When producing conditional estimates from a regression model, it is often useful to set variables not of interest to their mode when creating the newdata object.
ff_mode(...)
finalfit_mode(...)Unquoted factor names.
The most frequent level in a factor.
library(dplyr)
colon_s %>%
summarise(age.factor = ff_mode(age.factor))
#> age.factor
#> 1 60+ years
colon_s %>%
select(sex.factor, rx.factor, obstruct.factor, perfor.factor) %>%
summarise(across(everything(), ff_mode))
#> sex.factor rx.factor obstruct.factor perfor.factor
#> 1 Male Obs No No
colon_s %>%
reframe(across(where(is.factor), ff_mode))
#> rx sex.factor rx.factor obstruct.factor perfor.factor adhere.factor
#> 1 Obs Male Obs No No No
#> 2 Obs Male Obs No No No
#> 3 Obs Male Obs No No No
#> 4 Obs Male Obs No No No
#> differ.factor extent.factor surg.factor node4.factor status.factor age.factor
#> 1 Moderate Serosa Short No Alive 60+ years
#> 2 Moderate Serosa Short No Alive 60+ years
#> 3 Moderate Serosa Short No Alive 60+ years
#> 4 Moderate Serosa Short No Alive 60+ years
#> loccomp.factor mort_5yr hospital
#> 1 No Alive hospital_1
#> 2 No Alive hospital_2
#> 3 No Alive hospital_4
#> 4 No Alive hospital_5
# Note, 4 rows is returned in this example because 4 factor levels within `hospital`
# have the same frequency.