## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  fig.align = "center",
  fig.height = 4,
  fig.width = 6,
  collapse = TRUE,
  comment = "#>"
)

## ----results='hide'-----------------------------------------------------------
library("oddsratio")

fit_gam <- mgcv::gam(y ~ s(x0) + s(I(x1^2)) + s(x2) + offset(x3) + x4,
  data = data_gam
)

## -----------------------------------------------------------------------------
or_gam(
  data = data_gam, model = fit_gam, pred = "x2",
  values = c(0.099, 0.198)
)

## -----------------------------------------------------------------------------
or_gam(
  data = data_gam, model = fit_gam,
  pred = "x4", values = c("A", "B")
)

## -----------------------------------------------------------------------------
or_gam(
  data = data_gam, model = fit_gam, pred = "x2",
  percentage = 20, slice = TRUE
)

## -----------------------------------------------------------------------------
library(ggplot2)
plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'") +
  theme_minimal()

## -----------------------------------------------------------------------------
plot_object <- plot_gam(fit_gam, pred = "x2", title = "Predictor 'x2'")
or_object <- or_gam(
  data = data_gam, model = fit_gam,
  pred = "x2", values = c(0.099, 0.198)
)

plot <- insert_or(plot_object, or_object,
  or_yloc = 3,
  values_xloc = 0.05, arrow_length = 0.02,
  arrow_col = "red"
)
plot +
  theme_minimal()

## -----------------------------------------------------------------------------
or_object2 <- or_gam(
  data = data_gam, model = fit_gam,
  pred = "x2", values = c(0.4, 0.6)
)

insert_or(plot, or_object2,
  or_yloc = 2.1, values_yloc = 2,
  line_col = "green4", text_col = "black",
  rect_col = "green4", rect_alpha = 0.2,
  line_alpha = 1, line_type = "dashed",
  arrow_xloc_r = 0.01, arrow_xloc_l = -0.01,
  arrow_length = 0.02, rect = TRUE
) +
  theme_minimal()

## -----------------------------------------------------------------------------
fit_glm <- glm(admit ~ gre + gpa + rank, data = data_glm, family = "binomial")

## -----------------------------------------------------------------------------
or_glm(data = data_glm, model = fit_glm, incr = list(gre = 380, gpa = 5))

## -----------------------------------------------------------------------------
or_glm(
  data = data_glm, model = fit_glm,
  incr = list(gre = 380, gpa = 5), ci = 0.70
)