimp_fs(y, z) |
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scimp_fs <- function(inpmat, alpha) { |
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k <- length(inpmat) |
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s <- sum(inpmat) |
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zval <- abs(qnorm(1 - (alpha/2))) |
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pi <- inpmat/s |
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fs_ll <- pi - (zval / (2 * sqrt(s))) |
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fs_ul <- pi + (zval / (2 * sqrt(s))) |
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adj_ll <- adj_ul <- 0 |
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for (r in 1:length(inpmat)) { |
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if (fs_ll[r] < 0) adj_ll[r] <- 0 else adj_ll[r] <- fs_ll[r] |
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if (fs_ul[r] > 1) adj_ul[r] <- 1 else adj_ul[r] <- fs_ul[r] |
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} |
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ci_length <- adj_ul - adj_ll |
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volume <- round(prod(ci_length), 8) |
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data_frame( |
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method = "fs", |
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lower_limit = fs_ll, |
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upper_limit = fs_ul, |
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adj_ll = adj_ll, |
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adj_ul = adj_ul, |
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volume = volume |
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) -> ret |
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ret |
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} |
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#' Goodman Confidence Interval |
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#' |
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#' The simultaneous confidence interval for multinomial proportions based on the method proposed in Goodman (1965) |
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#' |
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#' @md |
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#' @param inpmat the cell counts of given contingency tables corresponding to categorical data |
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#' @param alpha a number in `[0..1]` to get the upper 100(1-`alpha`) percentage point of the chi square distribution |
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#' @return `tibble` with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
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#' @author Dr M Subbiah |
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#' @references Goodman, L.A. (1965). On Simultaneous Confidence Intervals for Multinomial Proportions. Technometrics 7: 247-254. |
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#' @export |
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#' @examples |
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#' y <- c(44, 55, 43, 32, 67, 78) |
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#' z <- 0.05 |
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#' scimp_goodman(y, z) |
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scimp_goodman <- function(inpmat, alpha) { |
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k <- length(inpmat) |
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s <- sum(inpmat) |
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chi <- qchisq(1 - (alpha/k), df = 1) |
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pi <- inpmat/s |
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goodman_ul <- (chi + 2*inpmat + sqrt(chi*chi + 4*inpmat*chi*(1 - pi)))/(2*(chi+s)) |
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goodman_ll <- (chi + 2*inpmat - sqrt(chi*chi + 4*inpmat*chi*(1 - pi)))/(2*(chi+s)) |
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adj_ll <- adj_ul <- 0 |
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for (r in 1:length(inpmat)) { |
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if (goodman_ll[r] < 0) adj_ll[r] <- 0 else adj_ll[r] <- goodman_ll[r] |
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if (goodman_ul[r] > 1) adj_ul[r] <- 1 else adj_ul[r] <- goodman_ul[r] |
