tune.splslevel: Tuning functions for multilevel sPLS method

In mixOmics: Omics Data Integration Project

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. NAs are allowed.
Y	if(method = 'spls') numeric vector or matrix of continuous responses (for multi-response models) NAs 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

See Also

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

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)

Example output

Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2

Loaded mixOmics 6.3.2

Thank you for using mixOmics!

How to apply our methods: http://www.mixOmics.org for some examples.
Questions or comments: email us at mixomics[at]math.univ-toulouse.fr  
Any bugs? https://bitbucket.org/klecao/package-mixomics/issues
Cite us:  citation('mixOmics')
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 
 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 
 4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4 
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
           varY 1    varY 2    varY 5
varX 5  0.9637933 0.9603998 0.9735052
varX 10 0.9698826 0.9716134 0.9765976
varX 15 0.9684396 0.9698546 0.9794716

mixOmics documentation built on April 15, 2021, 6:01 p.m.