tune.splslevel: Tuning functions for multilevel sPLS method
In mixOmics: Omics Data Integration Project

Description Usage Arguments Details Value Author(s) References See Also Examples

For a multilevel spls analysis, the tuning criterion is based on the maximisation of the correlation between the components from both data sets

tune.splslevel(X, Y, multilevel, ncomp = NULL,
mode = "regression",
test.keepX = rep(ncol(X), ncomp),
test.keepY = rep(ncol(Y), ncomp),
already.tested.X = NULL,
already.tested.Y = NULL)

`X`	numeric matrix of predictors. `NA`s are allowed.
`Y`	`if(method = 'spls')` numeric vector or matrix of continuous responses (for multi-response models) `NA`s are allowed.
`multilevel`	Design matrix for multilevel analysis (for repeated measurements) that indicates the repeated measures on each individual, i.e. the individuals ID. See Details.
`ncomp`	the number of components to include in the model.
`mode`	character string. What type of algorithm to use, (partially) matching one of `"regression"`, `"canonical"`, `"invariant"` or `"classic"`.
`test.keepX`	numeric vector for the different number of variables to test from the X data set
`test.keepY`	numeric vector for the different number of variables to test from the Y data set
`already.tested.X`	Optional, if `ncomp > 1` A numeric vector indicating the number of variables to select from the X data set on the firsts components.
`already.tested.Y`	Optional, if `ncomp > 1` A numeric vector indicating the number of variables to select from the Y data set on the firsts components.

For a multilevel spls analysis, the tuning criterion is based on the maximisation of the correlation between the components from both data sets

cor.value

correlation between latent variables

Kim-Anh Lê Cao, Benoit Gautier, Francois Bartolo, Florian Rohart.

mixOmics article:

Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

splsda, predict.splsda and http://www.mixOmics.org for more details.

data(liver.toxicity)
# note: we made up those data, pretending they are repeated measurements
repeat.indiv <- c(1, 2, 1, 2, 1, 2, 1, 2, 3, 3, 4, 3, 4, 3, 4, 4, 5, 6, 5, 5,
6, 5, 6, 7, 7, 8, 6, 7, 8, 7, 8, 8, 9, 10, 9, 10, 11, 9, 9,
10, 11, 12, 12, 10, 11, 12, 11, 12, 13, 14, 13, 14, 13, 14,
13, 14, 15, 16, 15, 16, 15, 16, 15, 16)
summary(as.factor(repeat.indiv)) # 16 rats, 4 measurements each

# this is a spls (unsupervised analysis) so no need to mention any factor in design
# we only perform a one level variation split
design <- data.frame(sample = repeat.indiv)

tune.splslevel(X = liver.toxicity$gene,
Y=liver.toxicity$clinic,
multilevel = design,
test.keepX = c(5,10,15),
test.keepY = c(1,2,5),
ncomp = 1)