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} |
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ci_length <- adj_ul - adj_ll |
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volume <- round(prod(ci_length), 8) |
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data_frame( |
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method = "goodman", |
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lower_limit = goodman_ll, |
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upper_limit = goodman_ul, |
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adj_ll = adj_ll, |
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adj_ul = adj_ul, |
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volume = volume |
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) -> ret |
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ret |
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} |
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#' Tools to ... |
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#' Simultaneous Confidence Intervals for Multinomial Proportions |
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#' |
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#' Methods for obtaining simultaneous confidence intervals for multinomial proportions have |
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#' been proposed by many authors and the present study include a variety of widely |
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#' applicable procedures. Seven classical methods (Wilson, Quesenberry and Hurst, Goodman, |
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#' Wald with and without continuity correction, Fitzpatrick and Scott, Sison and Glaz) |
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#' and Bayesian Dirichlet models are included in the package. The advantage of MCMC pack |
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#' has been exploited to derive the Dirichlet posterior directly and this also helps in |
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#' handling the Dirichlet prior parameters. This package is prepared to have equal and |
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#' unequal values for the Dirichlet prior distribution that will provide better scope for |
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#' data analysis and associated sensitivity analysis. |
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#' |
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#' @name scimple |
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#' @docType package |
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#' @author Bob Rudis (bob@@rud.is) |
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#' @import purrr |
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#' @author Dr M.Subbiah [primary], Bob Rudis (bob@@rud.is) [tidy version] |
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#' @import tibble stats MCMCpack |
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#' @importFrom dplyr mutate select |
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#' @importFrom purrr map map_df |
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NULL |
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|
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#' Quesenberry and Hurst Confidence Interval |
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#' |
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#' The simultaneous confidence interval for multinomial proportions based on the method proposed in Quesenberry and Hurst (1964) |
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#' |
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#' @md |
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#' @param inpmat the cell counts of given contingency tables corresponding to categorical data |
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#' @param alpha a number in `[0..1]` to get the upper 100(1-`alpha`) percentage point of the chi square distribution |
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#' @return `tibble` with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
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#' @author Dr M Subbiah |
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#' @references Quesensberry, C.P. and Hurst, D.C. (1964). Large Sample Simultaneous Confidence Intervals for Multinational Proportions. Technometrics, 6: 191-195. |
