Correlation between variables and components using pls package - r

Question

Using the data.frame below (Source: http://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/en_Tanagra_PLSR_Software_Comparison.pdf)
Data
df <- read.table(text = c("
diesel twodoors sportsstyle wheelbase length width height curbweight enginesize horsepower horse_per_weight conscity price symboling
0 1 0 97 172 66 56 2209 109 85 0.0385 8.7 7975 2
0 0 0 100 177 66 54 2337 109 102 0.0436 9.8 13950 2
0 0 0 116 203 72 57 3740 234 155 0.0414 14.7 34184 -1
0 1 1 103 184 68 52 3016 171 161 0.0534 12.4 15998 3
0 0 0 101 177 65 54 2765 164 121 0.0438 11.2 21105 0
0 1 0 90 169 65 52 2756 194 207 0.0751 13.8 34028 3
1 0 0 105 175 66 54 2700 134 72 0.0267 7.6 18344 0
0 0 0 108 187 68 57 3020 120 97 0.0321 12.4 11900 0
0 0 1 94 157 64 51 1967 90 68 0.0346 7.6 6229 1
0 1 0 95 169 64 53 2265 98 112 0.0494 9.0 9298 1
1 0 0 96 166 64 53 2275 110 56 0.0246 6.9 7898 0
0 1 0 100 177 66 53 2507 136 110 0.0439 12.4 15250 2
0 1 1 94 157 64 51 1876 90 68 0.0362 6.4 5572 1
0 0 0 95 170 64 54 2024 97 69 0.0341 7.6 7349 1
0 1 1 95 171 66 52 2823 152 154 0.0546 12.4 16500 1
0 0 0 103 175 65 60 2535 122 88 0.0347 9.8 8921 -1
0 0 0 113 200 70 53 4066 258 176 0.0433 15.7 32250 0
0 0 0 95 165 64 55 1938 97 69 0.0356 7.6 6849 1
1 0 0 97 172 66 56 2319 97 68 0.0293 6.4 9495 2
0 0 0 97 172 66 56 2275 109 85 0.0374 8.7 8495 2"), header = T)
and this
Code
library(plsdepot)
df.plsdepot = plsreg1(df[, 1:11], df[, 14, drop = FALSE], comps = 3)
data<-df.plsdepot$cor.xyt
data<-as.data.frame(data)
I got this data.frame of the correlation between variables and components
data
# t1 t2 t3
#diesel -0.23513860 -0.38154681 0.439221649
#twodoors 0.71849247 0.45622386 0.055982798
#sportsstyle 0.51909329 -0.02381952 -0.672617464
#wheelbase -0.86843937 0.34114664 -0.254589548
#length -0.75311884 0.62404991 -0.085596033
#width -0.67444970 0.62282146 -0.158675019
#height -0.67228557 -0.14675385 0.317166599
#curbweight -0.59305898 0.73532560 -0.241983833
#enginesize -0.39475651 0.82353941 -0.252270394
#horsepower 0.04843256 0.96637015 -0.148407288
#horse_per_weight 0.50515322 0.81502376 -0.006045151
#symboling 0.64900253 0.23673633 0.346902434
and I managed to plot them as below
library(plsdepot)
df.plsdepot = plsreg1(df[, 1:11], df[, 14, drop = FALSE], comps = 3)
plot(df.plsdepot, comps = c(1, 2))
I had to use pls package instead of plsdepot. I need to get the correlations between variables and components and plot them
Using pls, I managed to plot the correlation between variables and components as below
library(pls)
Y <- as.matrix(df[,14])
X <- as.matrix(df[,1:11])
df.pls <- mvr(Y ~ X, ncomp = 3, method = "oscorespls", scale = T)
plot(df.pls, "correlation")
However, I couldn't find a way to get these values (correlation between variables and components) and convert them to data.frame using pls package.
Any help how can I get these correlation values using pls package will be highly appreciated?

Answer 1

Thanks to Bjørn-Helge Mevik (the maintainer of pls package), for his answer below
==========================================================================
If you look at the corrplot code:
> corrplot
function (object, comps = 1:2, labels, radii = c(sqrt(1/2), 1),
identify = FALSE, type = "p", xlab, ylab, ...) {
nComps <- length(comps)
if (nComps < 2)
stop("At least two components must be selected.")
if (is.matrix(object)) {
cl <- object[, comps, drop = FALSE]
varlab <- colnames(cl)
}
else {
S <- scores(object)[, comps, drop = FALSE]
if (is.null(S))
stop("`", deparse(substitute(object)), "' has no scores.")
cl <- cor(model.matrix(object), S)
varlab <- compnames(object, comps, explvar = TRUE)
}
you will see that it basically does
S <- scores(object)[, comps, drop = FALSE]
cl <- cor(model.matrix(object), S)
to calculate the correlation loadings. Using df.pls in place of object should give you a matrix of correlation loadings.
S <- scores(df.pls)[, comps= 1:2, drop = FALSE]
cl <- cor(model.matrix(df.pls), S)
df.cor <- as.data.frame(cl)
df.cor
# Comp 1 Comp 2
#diesel -0.23513860 -0.38154681
#twodoors 0.71849247 0.45622386
#sportsstyle 0.51909329 -0.02381952
#wheelbase -0.86843937 0.34114664
#length -0.75311884 0.62404991
#width -0.67444970 0.62282146
#height -0.67228557 -0.14675385
#curbweight -0.59305898 0.73532560
#enginesize -0.39475651 0.82353941
#horsepower 0.04843256 0.96637015
#horse_per_weight 0.50515322 0.81502376