Description Usage Arguments Details Value See Also Examples
Similar to other predict methods, this function returns
predictions from a fitted PCLasso2
object.
1 2 3 4 5 6 7 8 9 |
object |
Fitted |
x |
Matrix of values at which predictions are to be made. The features
(genes/proteins) contained in |
type |
Type of prediction: "link" returns the linear predictors; "response" gives the risk (i.e., exp(link)); "class" returns the binomial outcome with the highest probability; "vars" returns the indices for the nonzero coefficients; "vars.unique" returns unique features (genes/proteins) with nonzero coefficients (If a feature belongs to multiple groups and multiple groups are selected, the feature will be repeatedly selected. Compared with "var", "var.unique" will filter out repeated features.); "groups" returns the groups with at least one nonzero coefficient; "nvars" returns the number of nonzero coefficients; "nvars.unique" returns the number of unique features (genes/proteins) with nonzero coefficients; "ngroups" returns the number of groups with at least one nonzero coefficient; "norm" returns the L2 norm of the coefficients in each group. |
lambda |
Values of the regularization parameter |
... |
Arguments to be passed to |
See predict.grpreg
in the R package grpreg
for details.
The object returned depends on type
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | # load data
data(classData)
data(PCGroups)
x <- classData$Exp
y <- classData$Label
PC.Human <- getPCGroups(Groups = PCGroups, Organism = "Human",
Type = "GeneSymbol")
set.seed(20150122)
idx.train <- sample(nrow(x), round(nrow(x)*2/3))
x.train <- x[idx.train,]
y.train <- y[idx.train]
x.test <- x[-idx.train,]
y.test <- y[-idx.train]
# fit PCLasso2 model
fit.PCLasso2 <- PCLasso2(x = x.train, y = y.train, group = PC.Human,
penalty = "grLasso", family = "binomial")
# predict risk scores of samples in x.test
s <- predict(object = fit.PCLasso2, x = x.test, type="link",
lambda=fit.PCLasso2$fit$lambda)
# predict classes of samples in x.test
s <- predict(object = fit.PCLasso2, x = x.test, type="class",
lambda=fit.PCLasso2$fit$lambda[10])
# Nonzero coefficients
sel.groups <- predict(object = fit.PCLasso2, type="groups",
lambda = fit.PCLasso2$fit$lambda)
sel.ngroups <- predict(object = fit.PCLasso2, type="ngroups",
lambda = fit.PCLasso2$fit$lambda)
sel.vars.unique <- predict(object = fit.PCLasso2, type="vars.unique",
lambda = fit.PCLasso2$fit$lambda)
sel.nvars.unique <- predict(object = fit.PCLasso2, type="nvars.unique",
lambda = fit.PCLasso2$fit$lambda)
sel.vars <- predict(object = fit.PCLasso2, type="vars",
lambda=fit.PCLasso2$fit$lambda)
sel.nvars <- predict(object = fit.PCLasso2, type="nvars",
lambda=fit.PCLasso2$fit$lambda)
# For values of lambda not in the sequence of fitted models,
# linear interpolation is used.
sel.groups <- predict(object = fit.PCLasso2, type="groups",
lambda = c(0.1, 0.05))
sel.ngroups <- predict(object = fit.PCLasso2, type="ngroups",
lambda = c(0.1, 0.05))
sel.vars.unique <- predict(object = fit.PCLasso2, type="vars.unique",
lambda = c(0.1, 0.05))
sel.nvars.unique <- predict(object = fit.PCLasso2, type="nvars.unique",
lambda = c(0.1, 0.05))
sel.vars <- predict(object = fit.PCLasso2, type="vars",
lambda=c(0.1, 0.05))
sel.nvars <- predict(object = fit.PCLasso2, type="nvars",
lambda=c(0.1, 0.05))
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