add examples to perf.assess

R/perf.assess.R

@@ -231,7 +231,7 @@
 #' 4:e1845.
 #' @keywords regression multivariate
 #' @export
-#' @example ./examples/perf-examples.R
+#' @example ./examples/perf.assess-examples.R
 ## ------------------------------- Generic -------------------------------- ##
 perf.assess <- function(object, ...)
     UseMethod("perf.assess")

examples/perf.assess-examples.R

@@ -0,0 +1,93 @@
+## PLS-DA example
+
+data(liver.toxicity) # rats gex and clinical measurements/treatments
+unique(liver.toxicity$treatment$Treatment.Group) # 16 groups
+length(liver.toxicity$treatment$Treatment.Group) # 64 samples
+
+plsda.res <- plsda(liver.toxicity$gene, liver.toxicity$treatment$Treatment.Group, ncomp = 2)
+
+performance <- perf.assess(plsda.res, 
+                           validation = "Mfold", folds = 3, nrepeat = 10, # to make sure each fold has all classes represented
+                           seed = 12) # for reproducibility, remove for analysis
+
+performance$error.rate$BER
+
+## sPLS-DA example
+
+splsda.res <- splsda(liver.toxicity$gene, liver.toxicity$treatment$Treatment.Group,
+                     keepX = c(25, 25), ncomp = 2)
+
+performance <- perf.assess(splsda.res,
+                           validation = "Mfold", folds = 3, nrepeat = 10, # to make sure each fold has all classes represented
+                           seed = 12)
+
+performance$error.rate$BER # can see slight improvement in error rate over PLS-DA example
+
+## PLS example
+
+ncol(liver.toxicity$clinic) # 10 Y variables as output of PLS model
+
+pls.res <- pls(liver.toxicity$gene, liver.toxicity$clinic, ncomp = 2)
+
+performance <- perf.assess(pls.res, 
+                           validation = "Mfold", folds = 3, nrepeat = 10, # to make sure each fold has all classes represented
+                           seed = 12)
+
+performance$measures$Q2$summary # see Q2 which gives indication of predictive ability for each of the 10 Y outputs
+
+## sPLS example
+
+spls.res <- spls(liver.toxicity$gene, liver.toxicity$clinic, ncomp = 2, keepX = c(50, 50))
+
+performance <- perf.assess(spls.res, 
+                           validation = "Mfold", folds = 3, nrepeat = 10, # to make sure each fold has all classes represented
+                           seed = 12)
+
+performance$measures$Q2$summary # see Q2 which gives indication of predictive ability for each of the 10 Y outputs
+
+
+## block PLS-DA example
+
+data("breast.TCGA")
+mrna <- breast.TCGA$data.train$mrna
+mirna <- breast.TCGA$data.train$mirna
+data <- list(mrna = mrna, mirna = mirna)
+design <- matrix(1, ncol = length(data), nrow = length(data), dimnames = list(names(data), names(data)))
+diag(design) <-  0
+
+block.plsda.res <- block.plsda(X = data, Y = breast.TCGA$data.train$subtype, 
+                               ncomp = 2, design = design)
+
+performance <- perf.assess(block.plsda.res)
+
+performance$error.rate.per.class # error rate per class per distance metric
+
+## block sPLS-DA example
+
+block.splsda.res <- block.splsda(X = data, Y = breast.TCGA$data.train$subtype, 
+                                 ncomp = 2, design = design,
+                                 keepX = list(mrna = c(8,8), mirna = c(8,8)))
+
+performance <- perf.assess(block.splsda.res)
+
+performance$error.rate.per.class
+
+## MINT PLS-DA example
+
+data("stemcells")
+
+mint.plsda.res <- mint.plsda(X = stemcells$gene, Y = stemcells$celltype, ncomp = 3,
+                             study = stemcells$study)
+
+performance <- perf.assess(mint.plsda.res)
+
+performance$global.error$BER # global error per distance metric
+
+## MINT sPLS-DA example
+
+mint.splsda.res <- mint.splsda(X = stemcells$gene, Y = stemcells$celltype, ncomp = 3, 
+                               keepX = c(10, 5, 15), study = stemcells$study)
+
+performance <- perf.assess(mint.splsda.res)
+
+performance$global.error$BER # error slightly higher in this sparse model verses non-sparse