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
)Dataframe.
Optional character vector of length 1: name of depdendent variable. Not usually specified in bootstrapping model predictions.
Character vector of any length: name(s) of explanatory variables.
Logical. Format newdata is provided in.
A list of rows or columns coresponding exactly to the order of explanatory variables. Useful errors generated if requirements not fulfilled
A list of multivariable glm fitted model
outputs. Output is of class glmlist.
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.
# 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))
)) -> newdata