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#' @export |
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#' @examples |
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#' y <- c(44, 55, 43, 32, 67, 78) |
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#' z <- 0.05 |
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#' scimp_qh(y, z) |
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scimp_qh <- function(inpmat, alpha) { |
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k <- length(inpmat) |
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s <- sum(inpmat) |
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chi <- qchisq(1-alpha, df=k-1) |
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pi <- inpmat/s |
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qh_ul <- (chi + 2*inpmat + sqrt(chi*chi + 4*inpmat*chi*(1 - pi)))/(2*(chi+s)) |
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qh_ll <- (chi + 2*inpmat - sqrt(chi*chi + 4*inpmat*chi*(1 - pi)))/(2*(chi+s)) |
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adj_ll <- adj_ul <- 0 |
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for (r in 1:length(inpmat)) { |
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if (qh_ll[r] < 0) adj_ll[r] <- 0 else adj_ll[r] <- qh_ll[r] |
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if (qh_ul[r] > 1) adj_ul[r] <- 1 else adj_ul[r] <- qh_ul[r] |
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} |
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ci_length <- adj_ul - adj_ll |
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volume <- round(prod(ci_length), 8) |
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data_frame( |
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method = "qh", |
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lower_limit = qh_ll, |
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upper_limit = qh_ul, |
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adj_ll = adj_ll, |
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adj_ul = adj_ul, |
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volume = volume |
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) -> ret |
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ret |
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} |
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#' Calculate multiple simultaneous confidence intervals using selected methods (excluding Bayesian methods) |
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#' |
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#' Return simultaneous confidence intervals for multinomial proportions based on selected methods. |
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#' |
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#' @md |
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#' @param inpmat the cell counts of given contingency tables corresponding to categorical data |
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#' @param alpha a number in `[0..1]` to get the upper 100(1-`alpha`) percentage point of the chi square distribution |
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#' @param methods character vector of one or more methods to run over the input parameters |
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#' @return `tibble` with original and adjusted limits of multinomial proportions together with |
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#' product of length of k intervals as volume of simultaneous confidence intervals. |
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#' The `inputmat` and `alpha` values are also returned in the `tibble`. |
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#' @author Bob Rudis (bob@@rud.is) |
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#' @export |
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#' @examples |
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#' y <- c(44,55,43,32,67,78) |
|||
#' z <- 0.05 |
|||
#' scimple_ci(y, z) |
|||
#' scimple_ci(y, z, c("goodman", "waldcc", "wilson")) |
|||
scimple_ci <- function(inpmat, alpha, |
|||
methods=c("fs", "goodman", "qh", "sg", "wald", "waldcc", "wilson")) { |
|||
|
|||
methods <- unique(tolower(methods)) |
|||
methods <- match.arg(methods, c("fs", "goodman", "qh", "sg", "wald", "waldcc", "wilson"), |
|||
several.ok=TRUE) |
|||
|
|||
map_df(methods, function(func) { |
|||
df <- ci_funcs[[func]](inpmat, alpha) |
|||
