rchemo (version 0.1-3)

mbplsr_mbplsda_allsteps: MBPLSR or MBPLSDA analysis steps

Description

Help determine the optimal number of latent variables by cross-validation, perform a permutation test, calculate model parameters and predict new observations, for mbplsr (mbplsr), mbplsrda (mbplsrda), mbplslda (mbplslda) or mbplsqda (mbplsqda) models.

Usage

mbplsr_mbplsda_allsteps(Xlist, Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y, Yscaling = c("none","pareto","sd")[1], weights = NULL,
                   newXlist = NULL, newXnames = NULL,
                   
                   method = c("mbplsr", "mbplsrda","mbplslda","mbplsqda")[1],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[1],
                   nlv, 
                   modeloutput = c("scores","loadings","coef","vip"), 
                   
                   cvmethod = c("kfolds","loo")[1], 
                   nbrep = 30, 
                   seed = 123, 
                   samplingk = NULL, 
                   nfolds = 10, 
                   npermut = 30, 
                   
                   criterion = c("err","rmse")[1], 
                   selection = c("localmin","globalmin","1std")[1],
                   
                   import = c("R","ChemFlow","W4M")[1],
                   outputfilename = NULL)

Value

If step is "nlvtest": table with rmsecv or cross-validated classification error rates. The suggested optimal number of latent variables is indicated by the binary "optimum" variable.

If step is "permutation": table with the dissimilarity between the original and the permutated Y-block, and the rmsecv or cross-validated classification error rates obtained with the permutated Y-block by the model and the given number of latent variables.

If step is "model": tables of scores, loadings, regression coefficients, and vip values, depending of the "modeloutput" parameter.

If step is "prediction": table of predicted scores and predicted classes or values.

Arguments

Xlist

list of training X-data (\(n, p\)).

Xnames

names of the X-matrices

Xscaling

vector of Xlist length. X variable scaling among "none" (mean-centering only), "pareto" (mean-centering and pareto scaling), "sd" (mean-centering and unit variance scaling). If "pareto" or "sd", uncorrected standard deviation is used.

Y

Training Y-data (\(n, q\)) for plsr models, and (\(n, 1\)) for plsrda, plslda or plsqda models.

Yscaling

Y variable scaling among "none" (mean-centering only), "pareto" (mean-centering and pareto scaling), "sd" (mean-centering and unit variance scaling). If "pareto" or "sd", uncorrected standard deviation is used.

weights

Weights (\(n, 1\)) to apply to the training observations. Internally, weights are "normalized" to sum to 1. Default to NULL (weights are set to \(1 / n\)).

newXlist

list of new X-data (\(m, p\)) to consider.

newXnames

names of the newX-matrices

method

method to apply among "plsr", "plsrda","plslda","plsqda"

prior

for plslda or plsqda models : The prior probabilities of the classes. Possible values are "unif" (default; probabilities are set equal for all the classes) or "prop" (probabilities are set equal to the observed proportions of the classes in y).

step

step of the analysis among "nlvtest" (cross-validation to help determine the optimal number of latent variables), "permutation" (permutation test),"model" (model calculation),"prediction" (prediction of newX-data or X-data if any))

nlv

number of latent variables to test if step is "nlvtest"; number of latent variables of the model if step is not "nlvtest".

modeloutput

if step is "model": outputs among "scores", "loadings", "coef" (regression coefficients), "vip" (Variable Importance in Projection; the VIP calculation being based on the proportion of Y-variance explained by the components, as proposed by Mehmood et al (2012, 2020).)

cvmethod

if step is "nlvtest" or "permutation": "kfolds" for k-folds cross-validation, or "loo" for leave-one-out.

nbrep

if step is "nlvtest" and cvmethod is "kfolds": An integer, setting the number of CV repetitions. Default value is 30. Must me set to 1 if cvmethod is "loo"

seed

if step is "nlvtest" and cvmethod is "kfolds", or if step is "permutation: a numeric. Seed used for the repeated resampling

samplingk

A vector of length n. The elements are the values of a qualitative variable used for stratified partition creation. If NULL, the first observation is set in the first fold, the second observation in the second fold, etc...

nfolds

if cvmethod is "kfolds". An integer, setting the number of partitions to create. Default value is 10.

npermut

if step is "permutation": An integer, setting the number of Y-Block with permutated responses to create. Default value is 30.

criterion

if step is "nlvtest" or "permutation" and method is "plsrda", "plslda" or "plsqda": optimisation criterion among "rmse" and "err" (for classification error rate)))

selection

if step is "nlvtest": a character indicating the selection method to use to choose the optimal combination of components, among "localmin","globalmin","1std". If "localmin": the optimal combination corresponds to the first local minimum of the mean CV rmse or error rate. If "globalmin" : the optimal combination corresponds to the minimum mean CV rmse or error rate. If "1std" (one standard error rule) : it corresponds to the first combination after which the mean cross-validated rmse or error rate does not decrease significantly.

import

If "R", X and Y are in the global environment, and the observation names are in rownames. If "ChemFlow", X and Y are tabulated tables (.txt), and the observation names are in the first column. If "W4M", X and Y are tabulated tables (.txt), and the observation names are in the headers of X, and in the first column of Y.

outputfilename

character: If not NULL, name of the tabular file, in which the function outputs have to be written.)

