soplsr_soplsda_allsteps: SOPLSR or SOPLSDA analysis steps

In rchemo: Dimension Reduction, Regression and Discrimination for Chemometrics

SOPLSR or SOPLSDA 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 soplsr (soplsr), soplsrda (soplsrda), soplslda (soplslda) or soplsqda (soplsqda) models.

Usage

soplsr_soplsda_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("soplsr", "soplsrda","soplslda","soplsqda")[1],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[1],
                   nlv = c(), 
                   nlvlist = list(),
                   modeloutput = c("scores","loadings","coef","vip"), 
                   
                   cvmethod = c("kfolds","loo")[1], 
                   nbrep = 30, 
                   seed = 123, 
                   samplingk = NULL, 
                   nfolds = 10, 
                   npermut = 30, 
                   
                   optimisation = c("global","sequential")[1],
                   criterion = c("err","rmse")[1], 
                   selection = c("localmin","globalmin","1std")[1],
                   
                   import = c("R","ChemFlow","W4M")[1],
                   outputfilename = NULL)

Arguments

Xlist	list of training X-data (n, p).
Xnames	name 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	if step is not "nlvtest". A vector of same length as the number of blocks defining the number of scores to calculate for each block, or a single number. In this last case, the same number of scores is used for all the blocks.
nlvlist	if step is not "nlvtest". A list of same length as the number of X-blocks. Each component of the list gives the number of PLS components of the corresponding X-block to test.
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.
optimisation	if step is "nlvtest", method for the error optimisation among "global" (the optimal number of latent variables is determined after all the ordred block combination have been computed), or "sequential" (the optimal number of latent variables is determined after each addition of X-block)
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.)

Value

If step is "nlvtest": table with rmsecv or cross-validated classification error rates. The suggested optimal number of latent variable combination 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": list of tables of scores, loadings, regression coefficients, and vip values by X-Block, depending of the "modeloutput" parameter.

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

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

resnlvtestsoplsrda <- soplsr_soplsda_allsteps(Xlist = Xtrainlist, 
                   Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y = ytrain, Yscaling = "none", weights = NULL,
                   newXlist = Xtestlist, newXnames = NULL,
                   
                   method = c("soplsr", "soplsrda","soplslda","soplsqda")[2],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[1],
                   nlvlist = list(1:2, 1:2), 
                   modeloutput = c("scores","loadings","coef","vip"), 
                   
                   cvmethod = c("kfolds","loo")[2], 
                   nbrep = 1, 
                   seed = 123, 
                   samplingk = NULL, 
                   nfolds = 10, 
                   npermut = 5, 
                   
                   optimisation = "global",
                   criterion = c("err","rmse")[1], 
                   selection = c("localmin","globalmin","1std")[1],
                   
                   outputfilename = NULL)
                   
respermutationsoplsrda <- soplsr_soplsda_allsteps(Xlist = Xtrainlist, 
                   Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y = ytrain, Yscaling = "none", weights = NULL,
                   newXlist = Xtestlist, newXnames = NULL,
                   
                   method = c("soplsr", "soplsrda","soplslda","soplsqda")[2],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[2],
                   nlv = c(2,1), 
                   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(respermutationsoplsrda, pch=16)
abline (h = respermutationsoplsrda[respermutationsoplsrda[,"permut_dyssimilarity"]==0,"res_permut"])

resmodelsoplsrda <- soplsr_soplsda_allsteps(Xlist = Xtrainlist, 
                   Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y = ytrain, Yscaling = "none", weights = NULL,
                   newXlist = Xtestlist, newXnames = NULL,
                   
                   method = c("soplsr", "soplsrda","soplslda","soplsqda")[2],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[3],
                   nlv = c(2,1), 
                   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)
                   

resmodelsoplsrda$scores
resmodelsoplsrda$loadings
resmodelsoplsrda$coef
resmodelsoplsrda$vip

respredictionsoplsrda <- soplsr_soplsda_allsteps(Xlist = Xtrainlist, 
                   Xnames = NULL, Xscaling = c("none","pareto","sd")[1], 
                   Y = ytrain, Yscaling = "none", weights = NULL,
                   newXlist = Xtestlist, newXnames = NULL,
                   
                   method = c("soplsr", "soplsrda","soplslda","soplsqda")[2],
                   prior = c("unif", "prop")[1],
                   
                   step = c("nlvtest","permutation","model","prediction")[4],
                   nlv = c(2,1), 
                   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)
                   

rchemo documentation built on Sept. 11, 2024, 8:05 p.m.