Prediction with support vector machine classifier

Predict from a support vector machine classifier fit.

Usage

# S3 method for predict
svm(svm.intcv.model, pred.obj, pred.obj.group.id)

Arguments

svm.intcv.model

a support vector machine classifier built with svm.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]

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

svm.pred <- svm.predict(svm.intcv.model = svm.int,
                        pred.obj = biological.effect.nc.te,
                        pred.obj.group.id = substr(colnames(biological.effect.nc.te), 7, 7))
svm.int$mc
#> [1] 0.1326154
svm.pred$mc
#> [1] 0.078125