Converting modelbased-objects into raw data frames — as.data.frame.estimate

as.data.frame() method for modelbased objects. Can be used to return a "raw" data frame without attributes and with standardized column names. By default, the original column names are preserved, to avoid unexpected changes, but this can be changed with the preserve_names argument.

Usage

# S3 method for class 'estimate_contrasts'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  ...,
  stringsAsFactors = FALSE,
  use_responsename = FALSE,
  preserve_names = TRUE
)

Arguments

x: An object returned by the different estimate_*() functions.
row.names: NULL or a character vector giving the row names for the data frame. Missing values are not allowed.
optional: logical. If TRUE, setting row names and converting column names (to syntactic names: see make.names) is optional. Note that all of R's base package as.data.frame() methods use optional only for column names treatment, basically with the meaning of data.frame(*, check.names = !optional). See also the make.names argument of the matrix method.
...: Arguments passed to the data.frame method of as.data.frame().
stringsAsFactors: logical: should the character vector be converted to a factor?
use_responsename: Logical, if TRUE, the response variable name is used as column name for the estimate column (if available). If FALSE (default), the column is named "Coefficient".
preserve_names: Logical, if TRUE (default), the original column names are preserved. If FALSE, the estimate column is renamed to either the response name (if use_responsename = TRUE) or to "Coefficient".

Value

A data frame.

Examples

model <- lm(Petal.Length ~ Species, data = iris)
out <- estimate_means(model, "Species")

# default
print(out)
#> Estimated Marginal Means
#> 
#> Species    | Mean |   SE |       95% CI | t(147)
#> ------------------------------------------------
#> setosa     | 1.46 | 0.06 | [1.34, 1.58] |  24.02
#> versicolor | 4.26 | 0.06 | [4.14, 4.38] |  70.00
#> virginica  | 5.55 | 0.06 | [5.43, 5.67] |  91.23
#> 
#> Variable predicted: Petal.Length
#> Predictors modulated: Species
#> 

as.data.frame(out)
#>      Species  Mean         SE   CI_low  CI_high        t  df
#> 1     setosa 1.462 0.06085848 1.341729 1.582271 24.02294 147
#> 2 versicolor 4.260 0.06085849 4.139729 4.380271 69.99846 147
#> 3  virginica 5.552 0.06085848 5.431729 5.672271 91.22804 147

as.data.frame(out, preserve_names = FALSE)
#>      Species Coefficient         SE   CI_low  CI_high        t  df
#> 1     setosa       1.462 0.06085848 1.341729 1.582271 24.02294 147
#> 2 versicolor       4.260 0.06085849 4.139729 4.380271 69.99846 147
#> 3  virginica       5.552 0.06085848 5.431729 5.672271 91.22804 147

as.data.frame(out, preserve_names = FALSE, use_responsename = TRUE)
#>      Species Petal.Length         SE   CI_low  CI_high        t  df
#> 1     setosa        1.462 0.06085848 1.341729 1.582271 24.02294 147
#> 2 versicolor        4.260 0.06085849 4.139729 4.380271 69.99846 147
#> 3  virginica        5.552 0.06085848 5.431729 5.672271 91.22804 147