Output of selected variables
This function outputs the selected variables on each component for the sparse versions of the approaches (was also generalised to the non sparse versions for our internal functions).
selectVar(...) ## S3 method for class 'mixo_pls' selectVar(object, comp = 1, block = NULL, ...) ## S3 method for class 'mixo_spls' selectVar(object, comp = 1, block = NULL, ...) ## S3 method for class 'pca' selectVar(object, comp = 1, block = NULL, ...) ## S3 method for class 'sgcca' selectVar(object, comp = 1, block = NULL, ...) ## S3 method for class 'rgcca' selectVar(object, comp = 1, block = NULL, ...)
... |
other arguments. |
object |
object of class inherited from |
comp |
integer value indicating the component of interest. |
block |
for an object of class |
selectVar
provides the variables selected on a given component. \
outputs the name of the selected variables (provided that the input data have colnames) ranked in decreasing order of importance.
outputs the loading value for each selected variable, the loadings are ranked according to their absolute value.
These functions are only implemented for the sparse versions.
none
Kim-Anh Lê Cao, Florian Rohart, Al J Abadi
data(liver.toxicity) X = liver.toxicity$gene Y = liver.toxicity$clinic # example with sPCA # ------------------ liver.spca <- spca(X, ncomp = 1, keepX = 10) selectVar(liver.spca, comp = 1)$name selectVar(liver.spca, comp = 1)$value ## Not run: #example with sIPCA # ----------------- liver.sipca <- sipca(X, ncomp = 3, keepX = rep(10, 3)) selectVar(liver.sipca, comp = 1) # example with sPLS # ----------------- liver.spls = spls(X, Y, ncomp = 2, keepX = c(20, 40),keepY = c(5, 5)) selectVar(liver.spls, comp = 2) # example with sPLS-DA data(srbct) # an example with no gene name in the data X = srbct$gene Y = srbct$class srbct.splsda = splsda(X, Y, ncomp = 2, keepX = c(5, 10)) select = selectVar(srbct.splsda, comp = 2) select # this is a very specific case where a data set has no rownames. srbct$gene.name[substr(select$select, 2,5),] # example with sGCCA # ----------------- data(nutrimouse) # ! need to unmap the Y factor Y = unmap(nutrimouse$diet) data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid,Y) # in this design, gene expression and lipids are connected to the diet factor # and gene expression and lipids are also connected design = matrix(c(0,1,1, 1,0,1, 1,1,0), ncol = 3, nrow = 3, byrow = TRUE) #note: the penalty parameters need to be tuned wrap.result.sgcca = wrapper.sgcca(X = data, design = design, penalty = c(.3,.3, 1), ncomp = 2, scheme = "horst") #variables selected and loadings values on component 1 for the two blocs selectVar(wrap.result.sgcca, comp = 1, block = c(1,2)) #variables selected on component 1 for each block selectVar(wrap.result.sgcca, comp = 1, block = c(1,2))$'gene'$name selectVar(wrap.result.sgcca, comp = 1, block = c(1,2))$'lipid'$name #variables selected on component 2 for each block selectVar(wrap.result.sgcca, comp = 2, block = c(1,2))$'gene'$name selectVar(wrap.result.sgcca, comp = 2, block = c(1,2))$'lipid'$name # loading value of the variables selected on the first block selectVar(wrap.result.sgcca, comp = 1, block = 1)$'gene'$value ## End(Not run)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.