This looks for a column with a name including "Coefficient", "OR", or "HR" (finalfit defaults) and removes any rows with "-" (the default for the reference level). Can also be combined to produce an or_plot, see below.

ff_remove_ref(.data, only_binary = TRUE)

finalfit_remove_ref(.data, only_binary = TRUE)

Arguments

.data

Output from finalfit or similar.

only_binary

Logical. Remove reference level only for two-level factors. When set to false, reference level for all factors removed.

Value

Data frame.

Examples

# Table example
explanatory = c("age.factor", "age", "sex.factor", "nodes", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
   finalfit(dependent, explanatory, add_dependent_label = FALSE) %>%
   ff_remove_ref() %>%
   dependent_label(colon_s, dependent)
#> Note: dependent includes missing data. These are dropped.
#> 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...
#> 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                 Age (years)   Mean (SD) 59.8 (11.4) 59.9 (12.5)
#> 5                         Sex        Male  268 (56.1)  210 (43.9)
#> 6                       nodes   Mean (SD)   2.7 (2.4)   4.9 (4.4)
#> 7                 Obstruction         Yes   89 (51.1)   85 (48.9)
#> 8                 Perforation         Yes   14 (51.9)   13 (48.1)
#>            OR (univariable)        OR (multivariable)
#> 1                         -                         -
#> 2 0.54 (0.32-0.92, p=0.023) 0.71 (0.34-1.48, p=0.354)
#> 3 0.75 (0.45-1.25, p=0.270) 1.05 (0.37-2.99, p=0.930)
#> 4 1.00 (0.99-1.01, p=0.986) 1.00 (0.97-1.03, p=0.971)
#> 5 0.98 (0.76-1.27, p=0.889) 0.98 (0.74-1.30, p=0.896)
#> 6 1.24 (1.18-1.30, p<0.001) 1.25 (1.18-1.32, p<0.001)
#> 7 1.25 (0.90-1.74, p=0.189) 1.36 (0.95-1.94, p=0.092)
#> 8 1.18 (0.54-2.55, p=0.672) 1.05 (0.47-2.36, p=0.896)

# Plot example
explanatory = c("age.factor", "age", "sex.factor", "nodes", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  summary_factorlist(dependent, explanatory, total_col = TRUE, fit_id=TRUE) %>%
  ff_merge(
    glmuni(colon_s, dependent, explanatory) %>%
    fit2df()) %>%
  ff_remove_ref() %>%
  dplyr::select(-`OR`) -> factorlist_plot
#> Note: dependent includes missing data. These are dropped.
#> 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...
#> Waiting for profiling to be done...

colon_s %>%
   or_plot(dependent, explanatory, factorlist = factorlist_plot)
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Warning: Removed 1 rows containing missing values (geom_errorbarh).