mutate(df, inpmat=inpmat, alpha=alpha) |
|||
}) |
|||
|
|||
} |
|||
@ -0,0 +1,18 @@ |
|||
#' Sison & Glaz Confidence Interval |
|||
#' |
|||
#' The simultaneous confidence interval for multinomial proportions based on the method proposed in Sison and Glaz (1995). |
|||
#' |
|||
#' @md |
|||
#' @param inpmat the cell counts of given contingency tables corresponding to categorical data |
|||
#' @param alpha a number in `[0..1]` to get the upper 100(1-`alpha`) percentage point of the chi square distribution |
|||
#' @return `tibble` with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
#' @author Dr M Subbiah |
|||
#' @references Sison, P.C. and Glaz J. (1995). Simultaneous Confidence Intervals and Sample Size Determination for Multinomial Proportions. Journal of the American Statistical Association 90: 366-369. |
|||
#' @export |
|||
#' @examples |
|||
#' y <- c(44, 55, 43, 32, 67, 78) |
|||
#' z <- 0.05 |
|||
#' scimp_sg(y, z) |
|||
scimp_sg <- function(inpmat, alpha) { |
|||
return(SG(inpmat, alpha)) |
|||
} |
|||
@ -0,0 +1,49 @@ |
|||
#' Wald Confidence Interval |
|||
#' |
|||
#' The simple Wald type interval for multinomial proportions which is symmetrical about the |
|||
#' sample proportions. In this method no continuity corrections are made to avoid zero width |
|||
#' intervals when the sample proportions are at extreme. |
|||
#' |
|||
#' @md |
|||
#' @param inpmat the cell counts of given contingency tables corresponding to categorical data |
|||
#' @param alpha a number in `[0..1]` to get the upper 100(1-`alpha`) percentage point of the chi square distribution |
|||
#' @return `tibble` with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
#' @author Dr M Subbiah |
|||
#' @references Wald, A Tests of statistical hypotheses concerning several parameters when the number of observations is large, Trans. Am. Math. Soc. 54 (1943) 426-482. |
|||
#' @export |
|||
#' @examples |
|||
#' y <- c(44, 55, 43, 32, 67, 78) |
|||
#' z <- 0.05 |
|||
#' scimp_wald(y, z) |
|||
scimp_wald <- function(inpmat, alpha) { |
|||
|
|||
k <- length(inpmat) |
|||
s <- sum(inpmat) |
|||
chi <- qchisq(1 - alpha, df = 1) |
|||
pi <- inpmat / s |
|||
|
|||
wald_ll = pi - (sqrt(chi*(pi)*(1-pi)/s)) |
|||
wald_ul = pi + (sqrt(chi*(pi)*(1-pi)/s)) |
|||
|
|||
adj_ll <- adj_ul <- 0 |
|||
|
|||
for (r in 1:length(inpmat)) { |
|||
if (wald_ll[r] < 0) adj_ll[r] <- 0 else adj_ll[r] <- wald_ll[r] |
|||
if (wald_ul[r] > 1) adj_ul[r] <- 1 else adj_ul[r] <- wald_ul[r] |
|||
} |
|||
|
|||
ci_length <- adj_ul - adj_ll |
|||
volume <- round(prod(ci_length), 8) |
|||
|
|||
data_frame( |
|||
method = "wald", |
|||
lower_limit = wald_ll, |
|||
upper_limit = wald_ul, |
|||
adj_ll = adj_ll, |
|||
adj_ul = adj_ul, |
|||
volume = volume |
|||
) -> ret |
|||
|
|||
ret |
|||
|
|||
} |
|||
@ -0,0 +1,46 @@ |
|||
#' Wald Confidence Interval (with continuity correction) |
|||
#' |
|||
#' The simple Wald type interval with continuity corrections for multinomial proportions which is symmetrical about the sample proportions. |
|||
#' |
|||
#' @md |
|||
#' @param inpmat the cell counts of given contingency tables corresponding to categorical data |
|||
#' @param alpha a number in `[0..1]` to get the upper 100(1-`alpha`) percentage point of the chi square distribution |
|||
#' @return `tibble` with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
#' @author Dr M Subbiah |
|||
#' @export |
|||
#' @examples |
|||
#' y <- c(44, 55, 43, 32, 67, 78) |
|||
#' z <- 0.05 |
|||
#' scimp_waldcc(y, z) |
|||
scimp_waldcc <- function(inpmat, alpha) { |
|||
|
|||
k <- length(inpmat) |
|||
s <- sum(inpmat) |
|||
chi <- qchisq(1 - alpha, df = 1) |
|||
pi <- inpmat / s |
|||
|
|||
waldcc_ll = pi - (sqrt(chi*(pi)*(1-pi)/s))-(1/(2*s)) |
|||
waldcc_ul = pi + (sqrt(chi*(pi)*(1-pi)/s))+(1/(2*s)) |
|||
|
|||
adj_ll <- adj_ul <- 0 |
|||
|
|||
for (r in 1:length(inpmat)) { |
|||
if (waldcc_ll[r] < 0) adj_ll[r] <- 0 else adj_ll[r] <- waldcc_ll[r] |
|||
if (waldcc_ul[r] > 1) adj_ul[r] <- 1 else adj_ul[r] <- waldcc_ul[r] |
|||
} |
|||
|
|||
ci_length <- adj_ul - adj_ll |
|||
volume <- round(prod(ci_length), 8) |
|||
|
|||
data_frame( |
|||
method = "waldcc", |
|||
lower_limit = waldcc_ll, |
|||
upper_limit = waldcc_ul, |
|||
adj_ll = adj_ll, |
|||
adj_ul = adj_ul, |
|||
volume = volume |
|||
) -> ret |
|||
|
|||
ret |
|||
|
|||
} |
|||
@ -0,0 +1,47 @@ |
|||
#' Wilson Confidence Interval |
|||
#' |
|||
#' The simultaneous confidence interval for multinomial proportions based on the method proposed in Wilson (1927) |
|||
