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)





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 also


# 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