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Prediction with least absolute shrinkage and selection operator classifier

lasso.predict.Rd

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$mc
#> [1] 0.1484375
lasso.pred$mc
#> [1] 0.15625