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 |

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