Using `finalfit`

conventions, produces multiple univariable binomial logistic
regression models for a set of explanatory variables against a binary dependent.

`glmuni(.data, dependent, explanatory, family = "binomial", weights = "", ...)`

## Arguments

- .data
Data frame.

- dependent
Character vector of length 1: name of depdendent variable (must have 2 levels).

- explanatory
Character vector of any length: name(s) of explanatory variables.

- family
Character vector quoted or unquoted of the error distribution
and link function to be used in the model, see `glm`

.

- weights
Character vector of length 1: name of variabe for weighting.
'Prior weights' to be used in the fitting process.

- ...
Other arguments to pass to `glm`

.

## Value

A list of univariable `glm`

fitted model outputs.
Output is of class `glmlist`

.

## Details

Uses `glm`

with `finalfit`

modelling conventions. Output can be
passed to `fit2df`

.

## See also

`fit2df, finalfit_merge`

Other finalfit model wrappers:
`coxphmulti()`

,
`coxphuni()`

,
`crrmulti()`

,
`crruni()`

,
`glmmixed()`

,
`glmmulti_boot()`

,
`glmmulti()`

,
`lmmixed()`

,
`lmmulti()`

,
`lmuni()`

,
`svyglmmulti()`

,
`svyglmuni()`

## Examples

```
library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
glmuni(dependent, explanatory) %>%
fit2df(estimate_suffix=" (univariable)")
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> explanatory OR (univariable)
#> 1 age.factor40-59 years 0.54 (0.32-0.92, p=0.023)
#> 2 age.factor60+ years 0.75 (0.45-1.25, p=0.270)
#> 3 sex.factorMale 0.98 (0.76-1.27, p=0.889)
#> 4 obstruct.factorYes 1.25 (0.90-1.74, p=0.189)
#> 5 perfor.factorYes 1.18 (0.54-2.55, p=0.672)
```