ranfor.predict: Prediction with random forest classifier

In yilinwu123/precision1: PaiREd miCrorna sImulation on Study desIgn for mOlecular classificatioN

Prediction with random forest classifier

Description

Predict from a ranfom forest classifier fit.

Usage

ranfor.predict(ranfor.intcv.model, pred.obj, pred.obj.group.id)

Arguments

ranfor.intcv.model	a ranfom forest classifier built with ranfor.intcv.
pred.obj	dataset to have its sample group predicted. The dataset must have rows as probes and columns as samples. It must have an equal number of probes as the dataset being trained.
pred.obj.group.id	a vector of sample-group labels for each sample of the dataset to be predicted. It must have an equal length to the number of samples as pred.obj.

Value

a list of 3 elements:

pred	predicted sample group for each sample
mc	a predicted misclassification error rate (external validation)
prob	predicted probability for each sample

References

https://cran.r-project.org/web/packages/e1071/index.html

Examples

set.seed(101)
biological.effect <- estimate.biological.effect(uhdata = uhdata.pl)
ctrl.genes <- unique(rownames(uhdata.pl))[grep("NC", unique(rownames(uhdata.pl)))]
biological.effect.nc <- biological.effect[!rownames(biological.effect) %in% ctrl.genes, ]
group.id <- substr(colnames(biological.effect.nc), 7, 7)

biological.effect.train.ind <- colnames(biological.effect.nc)[c(sample(which(group.id == "E"), size = 64),
                                          sample(which(group.id == "V"), size = 64))]
biological.effect.test.ind <- colnames(biological.effect.nc)[!colnames(biological.effect.nc) %in% biological.effect.train.ind]

biological.effect.nc.tr <- biological.effect.nc[, biological.effect.train.ind]
biological.effect.nc.te <- biological.effect.nc[, biological.effect.test.ind]

ranfor.int <- ranfor.intcv(X = biological.effect.nc.tr,
                         y = substr(colnames(biological.effect.nc.tr), 7, 7),
                         kfold = 5, seed = 1)

ranfor.pred <- ranfor.predict(ranfor.intcv.model = ranfor.int,
                            pred.obj = biological.effect.nc.te,
                            pred.obj.group.id = substr(colnames(biological.effect.nc.te), 7, 7))
ranfor.int$mc
ranfor.pred$mc

yilinwu123/precision1 documentation built on June 28, 2022, 2:53 a.m.