Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval = F----------------------------------------------------------------
# # install.packages("devtools")
# devtools::install_github("umich-cphds/miselect", build_opts = c())
## -----------------------------------------------------------------------------
library(miselect)
colMeans(is.na(miselect.df))
## -----------------------------------------------------------------------------
library(mice)
set.seed(48109)
# Using the mice defaults for sake of example only.
mids <- mice(miselect.df, m = 5, printFlag = FALSE)
## -----------------------------------------------------------------------------
# Generate list of completed data.frames
dfs <- lapply(1:5, function(i) complete(mids, action = i))
# Generate list of imputed design matrices and imputed responses
x <- list()
y <- list()
for (i in 1:5) {
x[[i]] <- as.matrix(dfs[[i]][, paste0("X", 1:20)])
y[[i]] <- dfs[[i]]$Y
}
## -----------------------------------------------------------------------------
# Calculate observational weights
weights <- 1 - rowMeans(is.na(miselect.df))
pf <- rep(1, 20)
adWeight <- rep(1, 20)
alpha <- c(.5 , 1)
# Since 'Y' is a binary variable, we use 'family = "binomial"'
fit <- cv.saenet(x, y, pf, adWeight, weights, family = "binomial",
alpha = alpha, nfolds = 5)
# By default 'coef' returns the betas for (lambda.min , alpha.min)
coef(fit)
## -----------------------------------------------------------------------------
coef(fit, lambda = fit$lambda.1se, alpha = fit$alpha.1se)
## -----------------------------------------------------------------------------
adWeight <- 1 / (abs(coef(fit)[-1]) + 1 / nrow(miselect.df))
afit <- cv.saenet(x, y, pf, adWeight, weights, family = "binomial",
alpha = alpha, nfolds = 5)
coef(afit)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.