This will work with finalfit and any fit2df output.

ff_remove_p(.data)

finalfit_remove_p(.data)

Arguments

.data

Output from finalfit or similar.

Value

Data frame.

Examples

explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = 'mort_5yr' colon_s %>% finalfit(dependent, explanatory) %>% ff_remove_p()
#> Note: dependent includes missing data. These are dropped.
#> Warning: Factor `obstruct.factor` contains implicit NA, consider using `forcats::fct_explicit_na`
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Dependent: Mortality 5 year Alive Died #> 1 Age <40 years 31 (46.3) 36 (53.7) #> 2 40-59 years 208 (61.4) 131 (38.6) #> 3 60+ years 272 (53.4) 237 (46.6) #> 4 Sex Female 243 (55.6) 194 (44.4) #> 5 Male 268 (56.1) 210 (43.9) #> 6 Obstruction No 408 (56.7) 312 (43.3) #> 7 Yes 89 (51.1) 85 (48.9) #> 8 Perforation No 497 (56.0) 391 (44.0) #> 9 Yes 14 (51.9) 13 (48.1) #> OR (univariable) OR (multivariable) #> 1 - - #> 2 0.54 (0.32-0.92) 0.57 (0.34-0.98) #> 3 0.75 (0.45-1.25) 0.81 (0.48-1.36) #> 4 - - #> 5 0.98 (0.76-1.27) 0.98 (0.75-1.28) #> 6 - - #> 7 1.25 (0.90-1.74) 1.25 (0.90-1.76) #> 8 - - #> 9 1.18 (0.54-2.55) 1.12 (0.51-2.44)