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The goal of HCTR is to create a new searching scheme for the regularization parameter in penalized regression, such as Lasso, adaptive Lasso, SCAD, and MCP.
This is a basic example which shows you how to (1) estimate false null hypothesis proportion, and (2) create a new tuning region for the regularization parameter.
## basic example code
library('HCTR')
# 1. Estimate proportion
set.seed(10)
X <- matrix(runif(n = 10000, min = 0, max = 1), nrow = 100)
result <- bounding.seq(p.value = X)
Y <- matrix(runif(n = 100, min = 0, max = 1), nrow = 100)
test <- est.prop(p.value = Y, cn = result)
# 2. Estimate a new tuning region
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))These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.