finalfit (version 1.0.2)

finalfit: 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, 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, ...)

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: quoted name of random effects variable. When included mixed effects model generated (lme4::glmer lme4::lmer).

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

# NOT RUN {
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)
# }