#' |
|||
#' @md |
|||
#' @param inpmat the cell counts of given contingency tables corresponding to categorical data |
|||
#' @param alpha a number in `[0..1]` to get the upper 100(1-`alpha`) percentage point of the chi square distribution |
|||
#' @return `tibble` with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
#' @author Dr M Subbiah |
|||
#' @references E.B. Wilson, Probable inference, the law of succession and statistical inference, J.Am. Stat. Assoc. 22 (1927) 209-212. |
|||
#' @export |
|||
#' @examples |
|||
#' y <- c(44, 55, 43, 32, 67, 78) |
|||
#' z <- 0.05 |
|||
#' scimp_wilson(y, z) |
|||
scimp_wilson <- function(inpmat, alpha) { |
|||
|
|||
k <- length(inpmat) |
|||
s <- sum(inpmat) |
|||
chi <- qchisq(1 - alpha, df = 1) |
|||
pi <- inpmat / s |
|||
|
|||
wilson_ul <- (chi + 2*inpmat + sqrt(chi*chi + 4*inpmat*chi*(1 - pi)))/(2*(chi+s)) |
|||
wilson_ll <- (chi + 2*inpmat - sqrt(chi*chi + 4*inpmat*chi*(1 - pi)))/(2*(chi+s)) |
|||
|
|||
adj_ll <- adj_ul <- 0 |
|||
|
|||
for (r in 1:length(inpmat)) { |
|||
if (wilson_ll[r] < 0) adj_ll[r] <- 0 else adj_ll[r] <- wilson_ll[r] |
|||
if (wilson_ul[r] > 1) adj_ul[r] <- 1 else adj_ul[r] <- wilson_ul[r] |
|||
} |
|||
|
|||
ci_length <- adj_ul - adj_ll |
|||
volume <- round(prod(ci_length), 8) |
|||
|
|||
data_frame( |
|||
method = "wilson", |
|||
lower_limit = wilson_ll, |
|||
upper_limit = wilson_ul, |
|||
adj_ll = adj_ll, |
|||
adj_ul = adj_ul, |
|||
volume = volume |
|||
) -> ret |
|||
|
|||
ret |
|||
|
|||
} |
|||
@ -0,0 +1,2 @@ |
|||
ci_funcs <- c(scimp_fs, scimp_goodman, scimp_qh, scimp_sg, scimp_wald, scimp_waldcc, scimp_wilson) |
|||
names(ci_funcs) <- c("fs", "goodman", "qh", "sg", "wald", "waldcc", "wilson") |
|||
@ -0,0 +1,98 @@ |
|||
|
|||
scimple : Simultaneous Confidence Interval for Multinomial Proportion |
|||
|
|||
This is a tidy version of the `CoinMinD` R package with some extra bits tossed in for good measure. |
|||
|
|||
The following functions are implemented: |
|||
|
|||
- `scimple_ci`: Calculate multiple simultaneous confidence intervals using selected methods (excluding Bayesian methods) |
|||
- `scimp_bmde`: Bayesian Multinomial Dirichlet Model (Equal Prior) |
|||
- `scimp_bmdu`: Bayesian Multinomial Dirichlet Model (Unequal Prior) |
|||
- `scimp_fs`: Fitzpatrick and Scott Confidence Interval |
|||
- `scimp_goodman`: Goodman Confidence Interval |
|||
- `scimp_qh`: Quesenberry & Hurst Confidence Interval |
|||
- `scimp_sg`: Sison & Glaz Confidence Interval |
|||
- `scimp_wald`: Wald Confidence Interval |
|||
- `scimp_waldcc`: Wald Confidence Interval (with continuity correction) |
|||
- `scimp_wilson`: Wilson Confidence Interval |
|||
|
|||
### Installation |
|||
|
|||
``` r |
|||
devtools::install_github("hrbrmstr/scimple") |
|||
``` |
|||
|
|||
``` r |
|||
options(width=120) |
|||
``` |
|||
|
|||
### Usage |
|||
|
|||
``` r |
|||
library(scimple) |
|||
library(hrbrthemes) |
|||
library(tidyverse) |
|||
|
|||
# current verison |
|||
packageVersion("scimple") |
|||
``` |
|||
|
|||
## [1] '0.1.0' |
|||
|
|||
``` r |
|||
y <- c(44,55,43,32,67,78) |
|||
z <- 0.05 |
|||
|
|||
cis <- scimple_ci(y, z) |
|||
|
|||
print(cis) |
|||
``` |
|||
|
|||
## # A tibble: 42 × 8 |
|||
## method lower_limit upper_limit adj_ll adj_ul volume inpmat alpha |
|||
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> |
|||
## 1 fs 0.08306258 0.1927995 0.08306258 0.1927995 1.75e-06 44 0.05 |
|||
## 2 fs 0.11754534 0.2272823 0.11754534 0.2272823 1.75e-06 55 0.05 |
|||
## 3 fs 0.07992778 0.1896647 0.07992778 0.1896647 1.75e-06 43 0.05 |
|||
## 4 fs 0.04544502 0.1551819 0.04544502 0.1551819 1.75e-06 32 0.05 |
|||
## 5 fs 0.15516289 0.2648998 0.15516289 0.2648998 1.75e-06 67 0.05 |
|||
## 6 fs 0.18964565 0.2993826 0.18964565 0.2993826 1.75e-06 78 0.05 |
|||
## 7 goodman 0.09468368 0.1966413 0.09468368 0.1966413 1.55e-06 44 0.05 |
|||
## 8 goodman 0.12376893 0.2350489 0.12376893 0.2350489 1.55e-06 55 0.05 |
|||
## 9 goodman 0.09208527 0.1931040 0.09208527 0.1931040 1.55e-06 43 0.05 |
|||
## 10 goodman 0.06412671 0.1535697 0.06412671 0.1535697 1.55e-06 32 0.05 |
|||
## # ... with 32 more rows |
|||
|
|||
``` r |
|||
ggplot(cis) + |
|||
geom_segment(aes(x=lower_limit, xend=upper_limit, y=method, yend=method, color=method)) + |
|||
scale_color_ipsum(name=NULL) + |
|||
facet_wrap(~inpmat, scales="free_x") + |
|||
labs(x=NULL, y=NULL, |
|||
title="Multipe simultaneous confidence intervals", |
|||
subtitle="Note free X scale") + |
|||
theme_ipsum_rc(grid="X") + |
|||
theme(legend.position="bottom") |
