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

## ----setup--------------------------------------------------------------------
library(fgls)

## ----example------------------------------------------------------------------
het <- simulate_hetero(200, skedastic = "log2", seed = 10)
fit <- lasso_fgls(y ~ x, data = het, seed = 1)
summary(fit)

## ----skedastic----------------------------------------------------------------
fit$skedastic$coefficients
plot(fit, which = 4)

## ----z------------------------------------------------------------------------
het2 <- simulate_hetero(200, skedastic = "x2", n_extra = 3, seed = 11)
fit2 <- lasso_fgls(y ~ x, data = het2, z = ~ x + I(x^2) + w1 + w2 + w3, seed = 1)
fit2$skedastic$selected

## ----homo---------------------------------------------------------------------
hom <- simulate_hetero(200, skedastic = "homoskedastic", seed = 12)
fit_h <- lasso_fgls(y ~ x, data = hom, seed = 1)
fit_h$skedastic$selected
all.equal(coef(fit_h), coef(lm(y ~ x, data = hom)))

## ----alternatives-------------------------------------------------------------
sapply(
  list(
    OLS = lasso_fgls(y ~ x, data = het, hetero = "none"),
    `WLS-S1` = lasso_fgls(y ~ x, data = het, hetero = "wls_s1", z = "x"),
    Lasso = fit,
    GLS = lasso_fgls(y ~ x, data = het, hetero = "known", variance = attr(het, "info")$v)
  ),
  function(f) sqrt(vcov(f)[2, 2])
)

## ----weights------------------------------------------------------------------
weight_diagnostics(fit)