Final output tables for common regression models
An "all-in-one" function that takes a single dependent variable with a vector of explanatory variable names (continuous or categorical variables) to produce a final table for publication including summary statistics. The appropriate model is selected on the basis of dependent variable and whether a random effect is specified.
finalfit.lm method (not called directly)
finalfit.glm method (not called directly)
finalfit.coxph method (not called directly)
finalfit(.data, dependent, explanatory, explanatory_multi = NULL, random_effect = NULL, column = FALSE, keep_models = FALSE, metrics = FALSE, add_dependent_label = TRUE, dependent_label_prefix = "Dependent: ", dependent_label_suffix = "", keep_fit_id = FALSE, ...) finalfit.lm(.data, dependent, explanatory, explanatory_multi = NULL, random_effect = NULL, column = FALSE, keep_models = FALSE, metrics = FALSE, add_dependent_label = TRUE, dependent_label_prefix = "Dependent: ", dependent_label_suffix = "", keep_fit_id = FALSE, ...) finalfit.glm(.data, dependent, explanatory, explanatory_multi = NULL, random_effect = NULL, column = FALSE, keep_models = FALSE, metrics = FALSE, add_dependent_label = TRUE, dependent_label_prefix = "Dependent: ", dependent_label_suffix = "", keep_fit_id = FALSE, ...) finalfit.coxph(.data, dependent, explanatory, explanatory_multi = NULL, random_effect = NULL, column = TRUE, keep_models = FALSE, metrics = FALSE, add_dependent_label = TRUE, dependent_label_prefix = "Dependent: ", dependent_label_suffix = "", keep_fit_id = FALSE, ...)
.data |
Data frame or tibble. |
dependent |
Character vector of length 1: quoted name of dependent
variable. Can be continuous, a binary factor, or a survival object of form
|
explanatory |
Character vector of any length: quoted name(s) of explanatory variables. |
explanatory_multi |
Character vector of any length: quoted name(s) of a
subset of explanatory variables to generate reduced multivariable model
(must only contain variables contained in |
random_effect |
Character vector of length 1: quoted name of random
effects variable. When included mixed effects model generated
( |
column |
Logical: Compute margins by column rather than row. |
keep_models |
Logical: include full multivariable model in output when
working with reduced multivariable model ( |
metrics |
Logical: include useful model metrics in output in publication format. |
add_dependent_label |
Add the name of the dependent label to the top left of table. |
dependent_label_prefix |
Add text before dependent label. |
dependent_label_suffix |
Add text after dependent label. |
keep_fit_id |
Keep original model output coefficient label (internal). |
... |
Other arguments to pass to |
Returns a data frame with the final model table.
library(finalfit)
library(dplyr)
# Summary, univariable and multivariable analyses of the form:
# glm(depdendent ~ explanatory, family="binomial")
# lmuni(), lmmulti(), lmmixed(), glmuni(), glmmulti(), glmmixed(), glmmultiboot(),
# coxphuni(), coxphmulti()
data(colon_s) # Modified from survival::colon
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
finalfit(dependent, explanatory)
# Multivariable analysis with subset of explanatory
# variable set used in univariable analysis
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory_multi = c("age.factor", "obstruct.factor")
dependent = "mort_5yr"
colon_s %>%
finalfit(dependent, explanatory, explanatory_multi)
# Summary, univariable and multivariable analyses of the form:
# lme4::glmer(dependent ~ explanatory + (1 | random_effect), family="binomial")
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory_multi = c("age.factor", "obstruct.factor")
random_effect = "hospital"
dependent = "mort_5yr"
# colon_s %>%
# finalfit(dependent, explanatory, explanatory_multi, random_effect)
# Include model metrics:
colon_s %>%
finalfit(dependent, explanatory, explanatory_multi, metrics=TRUE)
# Summary, univariable and multivariable analyses of the form:
# survival::coxph(dependent ~ explanatory)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
finalfit(dependent, explanatory)
# Rather than going all-in-one, any number of subset models can
# be manually added on to a summary_factorlist() table using finalfit.merge().
# This is particularly useful when models take a long-time to run or are complicated.
# Note requirement for fit_id=TRUE.
# `fit2df` is a subfunction extracting most common models to a dataframe.
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
finalfit(dependent, explanatory, metrics=TRUE)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
explanatory_multi = c("age.factor", "obstruct.factor")
random_effect = "hospital"
dependent = 'mort_5yr'
# 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
# Edited as CRAN slow to run these
# colon_s %>%
# glmmixed(dependent, explanatory, random_effect) %>%
# fit2df(estimate_suffix=" (multilevel") -> example.multilevel
# Pipe together
example.summary %>%
finalfit_merge(example.univariable) %>%
finalfit_merge(example.multivariable, last_merge = TRUE)
# finalfit_merge(example.multilevel)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.