|||
``` |
|||
|
|||
<img src="README_files/figure-markdown_github/unnamed-chunk-3-1.png" width="960" /> |
|||
|
|||
### Test Results |
|||
|
|||
``` r |
|||
library(scimple) |
|||
library(testthat) |
|||
|
|||
date() |
|||
``` |
|||
|
|||
## [1] "Fri Mar 3 14:36:17 2017" |
|||
|
|||
``` r |
|||
test_dir("tests/") |
|||
``` |
|||
|
|||
## testthat results ======================================================================================================== |
|||
## OK: 0 SKIPPED: 0 FAILED: 0 |
|||
## |
|||
## DONE =================================================================================================================== |
|||
|
After Width: | Height: | Size: 115 KiB |
@ -0,0 +1,32 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/bmde.r |
|||
\name{scimp_bmde} |
|||
\alias{scimp_bmde} |
|||
\title{Bayesian Multinomial Dirichlet Model (Equal Prior)} |
|||
\usage{ |
|||
scimp_bmde(x, p, seed = 1492) |
|||
} |
|||
\arguments{ |
|||
\item{x}{cell counts of given contingency table corresponding to a categorical data - non negative integers} |
|||
|
|||
\item{p}{equal value for the Dirichlet prior parameter - positive real number} |
|||
|
|||
\item{seed}{random seed for reproducible results} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals and the mean |
|||
} |
|||
\description{ |
|||
This method provides 95 percent simultaneous confidence interval for multinomial proportions based on Bayesian Multinomial Dirichlet model. However, it provides a mechanism through which user can split the Dirichlet prior parameter vector and suitable distributions can be incorporated for each of two groups. |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 1 |
|||
scimp_bmde(y, z) |
|||
} |
|||
\references{ |
|||
Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2002). Bayesian Data Analysis. Chapman & Hall, London. |
|||
} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,32 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/bmdu.r |
|||
\name{scimp_bmdu} |
|||
\alias{scimp_bmdu} |
|||
\title{Bayesian Multinomial Dirichlet Model (Unequal Prior)} |
|||
\usage{ |
|||
scimp_bmdu(x, d, seed = 1492) |
|||
} |
|||
\arguments{ |
|||
\item{x}{cell counts of given contingency table corresponding to a categorical data - non negative integers} |
|||
|
|||
\item{d}{number of divisions required to split the prior vector of Dirichlet distribution to assign unequal values from U(0,1) and U(1,2)} |
|||
|
|||
\item{seed}{random seed for reproducible results} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals and the mean |
|||
} |
|||
\description{ |
|||
This method provides 95 percent simultaneous confidence interval for multinomial proportions based on Bayesian Multinomial Dirichlet model. However, it provides a mechanism through which user can split the Dirichlet prior parameter vector and suitable distributions can be incorporated for each of two groups. |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 2 |
|||
scimp_bmdu(y, z) |
|||
} |
|||
\references{ |
|||
Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2002). Bayesian Data Analysis. Chapman & Hall, London. |
|||
} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,30 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/fitz-scott.r |
|||
\name{scimp_fs} |
|||
\alias{scimp_fs} |
|||
\title{Fitzpatrick and Scott Confidence Interval} |
|||
\usage{ |
|||
scimp_fs(inpmat, alpha) |
|||
} |
|||
\arguments{ |
|||
\item{inpmat}{the cell counts of given contingency tables corresponding to categorical data} |
|||
|
|||
\item{alpha}{a number in \code{[0..1]} to get the upper 100(1-\code{alpha}) percentage point of the chi square distribution} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
} |
|||
\description{ |
|||
The simultaneous confidence interval for multinomial proportions based on the method proposed in Fitzpatrick and Scott (1987) |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 0.05 |
|||
scimp_fs(y, z) |
|||
} |
|||
\references{ |
|||
Fitzpatrick, S. and Scott, A. (1987). Quick simultaneous confidence interval for multinomial proportions. Journal of American Statistical Association 82(399): 875-878. |
|||
} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,30 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/goodman.r |
|||
\name{scimp_goodman} |
|||
\alias{scimp_goodman} |
|||
\title{Goodman Confidence Interval} |
|||
\usage{ |
|||
scimp_goodman(inpmat, alpha) |
|||
} |
|||
\arguments{ |
|||
