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
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X 
numeric matrix of predictors. 
Y 

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 
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 
already.tested.Y 
Optional, if 
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 
KimAnh Lê Cao, Benoit Gautier, Francois Bartolo, Florian Rohart, Al J Abadi
mixOmics article: Rohart F, Gautier B, Singh A, Lê Cao KA. 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  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)

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.univtoulouse.fr
Any bugs? https://bitbucket.org/klecao/packagemixomics/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
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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
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