lasso.predict: Prediction with least absolute shrinkage and selection...

In LXQin/precision: PaiREd miCrorna sImulation on Study desIgn for mOlecular classificatioN

Description

Predict from a least absolute shrinkage and selection operator fit.

Usage

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

Arguments

lasso.intcv.model	a LASSO classifier built with lasso.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

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Mod- els via Coordinate Descent, http://www.stanford.edu/~hastie/Papers/glmnet.pdf Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010

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]

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

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

LXQin/precision documentation built on May 11, 2019, 6:24 p.m.