Standardize column names from data frames, in particular objects returned
from `parameters::model_parameters()`

, so column
names are consistent and the same for any model object.

## Usage

```
standardize_names(data, ...)
# S3 method for parameters_model
standardize_names(
data,
style = c("easystats", "broom"),
ignore_estimate = FALSE,
...
)
```

## Arguments

- data
A data frame. In particular, objects from

*easystats*package functions like`parameters::model_parameters()`

or`effectsize::effectsize()`

are accepted, but also data frames returned by`broom::tidy()`

are valid objects.- ...
Currently not used.

- style
Standardization can either be based on the naming conventions from the easystats-project, or on broom's naming scheme.

- ignore_estimate
Logical, if

`TRUE`

, column names like`"mean"`

or`"median"`

will*not*be converted to`"Coefficient"`

resp.`"estimate"`

.

## Details

This method is in particular useful for package developers or users
who use, e.g.,
`parameters::model_parameters()`

in their own
code or functions to retrieve model parameters for further processing. As
`model_parameters()`

returns a data frame with varying column names
(depending on the input), accessing the required information is probably
not quite straightforward. In such cases, `standardize_names()`

can be
used to get consistent, i.e. always the same column names, no matter what
kind of model was used in `model_parameters()`

.

For `style = "broom"`

, column names are renamed to match broom's
naming scheme, i.e. `Parameter`

is renamed to `term`

,
`Coefficient`

becomes `estimate`

and so on.

For `style = "easystats"`

, when `data`

is an object from
`broom::tidy()`

, column names are converted from "broom"-style into
"easystats"-style.

## Examples

```
if (require("parameters")) {
model <- lm(mpg ~ wt + cyl, data = mtcars)
mp <- model_parameters(model)
as.data.frame(mp)
standardize_names(mp)
standardize_names(mp, style = "broom")
}
#> term estimate std.error conf.level conf.low conf.high statistic
#> 1 (Intercept) 39.686261 1.7149840 0.95 36.178725 43.1937976 23.140893
#> 2 wt -3.190972 0.7569065 0.95 -4.739020 -1.6429245 -4.215808
#> 3 cyl -1.507795 0.4146883 0.95 -2.355928 -0.6596622 -3.635972
#> df.error p.value
#> 1 29 3.043182e-20
#> 2 29 2.220200e-04
#> 3 29 1.064282e-03
```