plsr_plsda_allsteps: PLSR or PLSDA analysis steps
In rchemo: Dimension Reduction, Regression and Discrimination for Chemometrics
View source: R/plsr_plsda_allsteps.R
plsr_plsda_allsteps R Documentation
PLSR or PLSDA 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 plsr (plskern), plsrda (plsrda), plslda (plslda) or plsqda (plsqda) models.
Usage
plsr_plsda_allsteps(X, Xname = NULL, Xscaling = c("none","pareto","sd")[1],
Y, Yscaling = c("none","pareto","sd")[1], weights = NULL,
newX = NULL, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[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)
Arguments
X
Training X-data (n, p).
Xname
name of the X-matrix
Xscaling
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).
newX
New X-data (m, p) to consider.
newXname
name of the newX-matrix
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.)
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.
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]
resnlvtestplsrda <- plsr_plsda_allsteps(X = Xtrain, Xname = NULL,
Xscaling = c("none","pareto","sd")[1],
Y = ytrain, Yscaling = "none", weights = NULL,
newX = Xtest, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[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)
respermutationplsrda <- plsr_plsda_allsteps(X = Xtrain, Xname = NULL,
Xscaling = c("none","pareto","sd")[1],
Y = ytrain, Yscaling = "none", weights = NULL,
newX = Xtest, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[2],
nlv = 2,
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(respermutationplsrda, pch=16)
abline (h = respermutationplsrda[respermutationplsrda[,"permut_dyssimilarity"]==0,"res_permut"])
resmodelplsrda <- plsr_plsda_allsteps(X = Xtrain, Xname = NULL,
Xscaling = c("none","pareto","sd")[1],
Y = ytrain, Yscaling = "none", weights = NULL,
newX = Xtest, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[3],
nlv = 2,
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)
resmodelplsrda$scores
resmodelplsrda$loadings
resmodelplsrda$coef
resmodelplsrda$vip
respredictionplsrda <- plsr_plsda_allsteps(X = Xtrain, Xname = NULL,
Xscaling = c("none","pareto","sd")[1],
Y = ytrain, Yscaling = "none", weights = NULL,
newX = Xtest, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[4],
nlv = 2,
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.
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View source: R/plsr_plsda_allsteps.R
| plsr_plsda_allsteps | R Documentation |
PLSR or PLSDA 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 plsr (plskern), plsrda (plsrda), plslda (plslda) or plsqda (plsqda) models.
Usage
plsr_plsda_allsteps(X, Xname = NULL, Xscaling = c("none","pareto","sd")[1],
Y, Yscaling = c("none","pareto","sd")[1], weights = NULL,
newX = NULL, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[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)
Arguments
X |
Training X-data ( |
Xname |
name of the X-matrix |
Xscaling |
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 ( |
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 ( |
newX |
New X-data ( |
newXname |
name of the newX-matrix |
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 |
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.) |
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.
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]
resnlvtestplsrda <- plsr_plsda_allsteps(X = Xtrain, Xname = NULL,
Xscaling = c("none","pareto","sd")[1],
Y = ytrain, Yscaling = "none", weights = NULL,
newX = Xtest, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[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)
respermutationplsrda <- plsr_plsda_allsteps(X = Xtrain, Xname = NULL,
Xscaling = c("none","pareto","sd")[1],
Y = ytrain, Yscaling = "none", weights = NULL,
newX = Xtest, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[2],
nlv = 2,
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(respermutationplsrda, pch=16)
abline (h = respermutationplsrda[respermutationplsrda[,"permut_dyssimilarity"]==0,"res_permut"])
resmodelplsrda <- plsr_plsda_allsteps(X = Xtrain, Xname = NULL,
Xscaling = c("none","pareto","sd")[1],
Y = ytrain, Yscaling = "none", weights = NULL,
newX = Xtest, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[3],
nlv = 2,
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)
resmodelplsrda$scores
resmodelplsrda$loadings
resmodelplsrda$coef
resmodelplsrda$vip
respredictionplsrda <- plsr_plsda_allsteps(X = Xtrain, Xname = NULL,
Xscaling = c("none","pareto","sd")[1],
Y = ytrain, Yscaling = "none", weights = NULL,
newX = Xtest, newXname = NULL,
method = c("plsr", "plsrda","plslda","plsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[4],
nlv = 2,
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)
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