Using `finalfit`

conventions, produces multiple univariable linear
regression models for a set of explanatory variables against a continuous dependent.

`lmuni(.data, dependent, explanatory, weights = "", ...)`

## Arguments

- .data
Dataframe.

- dependent
Character vector of length 1, name of depdendent variable (must be continuous vector).

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

- 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 `lm`

.

## Value

A list of multivariable `lm`

fitted model outputs.
Output is of class `lmlist`

.

## Details

Uses `lm`

with `finalfit`

modelling conventions. Output can be
passed to `fit2df`

.

## See also

`fit2df`

Other finalfit model wrappers:
`coxphmulti()`

,
`coxphuni()`

,
`crrmulti()`

,
`crruni()`

,
`glmmixed()`

,
`glmmulti_boot()`

,
`glmmulti()`

,
`glmuni()`

,
`lmmixed()`

,
`lmmulti()`

,
`svyglmmulti()`

,
`svyglmuni()`

## Examples

```
library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"
colon_s %>%
lmuni(dependent, explanatory) %>%
fit2df()
#> explanatory Coefficient
#> 1 age.factor40-59 years -1.14 (-2.08 to -0.21, p=0.016)
#> 2 age.factor60+ years -1.19 (-2.10 to -0.28, p=0.010)
#> 3 sex.factorMale -0.14 (-0.60 to 0.33, p=0.565)
#> 4 obstruct.factorYes -0.24 (-0.83 to 0.36, p=0.435)
#> 5 perfor.factorYes 0.24 (-1.13 to 1.61, p=0.735)
```