v0.22

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: CAinterprTools
 Title: Package for graphical aid in Correspondence Analysis interpretation and
     significance testing
-Version: 0.21
+Version: 0.22
 Authors@R: "Gianmarco ALberti <gianmarcoalberti@tin.it> <gianmarcoalberti@gmail.com>[aut, cre]"
 Description: A number of interesting packages are available to perform
     Correspondence Analysis in R. At the best of my knowledge, they lack

R/sig_dim_perm.R

@@ -12,7 +12,7 @@
 #' data(greenacre_data)
 #' sig.dim.perm(greenacre_data, 1,2) #Returns a scatterplot of the permuted inertia of the 1 CA dimension against the permuted inertia of the 2 CA dimension. Observed inertia of the selected dimensions and 95th percentile of the permuted inertias are also displayed for testing the significance of the observed inertias.
 #' 
-sig.dim.perm <- function(data, x=1, y=2, B=999) {
+sig.dim.perm <- function(data, x=1, y=2, B=1000) {
   nIter <- B+1
   numb.dim.cols <- ncol(data) - 1
   numb.dim.rows <- nrow(data) - 1
@@ -21,19 +21,14 @@ sig.dim.perm <- function(data, x=1, y=2, B=999) {
   res <- CA(data, graph=FALSE)
   d[1,]<- rbind(res$eig[,1])
   pb <- txtProgressBar(min = 0, max = nIter, style = 3)                                     #set the progress bar to be used inside the loop
-
   for (i in 2:nIter){
     rand.table <- as.data.frame(r2dtable(1, apply(data, 1,sum), apply(data, 2, sum)))  
     res <- CA(rand.table, graph=FALSE)
     d[i,] <- rbind(res$eig[,1])
     setTxtProgressBar(pb, i)
   }
-
-  perm.pvalues <- round(1 + colSums(d[-1,] > d[1,][col(d[-1,])]) / (B+1), 4)
-  pvalues.toreport <- ifelse(perm.pvalues < 0.001, "< 0.001", 
-                             ifelse(perm.pvalues < 0.01, "< 0.01", 
-                                    ifelse(perm.pvalues < 0.05, "< 0.05",
-                                           round(perm.pvalues, 3))))
+  perm.pvalues <- round(colSums(d[-1,] > d[1,][col(d[-1,])]) / B, 4)
+  pvalues.toreport <- ifelse(perm.pvalues < 0.001, "< 0.001", ifelse(perm.pvalues < 0.01, "< 0.01", ifelse(perm.pvalues < 0.05, "< 0.05",round(perm.pvalues, 3))))
   plot(d[,x], d[,y], 
        main=" Scatterplot of permuted dimensions' inertia", 
        sub="large red dot: observed inertia", 
@@ -44,4 +39,4 @@ sig.dim.perm <- function(data, x=1, y=2, B=999) {
        col="#00000088") # hex code for 'black'; last two digits set the transparency
   par(new=TRUE)
   plot(d[1,x], d[1,y], xlim=c(min(d[,x]), max(d[,x])), ylim=c(min(d[,y]), max(d[,y])), pch=20, cex=1.5, col="red", xaxt = "n", xlab = "", ylab = "", sub = "") #add the observed inertia as a large red dot
-  }
+}

