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

## ----eval=FALSE---------------------------------------------------------------
# data("mortgage")

## -----------------------------------------------------------------------------
fm <- deny ~ black + p_irat + hse_inc + ccred + mcred + pubrec + ltv_med + ltv_high + denpmi + selfemp + single + hischl

## ----eval=FALSE---------------------------------------------------------------
# test <- spe(fm = fm, data = mortgage, var = "black", method = "logit",
#             us = c(2:98)/100, b = 200, bc = TRUE)

## ----eval=FALSE---------------------------------------------------------------
# plot(x = test, ylim = c(0, 0.25), ylab = "Change in Probability",
#      main = "APE and SPE of Being Black on the prob of Mortgage Denial",
#      sub = "Logit Model")

## ----eval=FALSE---------------------------------------------------------------
# t <- c("deny", "p_irat", "black", "hse_inc", "ccred", "mcred", "pubrec",
#        "denpmi", "selfemp", "single", "hischl", "ltv_med", "ltv_high")

## ----eval=FALSE---------------------------------------------------------------
# CA <- ca(fm = fm, data = mortgage, var = "black", method = "logit",
#          cl = "both", t = t, b = 200, bc = TRUE)

## ----eval=FALSE---------------------------------------------------------------
# summary(CA)

## ----eval=FALSE---------------------------------------------------------------
# CAdiff <- ca(fm = fm, data = mortgage, var = "black", t = t,
#              method = "logit", cl = "diff", b = 200, bc = TRUE)
# # Tabulate the results
# summary(CAdiff)

## ----eval=FALSE---------------------------------------------------------------
# data(wage2015)

## ----eval=FALSE---------------------------------------------------------------
# <<eval=FALSE>>=
# fmla1 <- lnw ~ female* (widowed + divorced + separated + nevermarried +
#                         exp1 + exp2 + exp3 + exp4 + educ + occ2 + ind2 +
#                         mw + so + we)

## ----eval=FALSE---------------------------------------------------------------
# set <- subpop(fm = fmla0, data = wage2015, var = "female",
#               samp_weight = wage2015$weight, boot_type = "weighted",
#               b = 500, subgroup = wage2015[,"female"]==1, u = 0.05)
# 
# plot(set, varx = wage2015$exp1, vary = wage2015$lnw,
#      main = "Projections of Exp-lnw", sub = "OLS", xlab = "Exp",
#      ylab = "Log Wages")
# 
# plot(set, varx = wage2015$exp1, vary =wage2015$ms,
#      main = "Projections of Exp-MS", sub = "OLS", xlab = "Exp",
#      ylab = "Marital Status")