HCTR (version 0.1.1)

final.selection: Final Selection

Description

Returns the index of final selected variables in the final chosen model.

Usage

final.selection(cv.fit, pihat, p, cov.num = 0)

Arguments

cv.fit

An object of either class "cv.glmnet" from glmnet::cv.glmnet() or class "cv.ncvreg" from ncvreg::cv.ncvreg(), which is a list generated by a cross-validation fit.

pihat

eatimated proprtion from HCTR::est.prop().

p

Total number of variables, except for covariates.

cov.num

Number of covariates in model, default is 0. Covariate matrix, W, is assumed on the left side of variable matrix, X. The column index of covariates are before those of variables.

Value

A sequence of index of final selected variables in the final chosen model.

Examples

# NOT RUN {
set.seed(10)
X <- matrix(rnorm(20000), nrow = 100)
beta <- rep(0, 200)
beta[1:100] <- 5
Y <- MASS::mvrnorm(n = 1, mu = X%*%beta, Sigma = diag(100))
fit <- glmnet::cv.glmnet(x = X, y = Y)
pihat <- 0.01
result <- est.lambda(cv.fit = fit, pihat = pihat, p = ncol(X))
lambda.seq <- seq(from = result$lambda.min, to = result$lambda.max, length.out = 100)
# Note: The lambda sequences in glmnet and ncvreg are diffrent.
fit2 <- glmnet::cv.glmnet(x = X, y = Y, lambda = lambda.seq)
result2 <- final.selection(cv.fit = fit2, pihat = 0.01, p = ncol(X))
# }