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.

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".

Value

A data frame, with standardized column names.

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