## ----style, eval=TRUE, echo=FALSE, results="asis"------------------------
BiocStyle::latex2()

## ----env, include=FALSE, echo=FALSE, cache=FALSE-------------------------
library("knitr")
opts_chunk$set(fig.show = 'hold',
               prompt = FALSE,
               eval = TRUE,
               stop_on_error = 1L)
               ## comment = NA)

knitr::opts_chunk$set(crop = NULL)

options(replace.assign = TRUE)

suppressPackageStartupMessages(library("MSnbase"))
suppressWarnings(suppressPackageStartupMessages(library("pRoloc")))
suppressPackageStartupMessages(library("pRolocdata"))
## suppressPackageStartupMessages(library("class"))
suppressPackageStartupMessages(library("xtable"))

## ----pRolocdata----------------------------------------------------------
library("pRolocdata")
data(tan2009r1)
tan2009r1

## ----svmParamOptim, cache = TRUE, warning = FALSE, message = FALSE-------
params <- svmOptimisation(tan2009r1, times = 10,
                          xval = 5, verbose = FALSE)
params

## ----svmRes, warning=FALSE, tidy=FALSE, eval=TRUE------------------------
tan2009r1 <- svmClassification(tan2009r1, params)
tan2009r1

## ----weigths, eval=FALSE-------------------------------------------------
## w <- table(fData(markerMSnSet(dunkley2006))$markers)
## wpar <- svmOptimisation(dunkley2006, class.weights = w)
## wres <- svmClassification(dunkley2006, wpar, class.weights = w)

## ----getmlfunction, echo=FALSE-------------------------------------------

## Add chi^2.
tab <- data.frame('parameter optimisation' =
                      grep("Optimisation",
                           ls("package:pRoloc"), value = TRUE), 
                  'classification' =
                      grep("Classification",
                           ls("package:pRoloc"), value = TRUE))

tab$algorithm <- c("nearest neighbour",
                   "nearest neighbour transfer learning",
                   "support vector machine",
                   "naive bayes",
                   "neural networks",
                   "PerTurbo",
                   "partial least square", 
                   "random forest",
                   "support vector machine")

tab$package <- c("class", "pRoloc", "kernlab", "e1071",
                 "nnet", "pRoloc", "caret",
                 "randomForest", "e1071")

colnames(tab)[1] <- c("parameter optimisation")

## ----comptab, results='asis', echo=FALSE---------------------------------
xt <- xtable(tab, label = "tab:algo",
             caption = "Supervised ML algorithm available in \\Biocpkg{pRoloc}.")
print(xt, include.rownames = FALSE, size = "small")

## ----sessioninfo, results='asis', echo=FALSE-----------------------------
toLatex(sessionInfo())