Function reference • finalfit

All-in-one functions Single functions to generate final output tables
`finalfit()` `finalfit.lm()` `finalfit.glm()` `finalfit.coxph()`	Final output tables for common regression models
`ff_permute()` `finalfit_permute()`	Permuate explanatory variables to produce multiple output tables for common regression models
`summary_factorlist()`	Summarise a set of factors (or continuous variables) by a dependent variable
`summary_factorlist_stratified()`	Summarise a set of factors (or continuous variables) by a dependent variable
Model wrappers Wrappers for common statistical models
`lmuni()`	Linear regression univariable models: `finalfit` model wrapper
`lmmulti()`	Linear regression multivariable models: `finalfit` model wrapper
`lmmixed()`	Mixed effects linear regression models: `finalfit` model wrapper
`glmuni()`	Binomial logistic regression univariable models: `finalfit` model wrapper
`glmmulti()`	Binomial logistic regression multivariable models: `finalfit` model wrapper
`glmmulti_boot()`	Binomial logistic regression multivariable models with bootstrapped confidence intervals: `finalfit` model wrapper
`glmmixed()`	Mixed effects binomial logistic regression models: `finalfit` model wrapper
`coxphuni()`	Cox proprotional hazards univariable models: `finalfit` model wrapper
`coxphmulti()`	Cox proprotional hazards multivariable models: `finalfit` model wrapper
`crruni()`	Competing risks univariable regression: `finalfit` model wrapper
`crrmulti()`	Competing risks multivariable regression: `finalfit` model wrapper
`svyglmuni()`	Univariable survey-weighted generalised linear models
`svyglmmulti()`	Multivariable survey-weighted generalised linear models
Bootstrap functions Bootstrap simulation for model prediction
`boot_predict()`	Bootstrap simulation for model prediction
`boot_compare()`	Compare bootstrapped distributions
`ff_newdata()` `finalfit_newdata()`	Generate newdata for simulations
`ff_expand()` `finalfit_expand()`	Summarise with mode and mean/median and expand given factors
Helper functions
`fit2df()`	Extract model fit results to dataframe (generic): `finalfit` model extractors
`ff_column_totals()` `finalfit_column_totals()`	Add column totals to `summary_factorlist()` output
`ff_row_totals()` `finalfit_row_totals()`	Add row totals to `summary_factorlist()` output
`ff_formula()` `finalfit_formula()`	Generate formula as character string
`ff_glimpse()` `finalfit_glimpse()`	Descriptive statistics for dataframe
`ff_interaction()` `finalfit_interaction()`	Make an interaction variable and add to dataframe
`ff_mode()` `finalfit_mode()`	Return the most frequent level in a factor
`ff_label()` `finalfit_label()`	Label a variable
`ff_merge()` `finalfit_merge()`	Merge a `summary_factorlist()` table with any number of model results tables.
`ff_metrics()`	Generate common metrics for regression model results
`ff_percent_only()` `finalfit_percent_only()`	Include only percentages for factors in `summary_factorlist` output
`ff_parse_formula()`	Parse a formula to finalfit grammar
`ff_relabel()` `finalfit_relabel()`	Relabel variables in a data frame
`ff_relabel_df()` `finalfit_relabel_df()`	Relabel variables from data frame after tidyverse functions
`ff_remove_ref()` `finalfit_remove_ref()`	Remove regression reference level row from table
`ff_remove_p()` `finalfit_remove_p()`	Remove p-value from output
`ff_stratify_helper()`	Help making stratified summary_factorlist tables
`check_recode()`	Check accurate recoding of variables
`dependent_label()`	Make a label for the dependent variable
`extract_variable_label()`	Extract variable labels from dataframe
`format_n_percent()`	Format n and percent as a character
`labels_to_column()`	Labels to column names
`labels_to_level()`	Labels to level
`p_tidy()`	Round p-values but keep trailing zeros
`rm_duplicates()`	Remove duplicates and replace
`rm_empty_block()`	Remove rows where all specified variables are missing
`round_tidy()`	Round values but keep trailing zeros
`summary_df()`	Summarise with mode for factors and mean/median for numeric variables
Plotting
`or_plot()`	Produce an odds ratio table and plot
`hr_plot()`	Produce a hazard ratio table and plot
`coefficient_plot()`	Produce a coefficient table and plot
`ff_plot()` `finalfit_plot()`	Produce a table and plot
`surv_plot()`	Plot survival curves with number-at-risk table
`missing_plot()`	Missing values occurrence plot
`missing_pairs()`	Missing values pairs plot
Missing data functions
`missing_glimpse()`	Summary of missing values
`missing_pattern()`	Characterise missing data for `finalfit` models
`missing_compare()`	Compare missing data
`missing_pairs()`	Missing values pairs plot
`missing_plot()`	Missing values occurrence plot
`missing_predictorMatrix()`	Create predictorMatrix for use with mice
Metrics
`metrics_hoslem()`	Hosmer-Lemeshow goodness of fit test
Data Sample data for examples and testing
`colon_s`	Chemotherapy for Stage B/C colon cancer
`wcgs`	Western Collaborative Group Study
Misc
`finalfit-package`	finalfit: Quickly create elegant final results tables and plots when modelling.

Reference

All-in-one functions

Model wrappers

Bootstrap functions

Helper functions

Plotting

Missing data functions

Metrics

Data

Misc