Examples

n <- 50 ; p <- 8
Xtrain <- matrix(rnorm(n * p), ncol = p)
colnames(Xtrain) <- paste0("V",1:p)

ytrain <- sample(c(1, 4, 10), size = n, replace = TRUE)

Xtest <- Xtrain[1:5, ] ; ytest <- ytrain[1:5]

Xtrainlist <- list(Xtrain[,1:3], Xtrain[,4:8])

Xtestlist <- list(Xtest[,1:3], Xtest[,4:8])

nlv <- 5

resnlvtestmbplsrda <- mbplsr_mbplsda_allsteps(Xlist = Xtrainlist, 
                   Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y = ytrain, Yscaling = "none", weights = NULL,
                   newXlist = Xtestlist, newXnames = NULL,
                   
                   method = c("mbplsr", "mbplsrda","mbplslda","mbplsqda")[2],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[1],
                   nlv = 5, 
                   modeloutput = c("scores","loadings","coef","vip"), 
                   
                   cvmethod = c("kfolds","loo")[2], 
                   nbrep = 1, 
                   seed = 123, 
                   samplingk = NULL, 
                   nfolds = 10, 
                   npermut = 5, 
                   
                   criterion = c("err","rmse")[1], 
                   selection = c("localmin","globalmin","1std")[1],
                   
                   outputfilename = NULL)
                   
respermutationmbplsrda <- mbplsr_mbplsda_allsteps(Xlist = Xtrainlist, 
                   Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y = ytrain, Yscaling = "none", weights = NULL,
                   newXlist = Xtestlist, newXnames = NULL,
                   
                   method = c("mbplsr", "mbplsrda","mbplslda","mbplsqda")[2],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[2],
                   nlv = 1, 
                   modeloutput = c("scores","loadings","coef","vip"), 
                   
                   cvmethod = c("kfolds","loo")[2], 
                   nbrep = 1, 
                   seed = 123, 
                   samplingk = NULL, 
                   nfolds = 10, 
                   npermut = 5, 
                   
                   criterion = c("err","rmse")[1], 
                   selection = c("localmin","globalmin","1std")[1],
                   
                   outputfilename = NULL)
                   
plotxy(respermutationmbplsrda, pch=16)
abline (h = respermutationmbplsrda[respermutationmbplsrda[,"permut_dyssimilarity"]==0,"res_permut"])

resmodelmbplsrda <- mbplsr_mbplsda_allsteps(Xlist = Xtrainlist, 
                   Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y = ytrain, Yscaling = "none", weights = NULL,
                   newXlist = Xtestlist, newXnames = NULL,
                   
                   method = c("mbplsr", "mbplsrda","mbplslda","mbplsqda")[2],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[3],
                   nlv = 1, 
                   modeloutput = c("scores","loadings","coef","vip"), 
                   
                   cvmethod = c("kfolds","loo")[2], 
                   nbrep = 1, 
                   seed = 123, 
                   samplingk = NULL, 
                   nfolds = 10, 
                   npermut = 5, 
                   
                   criterion = c("err","rmse")[1], 
                   selection = c("localmin","globalmin","1std")[1],
                   
                   outputfilename = NULL)
                   

resmodelmbplsrda$scores
resmodelmbplsrda$loadings
resmodelmbplsrda$coef
resmodelmbplsrda$vip

respredictionmbplsrda <- mbplsr_mbplsda_allsteps(Xlist = Xtrainlist, 
                   Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y = ytrain, Yscaling = "none", weights = NULL,
                   newXlist = Xtestlist, newXnames = NULL,
                   
                   method = c("mbplsr", "mbplsrda","mbplslda","mbplsqda")[2],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[4],
                   nlv = 1, 
                   modeloutput = c("scores","loadings","coef","vip"), 
                   
                   cvmethod = c("kfolds","loo")[2], 
                   nbrep = 1, 
                   seed = 123, 
                   samplingk = NULL, 
                   nfolds = 10, 
                   npermut = 5, 
                   
                   criterion = c("err","rmse")[1], 
                   selection = c("localmin","globalmin","1std")[1],
                   
                   outputfilename = NULL)