## ----style, eval=TRUE, echo=FALSE, results="asis"------------------------
BiocStyle::latex()

## ----install, eval = FALSE-----------------------------------------------
## source("http://bioconductor.org/biocLite.R")
## biocLite("OmicsMarkeR")

## ----datagen-------------------------------------------------------------
library("OmicsMarkeR")
set.seed(123)
dat.discr <- create.discr.matrix(
    create.corr.matrix(
        create.random.matrix(nvar = 50, 
                             nsamp = 100, 
                             st.dev = 1, 
                             perturb = 0.2)),
    D = 10
)

## ----fs.stability--------------------------------------------------------
vars <- dat.discr$discr.mat
groups <- dat.discr$classes
fits <- fs.stability(vars, 
                    groups, 
                    method = c("plsda", "rf"), 
                    f = 10, 
                    k = 3, 
                    k.folds = 10, 
                    verbose = 'none')

## ----performance---------------------------------------------------------
performance.metrics(fits)
fits$RPT

## ----feature.table-------------------------------------------------------
feature.table(fits, "plsda")

## ----predictClasses, eval=FALSE------------------------------------------
## 
## # create some 'new' data
## newdata <- create.discr.matrix(
##     create.corr.matrix(
##         create.random.matrix(nvar = 50,
##                              nsamp = 100,
##                              st.dev = 1,
##                              perturb = 0.2)),
##     D = 10
## )$discr.mat
## 
## # original data combined to a data.frame
## orig.df <- data.frame(vars, groups)
## 
## # see what the PLSDA predicts for the new data
## # NOTE, newdata does not require a .classes column
## predictNewClasses(fits, "plsda", orig.df, newdata)

## ----ensemble, eval=FALSE------------------------------------------------
## fits <- fs.ensembl.stability(vars,
##                             groups,
##                             method = c("plsda", "rf"),
##                             f = 10,
##                             k = 3,
##                             k.folds = 10,
##                             verbose = 'none')

## ----aggregation---------------------------------------------------------
# test data
ranks <- replicate(5, sample(seq(50), 50))
row.names(ranks) <- paste0("V", seq(50))

head(aggregation(ranks, "CLA"))

## ----grid, eval=FALSE----------------------------------------------------
## # requires data.frame of variables and classes
## plsda <- denovo.grid(orig.df, "plsda", 3)
## rf <- denovo.grid(orig.df, "rf", 5)
## 
## # create grid list
## # Make sure to assign appropriate model names
## grid <- list(plsda=plsda, rf=rf)
## 
## # pass to fs.stability or fs.ensemble.stability
## fits <- fs.stability(vars,
##                     groups,
##                     method = c("plsda", "rf"),
##                     f = 10,
##                     k = 3,
##                     k.folds = 10,
##                     verbose = 'none',
##                     grid = grid)
## 

## ----metabs--------------------------------------------------------------
metabs <- paste("Metabolite", seq(20), sep="_")

## ----samples-------------------------------------------------------------
set.seed(13)
run1 <- sample(metabs, 10)
run2 <- sample(metabs, 10)

## ----jaccard-------------------------------------------------------------
jaccard(run1, run2)

## ----kuncheva------------------------------------------------------------
# In this case, 20 original variables
kuncheva(run1, run2, 20)

## ----repeat.metabs-------------------------------------------------------
set.seed(21)
# matrix of Metabolites identified (e.g. 5 trials)
features <- replicate(5, sample(metabs, 10))

## ----pairwise.stability--------------------------------------------------
pairwise.stability(features, "sorensen")

## ----model.stability-----------------------------------------------------
set.seed(999)
plsda <- 
    replicate(5, paste("Metabolite", sample(metabs, 10), sep="_"))
rf <-
    replicate(5, paste("Metabolite", sample(metabs, 10), sep="_"))

features <- list(plsda=plsda, rf=rf)

# nc may be omitted unless using kuncheva
pairwise.model.stability(features, "kuncheva", nc=20)

## ----permutations, eval=FALSE--------------------------------------------
## # permuate class
## perm.class(fits, vars, groups, "rf", k.folds=5,
##            metric="Accuracy", nperm=10)
## 
## 
## # permute variables/features
## perm.features(fits, vars, groups, "rf",
##         sig.level = .05, nperm = 10)

## ----doMC, eval=FALSE----------------------------------------------------
## library(doMC)
## 
## n <- detectCores()
## registerDoMC(n)

## ----SNOW, eval=FALSE----------------------------------------------------
## library(parallel)
## library(doSNOW)
## 
## # get number of cores
## n <- detectCores()
## 
## # make clusters
## cl <- makeCluster(n)
## 
## # register backend
## registerDoSNOW(cl)

## ----sessionInfo---------------------------------------------------------
sessionInfo()