R: Make predictions from a cross-validated PCLasso model

predict.cv.PCLasso {PCLassoReg}

R Documentation

Make predictions from a cross-validated PCLasso model

Description

Similar to other predict methods, this function returns predictions from a fitted cv.PCLasso object, using the optimal value chosen for lambda.

Usage

## S3 method for class 'cv.PCLasso'
predict(
  object,
  x = NULL,
  type = c("link", "response", "survival", "median", "norm", "coefficients", "vars",
    "nvars", "vars.unique", "nvars.unique", "groups", "ngroups"),
  lambda,
  ...
)

Arguments

`object`	Fitted `cv.PCLasso` model object.
`x`	Matrix of values at which predictions are to be made. The features (genes/proteins) contained in `x` should be consistent with those contained in `x` in the `cv.PCLasso` function. Not used for type="coefficients" or for some of the type settings in `predict`.
`type`	Type of prediction: "link" returns the linear predictors; "response" gives the risk (i.e., exp(link)); "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" returens 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."survival" returns the estimated survival function; "median" estimates median survival times.
`lambda`	Values of the regularization parameter `lambda` at which predictions are requested. For values of `lambda` not in the sequence of fitted models, linear interpolation is used.
`...`	Arguments to be passed to `predict.cv.grpsurv` in the R package `grpreg`.

Value

The object returned depends on type.

Examples

# load data
data(survivalData)
data(PCGroups)

x <- survivalData$Exp
y <- survivalData$survData
PC.Human <- getPCGroups(Groups = PCGroups, Organism = "Human",
Type = "EntrezID")

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 cv.PCLasso model
cv.fit1 <- cv.PCLasso(x = x.train,
                      y = y.train,
                      group = PC.Human,
                      nfolds = 5)

# predict risk scores of samples in x.test
s <- predict(object = cv.fit1, x = x.test, type="link",
             lambda=cv.fit1$cv.fit$lambda.min)

# Nonzero coefficients
sel.groups <- predict(object = cv.fit1, type="groups",
                      lambda = cv.fit1$cv.fit$lambda.min)
sel.ngroups <- predict(object = cv.fit1, type="ngroups",
                       lambda = cv.fit1$cv.fit$lambda.min)
sel.vars.unique <- predict(object = cv.fit1, type="vars.unique",
                           lambda = cv.fit1$cv.fit$lambda.min)
sel.nvars.unique <- predict(object = cv.fit1, type="nvars.unique",
                            lambda = cv.fit1$cv.fit$lambda.min)
sel.vars <- predict(object = cv.fit1, type="vars",
                    lambda=cv.fit1$cv.fit$lambda.min)
sel.nvars <- predict(object = cv.fit1, type="nvars",
                     lambda=cv.fit1$cv.fit$lambda.min)