mixOmics: tune.splslevel – R documentation

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tune.splslevel

Tuning functions for multilevel sPLS method

Description

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

Usage

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
)

Arguments

`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.

Details

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

Value

cor.value

correlation between latent variables

Author(s)

Kim-Anh Lê Cao, Benoit Gautier, Francois Bartolo, Florian Rohart, Al J Abadi

References

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

Examples

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)

mixOmics

Omics Data Integration Project

v6.14.1

GPL (>= 2)

Authors

Kim-Anh Le Cao [aut, cre], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al Abadi [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb]

Initial release

2021-04-11