ff_newdata {finalfit} | R Documentation |
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)) )) -> newdata