summary_factorlist() table with any number of model
results tables.R/ff_merge.R
ff_merge.RdA 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
)Output from summary_factorlist(...,
fit_id=TRUE).
Output from model wrappers followed by
fit2df().
Reference symbol for model reference levels, typically "-" or "1.0".
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
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)
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
#> Note: dependent includes missing data. These are dropped.
colon_s %>%
glmuni(dependent, explanatory) %>%
fit2df(estimate_suffix=" (univariable)") -> example.univariable
#> 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 %>%
glmmulti(dependent, explanatory) %>%
fit2df(estimate_suffix=" (multivariable)") -> example.multivariable
#> Waiting for profiling to be done...
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)
#> label levels Alive Died OR (univariable)
#> 3 Age <40 years 31 (6.1) 36 (8.9) -
#> 1 40-59 years 208 (40.7) 131 (32.4) 0.54 (0.32-0.92, p=0.023)
#> 2 60+ years 272 (53.2) 237 (58.7) 0.75 (0.45-1.25, p=0.270)
#> 8 Sex Female 243 (47.6) 194 (48.0) -
#> 9 Male 268 (52.4) 210 (52.0) 0.98 (0.76-1.27, p=0.889)
#> 4 Obstruction No 408 (82.1) 312 (78.6) -
#> 5 Yes 89 (17.9) 85 (21.4) 1.25 (0.90-1.74, p=0.189)
#> 6 Perforation No 497 (97.3) 391 (96.8) -
#> 7 Yes 14 (2.7) 13 (3.2) 1.18 (0.54-2.55, p=0.672)
#> OR (multivariable) OR (multilevel)
#> 3 - -
#> 1 0.57 (0.34-0.98, p=0.041) 0.75 (0.39-1.44, p=0.382)
#> 2 0.81 (0.48-1.36, p=0.426) 1.03 (0.55-1.96, p=0.916)
#> 8 - -
#> 9 0.98 (0.75-1.28, p=0.902) 0.80 (0.58-1.11, p=0.180)
#> 4 - -
#> 5 1.25 (0.90-1.76, p=0.186) 1.23 (0.82-1.83, p=0.320)
#> 6 - -
#> 7 1.12 (0.51-2.44, p=0.770) 1.03 (0.43-2.51, p=0.940)
# Using finalfit()
colon_s %>%
finalfit(dependent, explanatory, keep_fit_id = TRUE) %>%
ff_merge(example.multilevel, last_merge = TRUE)
#> 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...
#> Dependent: Mortality 5 year Alive Died
#> 3 Age <40 years 31 (46.3) 36 (53.7)
#> 1 40-59 years 208 (61.4) 131 (38.6)
#> 2 60+ years 272 (53.4) 237 (46.6)
#> 8 Sex Female 243 (55.6) 194 (44.4)
#> 9 Male 268 (56.1) 210 (43.9)
#> 4 Obstruction No 408 (56.7) 312 (43.3)
#> 5 Yes 89 (51.1) 85 (48.9)
#> 6 Perforation No 497 (56.0) 391 (44.0)
#> 7 Yes 14 (51.9) 13 (48.1)
#> OR (univariable) OR (multivariable) OR (multilevel)
#> 3 - - -
#> 1 0.54 (0.32-0.92, p=0.023) 0.57 (0.34-0.98, p=0.041) 0.75 (0.39-1.44, p=0.382)
#> 2 0.75 (0.45-1.25, p=0.270) 0.81 (0.48-1.36, p=0.426) 1.03 (0.55-1.96, p=0.916)
#> 8 - - -
#> 9 0.98 (0.76-1.27, p=0.889) 0.98 (0.75-1.28, p=0.902) 0.80 (0.58-1.11, p=0.180)
#> 4 - - -
#> 5 1.25 (0.90-1.74, p=0.189) 1.25 (0.90-1.76, p=0.186) 1.23 (0.82-1.83, p=0.320)
#> 6 - - -
#> 7 1.18 (0.54-2.55, p=0.672) 1.12 (0.51-2.44, p=0.770) 1.03 (0.43-2.51, p=0.940)