R/sig_dim_perm_scree.R

@@ -14,7 +14,7 @@
 #' data(greenacre_data)
 #' sig.dim.perm.scree(greenacre_data, 9999)
 #' 
-sig.dim.perm.scree <- function(data, B=999, cex=0.7, pos=4, offset=0.5){
+sig.dim.perm.scree <- function(data, B=1000, cex=0.7, pos=4, offset=0.5){
   options(scipen = 999)
   nIter <- B+1
   numb.dim.cols <- ncol(data) - 1
@@ -24,33 +24,24 @@ sig.dim.perm.scree <- function(data, B=999, cex=0.7, pos=4, offset=0.5){
   res <- CA(data, graph=FALSE)
   d[1,]<- rbind(res$eig[,1])
   pb <- txtProgressBar(min = 0, max = nIter, style = 3)                                     #set the progress bar to be used inside the loop
-
   for (i in 2:nIter){
     rand.table <- as.data.frame(r2dtable(1, apply(data, 1,sum), apply(data, 2, sum)))  
     res <- CA(rand.table, graph=FALSE)
     d[i,] <- rbind(res$eig[,1])
     setTxtProgressBar(pb, i)
   }
-
   target.percent <- apply(d[-c(1),],2, quantile, probs = 0.95) #calculate the 95th percentile of the randomized eigenvalues, excluding the first row (which store the observed eigenvalues)
-
   max.y.lim <- max(d[1,], target.percent)
   obs.eig <- as.matrix(d[1,])
   obs.eig.to.plot <- melt(obs.eig) #requires reshape2
-
-  perm.p.values <- round(1 + colSums(d[-1,] > d[1,][col(d[-1,])]) / (B+1), 4)
-  perm.pvalues.to.report <- ifelse(perm.p.values < 0.001, "< 0.001", 
-                                   ifelse(perm.p.values < 0.01, "< 0.01", 
-                                          ifelse(perm.p.values < 0.05, "< 0.05", 
-                                                 round(perm.p.values, 3))))
-
+  perm.p.values <- round(colSums(d[-1,] > d[1,][col(d[-1,])]) / B, 4)
+  perm.pvalues.to.report <- ifelse(perm.p.values < 0.001, "< 0.001", ifelse(perm.p.values < 0.01, "< 0.01", ifelse(perm.p.values < 0.05, "< 0.05", round(perm.p.values, 3))))
   plot(obs.eig.to.plot$value, type = "o", ylim = c(0, max.y.lim), xaxt = "n", xlab = "Dimensions", ylab = "Eigenvalue", pch=20)
   text(obs.eig.to.plot$value, labels = perm.pvalues.to.report, cex = cex, pos = pos, offset = offset)
   axis(1, at = 1:table.dim)
-  title(main = "Correspondence Analysis: \nscree-plot of observed and permuted eigenvalues", 
-        sub = paste0("Black dots=observed eigenvalues; blue dots=95th percentile of the permutated eigenvalues' distribution. Number of permutations: ", B), 
-        cex.sub = 0.8)
+  title(main = "Correspondence Analysis: \nscree-plot of observed and permuted eigenvalues", sub = paste0("Black dots=observed eigenvalues; blue dots=95th percentile of the permutated eigenvalues' distribution. Number of permutations: ", B), cex.sub = 0.8)
   par(new = TRUE)
   percentile.to.plot <- melt(target.percent)
   plot(percentile.to.plot$value, type = "o", lty = 2, col = "blue", ylim = c(0, max.y.lim), xaxt = "n", xlab = "", ylab = "", sub = "")
+  #return(d)
 }

README.md

@@ -1,5 +1,5 @@
 # CAinterprTools
-vers 0.21
+vers 0.22
 
 A number of interesting packages are available to perform Correspondence Analysis in R. At the best of my knowledge, however, they lack some tools to help users to eyeball some critical CA aspects (e.g., contribution of rows/cols categories to the principal axes, quality of the display,correlation of rows/cols categories with dimensions, etc). Besides providing those facilities, this package allows calculating the significance of the CA dimensions by means of the 'Average Rule', the Malinvaud test, and by permutation test. Further, it allows to also calculate the permuted significance of the CA total inertia. 
 
@@ -307,7 +307,11 @@ improvements and typos fixes to the help documentation; improvements to the char
 
 New in `version 0.21`: 
 
-improvements and typos fixes to the help documentation; adjustments and improvements to the permuted p-values calculation in the `sig.dim.perm()`, `sig.dim.perm.scree()`, and `sig.tot.inertia.perm()` functions.   
+improvements and typos fixes to the help documentation; adjustments and improvements to the permuted p-values calculation in the `sig.dim.perm()`, `sig.dim.perm.scree()`, and `sig.tot.inertia.perm()` functions.
+
+New in `version 0.22`: 
+
+improvements and typos fixes to the help documentation; under-the-hood improvements of performance; error fix in the calculation of p-values in the `sig.dim.perm()` and `sig.dim.perm.scree()` functions (error introduced in v0.21).
 
 ## Installation
 To install the package  in R, just follow the few steps listed below:
@@ -322,7 +326,7 @@ library(devtools)
 ```
 3) download the 'CAinterprTools' package  from GitHub via the 'devtools''s command: 
 ```r
-install_github("gianmarcoalberti/CAinterprTools@v0.21")
+install_github("gianmarcoalberti/CAinterprTools@v0.22")
 ```
 4) load the package: 
 ```r

man/CAinterprTools-package.Rd

@@ -20,7 +20,7 @@ A number of interesting packages are available to perform
 \tabular{ll}{
 Package: \tab CAinterprTools\cr
 Type: \tab Package\cr
-Version: \tab 0.21\cr
+Version: \tab 0.22\cr
 Date: \tab 2018-04\cr
 License: \tab GPL\cr
 }