# glmmulti_boot: Binomial logistic regression multivariable models with... In finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

 glmmulti_boot R Documentation

## Binomial logistic regression multivariable models with bootstrapped confidence intervals: `finalfit` model wrapper

### Description

Using `finalfit` conventions, produces a multivariable binomial logistic regression models for a set of explanatory variables against a binary dependent.

### Usage

```glmmulti_boot(.data, dependent, explanatory, R = 1000)
```

### Arguments

 `.data` Dataframe. `dependent` Character vector length 1: name of depdendent variable (must have 2 levels). `explanatory` Character vector of any length: name(s) of explanatory variables. `R` Number of draws.

### Details

Uses `glm` with `finalfit` modelling conventions. `boot::boot` is used to draw bootstrapped confidence intervals on fixed effect model coefficients. Output can be passed to `fit2df`.

### Value

A multivariable `glm` fitted model with bootstrapped confidence intervals. Output is of class `glmboot`.

`fit2df, finalfit_merge`

Other finalfit model wrappers: `coxphmulti()`, `coxphuni()`, `crrmulti()`, `crruni()`, `glmmixed()`, `glmmulti()`, `glmuni()`, `lmmixed()`, `lmmulti()`, `lmuni()`, `svyglmmulti()`, `svyglmuni()`

### Examples

```library(finalfit)
library(dplyr)
## Note number of draws set to 100 just for speed in this example
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"

colon_s %>%
glmmulti_boot(dependent, explanatory, R=100) %>%
fit2df(estimate_suffix="(multivariable (BS CIs))")

```

finalfit documentation built on Jan. 14, 2023, 5:07 p.m.