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

## ----setup, include = FALSE---------------------------------------------------
library(risq)

## ----hlc_head-----------------------------------------------------------------
head(hlc)

## ----hlc_subset---------------------------------------------------------------
data <- hlc[seq(1, nrow(hlc), 40), ]

## ----risq_object--------------------------------------------------------------
model <- formula(~ age + gender + house_value + household)
weights <- rep(250, nrow(data))
risq_data <- risq(predictor = model, family = "binomial", data = data, weights = weights)
risq_data

## ----indicator_rr-------------------------------------------------------------
rr(risq_data, target = "response_3")

## ----indicator_ri-------------------------------------------------------------
ri(risq_data, target = "response_3")

## ----indicator_cv-------------------------------------------------------------
cv(risq_data, target = "response_3")

## ----partial_by_var-----------------------------------------------------------
ri_by_var(risq_data, target = "response_3", variables = c("age", "marital_status"), type = "unconditional")
ri_by_var(risq_data, target = "response_3", variables = c("age", "marital_status"), type = "conditional")

## ----partial_by_cat-----------------------------------------------------------
ri_by_cat(risq_data, target = "response_3", variables = c("gender", "job"), type = "unconditional")
ri_by_cat(risq_data, target = "response_3", variables = c("gender", "job"), type = "conditional")

## ----bootstrap_object---------------------------------------------------------
bs_ri_resp1 <- bootstrap(risq_data, fun = ri, seed = 0, target = "response_1", iterations = 10)
bs_ri_resp3 <- bootstrap(risq_data, fun = ri, seed = 0, target = "response_3", iterations = 10)

## ----indicator_quantile-------------------------------------------------------
quantile(bs_ri_resp3, probs = c(0.025, 0.975))

## ----indicator_diff_1---------------------------------------------------------
ri_resp1 <- ri(risq_data, target = "response_1", include_se = FALSE)
ri_resp3 <- ri(risq_data, target = "response_3", include_se = FALSE)
ri_resp3$value - ri_resp1$value

## ----indicator_diff_2---------------------------------------------------------
bs_ri_diff31 <- bs_ri_resp3 - bs_ri_resp1
mean(bs_ri_diff31)

## ----indicator_diff_3---------------------------------------------------------
sqrt(var(bs_ri_diff31))
quantile(bs_ri_diff31, probs = c(0.025, 0.975))