\item{inpmat}{the cell counts of given contingency tables corresponding to categorical data} |
|||
|
|||
\item{alpha}{a number in \code{[0..1]} to get the upper 100(1-\code{alpha}) percentage point of the chi square distribution} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
} |
|||
\description{ |
|||
The simultaneous confidence interval for multinomial proportions based on the method proposed in Goodman (1965) |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 0.05 |
|||
scimp_goodman(y, z) |
|||
} |
|||
\references{ |
|||
Goodman, L.A. (1965). On Simultaneous Confidence Intervals for Multinomial Proportions. Technometrics 7: 247-254. |
|||
} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,30 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/qh.r |
|||
\name{scimp_qh} |
|||
\alias{scimp_qh} |
|||
\title{Quesenberry and Hurst Confidence Interval} |
|||
\usage{ |
|||
scimp_qh(inpmat, alpha) |
|||
} |
|||
\arguments{ |
|||
\item{inpmat}{the cell counts of given contingency tables corresponding to categorical data} |
|||
|
|||
\item{alpha}{a number in \code{[0..1]} to get the upper 100(1-\code{alpha}) percentage point of the chi square distribution} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
} |
|||
\description{ |
|||
The simultaneous confidence interval for multinomial proportions based on the method proposed in Quesenberry and Hurst (1964) |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 0.05 |
|||
scimp_qh(y, z) |
|||
} |
|||
\references{ |
|||
Quesensberry, C.P. and Hurst, D.C. (1964). Large Sample Simultaneous Confidence Intervals for Multinational Proportions. Technometrics, 6: 191-195. |
|||
} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,30 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/sison-glaz.r |
|||
\name{scimp_sg} |
|||
\alias{scimp_sg} |
|||
\title{Sison & Glaz Confidence Interval} |
|||
\usage{ |
|||
scimp_sg(inpmat, alpha) |
|||
} |
|||
\arguments{ |
|||
\item{inpmat}{the cell counts of given contingency tables corresponding to categorical data} |
|||
|
|||
\item{alpha}{a number in \code{[0..1]} to get the upper 100(1-\code{alpha}) percentage point of the chi square distribution} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
} |
|||
\description{ |
|||
The simultaneous confidence interval for multinomial proportions based on the method proposed in Sison and Glaz (1995). |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 0.05 |
|||
scimp_sg(y, z) |
|||
} |
|||
\references{ |
|||
Sison, P.C. and Glaz J. (1995). Simultaneous Confidence Intervals and Sample Size Determination for Multinomial Proportions. Journal of the American Statistical Association 90: 366-369. |
|||
} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,32 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/wald.r |
|||
\name{scimp_wald} |
|||
\alias{scimp_wald} |
|||
\title{Wald Confidence Interval} |
|||
\usage{ |
|||
scimp_wald(inpmat, alpha) |
|||
} |
|||
\arguments{ |
|||
\item{inpmat}{the cell counts of given contingency tables corresponding to categorical data} |
|||
|
|||
\item{alpha}{a number in \code{[0..1]} to get the upper 100(1-\code{alpha}) percentage point of the chi square distribution} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
} |
|||
\description{ |
|||
The simple Wald type interval for multinomial proportions which is symmetrical about the |
|||
sample proportions. In this method no continuity corrections are made to avoid zero width |
|||
intervals when the sample proportions are at extreme. |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 0.05 |
|||
scimp_wald(y, z) |
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} |
|||
\references{ |
|||
Wald, A Tests of statistical hypotheses concerning several parameters when the number of observations is large, Trans. Am. Math. Soc. 54 (1943) 426-482. |
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} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,27 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/waldcc.r |
|||
\name{scimp_waldcc} |
|||
\alias{scimp_waldcc} |
|||
\title{Wald Confidence Interval (with continuity correction)} |
|||
\usage{ |
|||
scimp_waldcc(inpmat, alpha) |
|||
} |
|||
\arguments{ |
|||
\item{inpmat}{the cell counts of given contingency tables corresponding to categorical data} |
|||
|
|||
\item{alpha}{a number in \code{[0..1]} to get the upper 100(1-\code{alpha}) percentage point of the chi square distribution} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
} |
|||
\description{ |
|||
