## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(subsampling)

## -----------------------------------------------------------------------------
set.seed(2)
N <- 2 * 1e4
beta0 <- c(-6, -rep(0.5, 6))
d <- length(beta0) - 1
corr <- 0.5
sigmax <- corr ^ abs(outer(1:d, 1:d, "-"))
X <- MASS::mvrnorm(n = N, mu = rep(0, d), Sigma = sigmax)
Y <- rbinom(N, 1, 1 - 1 / (1 + exp(beta0[1] + X %*% beta0[-1])))
n.plt <- 200
n.ssp <- 600
data <- as.data.frame(cbind(Y, X))
colnames(data) <- c("Y", paste0("V", 1:ncol(X)))
formula <- Y ~ .
print(paste("N:", N))
print(paste("sum(Y):", sum(Y)))

## -----------------------------------------------------------------------------
set.seed(11)

fit_default <- ssp.relogit(
  formula = formula,
  data = data,
  n.plt = n.plt,
  n.ssp = n.ssp,
  criterion = "optL",
  likelihood = "logOddsCorrection"
)

summary(fit_default)

## -----------------------------------------------------------------------------
set.seed(2)

fit_optA <- ssp.relogit(
  formula = formula,
  data = data,
  n.plt = n.plt,
  n.ssp = n.ssp,
  criterion = "optA",
  likelihood = "logOddsCorrection"
)

summary(fit_optA)

## -----------------------------------------------------------------------------
set.seed(9)

fit_uniform <- ssp.relogit(
  formula = formula,
  data = data,
  n.plt = n.plt,
  n.ssp = n.ssp,
  criterion = "uniform",
  likelihood = "logOddsCorrection"
)

fit_uniform$subsample.size.expect
length(fit_uniform$index)
summary(fit_uniform)

## -----------------------------------------------------------------------------
names(fit_default)