Nothing
## ----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()
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