The simple Wald type interval with continuity corrections for multinomial proportions which is symmetrical about the sample proportions. |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 0.05 |
|||
scimp_waldcc(y, z) |
|||
} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,30 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/wilson.r |
|||
\name{scimp_wilson} |
|||
\alias{scimp_wilson} |
|||
\title{Wilson Confidence Interval} |
|||
\usage{ |
|||
scimp_wilson(inpmat, alpha) |
|||
} |
|||
\arguments{ |
|||
\item{inpmat}{the cell counts of given contingency tables corresponding to categorical data} |
|||
|
|||
\item{alpha}{a number in \code{[0..1]} to get the upper 100(1-\code{alpha}) percentage point of the chi square distribution} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original and adjusted limits of multinomial proportions together with product of length of k intervals as volume of simultaneous confidence intervals |
|||
} |
|||
\description{ |
|||
The simultaneous confidence interval for multinomial proportions based on the method proposed in Wilson (1927) |
|||
} |
|||
\examples{ |
|||
y <- c(44, 55, 43, 32, 67, 78) |
|||
z <- 0.05 |
|||
scimp_wilson(y, z) |
|||
} |
|||
\references{ |
|||
E.B. Wilson, Probable inference, the law of succession and statistical inference, J.Am. Stat. Assoc. 22 (1927) 209-212. |
|||
} |
|||
\author{ |
|||
Dr M Subbiah |
|||
} |
|||
@ -0,0 +1,21 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/package.r |
|||
\docType{package} |
|||
\name{scimple} |
|||
\alias{scimple} |
|||
\alias{scimple-package} |
|||
\title{Simultaneous Confidence Intervals for Multinomial Proportions} |
|||
\description{ |
|||
Methods for obtaining simultaneous confidence intervals for multinomial proportions have |
|||
been proposed by many authors and the present study include a variety of widely |
|||
applicable procedures. Seven classical methods (Wilson, Quesenberry and Hurst, Goodman, |
|||
Wald with and without continuity correction, Fitzpatrick and Scott, Sison and Glaz) |
|||
and Bayesian Dirichlet models are included in the package. The advantage of MCMC pack |
|||
has been exploited to derive the Dirichlet posterior directly and this also helps in |
|||
handling the Dirichlet prior parameters. This package is prepared to have equal and |
|||
unequal values for the Dirichlet prior distribution that will provide better scope for |
|||
data analysis and associated sensitivity analysis. |
|||
} |
|||
\author{ |
|||
Dr M.Subbiah [primary], Bob Rudis (bob@rud.is) [tidy version] |
|||
} |
|||
@ -0,0 +1,33 @@ |
|||
% Generated by roxygen2: do not edit by hand |
|||
% Please edit documentation in R/scimple.r |
|||
\name{scimple_ci} |
|||
\alias{scimple_ci} |
|||
\title{Calculate multiple simultaneous confidence intervals using selected methods (excluding Bayesian methods)} |
|||
\usage{ |
|||
scimple_ci(inpmat, alpha, methods = c("fs", "goodman", "qh", "sg", "wald", |
|||
"waldcc", "wilson")) |
|||
} |
|||
\arguments{ |
|||
\item{inpmat}{the cell counts of given contingency tables corresponding to categorical data} |
|||
|
|||
\item{alpha}{a number in \code{[0..1]} to get the upper 100(1-\code{alpha}) percentage point of the chi square distribution} |
|||
|
|||
\item{methods}{character vector of one or more methods to run over the input parameters} |
|||
} |
|||
\value{ |
|||
\code{tibble} with original and adjusted limits of multinomial proportions together with |
|||
product of length of k intervals as volume of simultaneous confidence intervals. |
|||
The \code{inputmat} and \code{alpha} values are also returned in the \code{tibble}. |
|||
} |
|||
\description{ |
|||
Return simultaneous confidence intervals for multinomial proportions based on selected methods. |
|||
} |
|||
\examples{ |
|||
y <- c(44,55,43,32,67,78) |
|||
z <- 0.05 |
|||
scimple_ci(y, z) |
|||
scimple_ci(y, z, c("goodman", "waldcc", "wilson")) |
|||
} |
|||
\author{ |
|||
Bob Rudis (bob@rud.is) |
|||
} |
|||
@ -0,0 +1,21 @@ |
|||
Version: 1.0 |
|||
|
|||
RestoreWorkspace: No |
|||
SaveWorkspace: No |
|||
AlwaysSaveHistory: No |
|||
|
|||
EnableCodeIndexing: Yes |
|||
UseSpacesForTab: Yes |
|||
NumSpacesForTab: 2 |
|||
Encoding: UTF-8 |
|||
|
|||
RnwWeave: Sweave |
|||
LaTeX: pdfLaTeX |
|||
|
|||
AutoAppendNewline: Yes |
|||
StripTrailingWhitespace: Yes |
|||
|
|||
BuildType: Package |
|||
PackageUseDevtools: Yes |
|||
PackageInstallArgs: --no-multiarch --with-keep.source |
|||
PackageRoxygenize: rd,collate,namespace |
|||
Loading…
Reference in new issue