Generate newdata for simulations
Generate newdata while respecting the variable types and factor levels in the primary data frame used to run model.
ff_newdata(.data, dependent = NULL, explanatory = NULL, rowwise = TRUE, newdata) finalfit_newdata(.data, dependent = NULL, explanatory = NULL, rowwise = TRUE, newdata)
.data |
Dataframe. |
dependent |
Optional character vector of length 1: name of depdendent variable. Not usually specified in bootstrapping model predictions. |
explanatory |
Character vector of any length: name(s) of explanatory variables. |
rowwise |
Logical. Format |
newdata |
A list of rows or columns coresponding exactly to the order of explanatory variables. Useful errors generated if requirements not fulfilled |
Generate model predictions against a specified set of explanatory levels with
bootstrapped confidence intervals. Add a comparison by difference or ratio of
the first row of newdata with all subsequent rows.
A list of multivariable glm fitted model
outputs. Output is of class glmlist.
# See boot_predict.
library(finalfit)
library(dplyr)
# Predict probability of death across combinations of factor levels
explanatory = c("age.factor", "extent.factor", "perfor.factor")
dependent = 'mort_5yr'
# Generate combination of explanatory variable levels rowwise
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
# Generate combination of explanatory variable levels colwise.
explanatory = c("nodes", "extent.factor", "perfor.factor")
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))
)) -> newdataPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.