ff_merge {finalfit} | R Documentation |
summary_factorlist()
table with any number of model
results tables.A function that takes the output from summary_factorlist(...,
fit_id=TRUE)
and merges with any number of model dataframes, usually
produced with a model wrapper followed by the fit2df()
function
(see examples).
ff_merge( factorlist, fit2df_df, ref_symbol = "-", estimate_name = NULL, last_merge = FALSE ) finalfit_merge( factorlist, fit2df_df, ref_symbol = "-", estimate_name = NULL, last_merge = FALSE )
factorlist |
Output from |
fit2df_df |
Output from model wrappers followed by
|
ref_symbol |
Reference symbol for model reference levels, typically "-" or "1.0". |
estimate_name |
If you have chosen a new 'estimate name' (e.g. "Odds ratio") when running a model wrapper (e.g. 'glmuni'), then you need to pass this new name to 'finalfit_merge' to generate correct table. Defaults to OR/HR/Coefficient |
last_merge |
Logical. Set to try for the final merge in a series to remove index and fit_id columns. |
Returns a dataframe of combined tables.
library(finalfit) library(dplyr) data(colon_s) explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") explanatory_multi = c("age.factor", "obstruct.factor") random_effect = "hospital" dependent = "mort_5yr" # Create separate tables colon_s %>% summary_factorlist(dependent, explanatory, fit_id=TRUE) -> example.summary colon_s %>% glmuni(dependent, explanatory) %>% fit2df(estimate_suffix=" (univariable)") -> example.univariable colon_s %>% glmmulti(dependent, explanatory) %>% fit2df(estimate_suffix=" (multivariable)") -> example.multivariable colon_s %>% glmmixed(dependent, explanatory, random_effect) %>% fit2df(estimate_suffix=" (multilevel)") -> example.multilevel # Pipe together example.summary %>% ff_merge(example.univariable) %>% ff_merge(example.multivariable) %>% ff_merge(example.multilevel, last_merge = TRUE) # Using finalfit() colon_s %>% finalfit(dependent, explanatory, keep_fit_id = TRUE) %>% ff_merge(example.multilevel, last_merge = TRUE)