finalfit: Final output tables for common regression models

In finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

Final output tables for common regression models

Description

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)

Usage

finalfit(
  .data,
  dependent = NULL,
  explanatory = NULL,
  explanatory_multi = NULL,
  random_effect = NULL,
  formula = NULL,
  model_args = list(),
  weights = NULL,
  cont_cut = 5,
  column = NULL,
  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,
  model_args = NULL,
  weights = NULL,
  cont_cut = 5,
  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,
  model_args = NULL,
  weights = NULL,
  cont_cut = 5,
  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,
  model_args = NULL,
  column = TRUE,
  cont_cut = 5,
  keep_models = FALSE,
  metrics = FALSE,
  add_dependent_label = TRUE,
  dependent_label_prefix = "Dependent: ",
  dependent_label_suffix = "",
  keep_fit_id = FALSE,
  ...
)

Arguments

.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 Surv(time, status).
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 explanatory).
random_effect	Character vector of length 1, either, (1) name of random intercept variable, e.g. "var1", (automatically convered to "(1 | var1)"); or, (2) the full lme4 specification, e.g. "(var1 | var2)". Note parenthesis MUST be included in (2) but NOT included in (1).
formula	an object of class "formula" (or one that can be coerced to that class). Optional instead of standard dependent/explanatory format. Do not include if using dependent/explanatory.
model_args	List. A list of arguments to pass to lm, glm, coxph.
weights	Character vector of length 1: quoted name of weights variable. Passed to summary_factorlist, lm, and glm to provide weighted summary table and regression (e.g. IPTW). If wish weighted regression and non-weighted summary table, pass weights argument within model_args. Not available with surival dependent variable.
cont_cut	Numeric: number of unique values in continuous variable at which to consider it a factor.
column	Logical: Compute margins by column rather than row.
keep_models	Logical: include full multivariable model in output when working with reduced multivariable model (explanatory_multi) and/or mixed effect models (random_effect).
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 fit2df: estimate_name, digits, confint_type, confint_level, confint_sep.

Value

Returns a data frame with the final model table.

Examples

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)

finalfit documentation built on Nov. 17, 2023, 1:09 a.m.