## ----style, echo = FALSE, results = 'asis'------------------------------------ BiocStyle::markdown() ## ----env, include=FALSE, echo=FALSE, cache=FALSE------------------------------ library("knitr") opts_chunk$set(stop_on_error = 1L) suppressPackageStartupMessages(library("MSnbase")) suppressWarnings(suppressPackageStartupMessages(library("pRoloc"))) suppressPackageStartupMessages(library("pRolocdata")) ## ----pRolocdata--------------------------------------------------------------- library("pRolocdata") data(tan2009r1) tan2009r1 ## ----svmParamOptim, cache = TRUE, warning = FALSE, message = FALSE------------ params <- svmOptimisation(tan2009r1, times = 10, xval = 5, verbose = FALSE) params ## ----svmRes, warning=FALSE, tidy=FALSE, eval=TRUE----------------------------- tan2009r1 <- svmClassification(tan2009r1, params) tan2009r1 ## ----weigths, eval=FALSE------------------------------------------------------ # w <- table(fData(markerMSnSet(dunkley2006))$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, echo=FALSE------------------------------------------------------ kable(tab) ## ----sessioninfo, echo=FALSE-------------------------------------------------- sessionInfo()