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

## ----eval=FALSE---------------------------------------------------------------
# install.packages("finalfit")

## -----------------------------------------------------------------------------
library(finalfit)
explanatory = c("age.factor", "extent.factor", "perfor.factor")
dependent = 'mort_5yr'

colon_s %>%
  finalfit_newdata(explanatory = explanatory, newdata = list(
    c("<40 years",  "Submucosa", "No"),
    c("<40 years", "Submucosa", "Yes"),
    c("<40 years", "Adjacent structures", "No"),
    c("<40 years", "Adjacent structures", "Yes") )) -> newdata
newdata

## -----------------------------------------------------------------------------
library(dplyr)
colon_s %>% 
	select(-hospital) %>% 
	ff_expand(age.factor, sex.factor)

## -----------------------------------------------------------------------------
colon_s %>% 
  glmmulti(dependent, explanatory) %>% 
  boot_predict(newdata, 
    estimate_name = "Predicted probability of death",
    R=100, boot_compare = FALSE,
    digits = c(2,3))

## ----eval=FALSE---------------------------------------------------------------
# knitr::kable(table, row.names = FALSE, align = c("l", "l", "l", "r"))

## -----------------------------------------------------------------------------
colon_s %>% 
  glmmulti(dependent, explanatory) %>% 
  boot_predict(newdata, 
    estimate_name = "Predicted probability of death",
    #compare_name = "Absolute risk difference",
    R=100, digits = c(2,3))

## ----eval=FALSE---------------------------------------------------------------
# library(finalfit)
# library(ggplot2)
# theme_set(theme_bw())
# 
# explanatory = c("nodes", "extent.factor", "perfor.factor")
# dependent = 'mort_5yr'
# 
# colon_s %>%
#   finalfit_newdata(explanatory = explanatory, rowwise = FALSE,
#     newdata = list(
#       rep(seq(0, 30), 4),
#       c(rep("Muscle", 62), rep("Adjacent structures", 62)),
#       c(rep("No", 31), rep("Yes", 31), rep("No", 31), rep("Yes", 31))
#     )
#   ) -> newdata
# 
# colon_s %>%
#   glmmulti(dependent, explanatory) %>%
#   boot_predict(newdata, boot_compare = FALSE,
#   R=100, condense=FALSE) %>%
#   ggplot(aes(x = nodes, y = estimate, ymin = estimate_conf.low,
#       ymax = estimate_conf.high, fill=extent.factor))+
#     geom_line(aes(colour = extent.factor))+
#     geom_ribbon(alpha=0.1)+
#     facet_grid(.~perfor.factor)+
#     xlab("Number of postive lymph nodes")+
#     ylab("Probability of death")+
#     labs(fill = "Extent of tumour", colour = "Extent of tumour")+
#     ggtitle("Probability of death by lymph node count")