reshape_loadings(x, ...)

# S3 method for parameters_efa

# S3 method for data.frame
reshape_loadings(x, threshold = NULL, loadings_columns = NULL, ...)

## Arguments

x A data frame or a statistical model. Arguments passed to or from other methods. A value between 0 and 1 indicates which (absolute) values from the loadings should be removed. An integer higher than 1 indicates the n strongest loadings to retain. Can also be "max", in which case it will only display the maximum loading per variable (the most simple structure). Vector indicating the columns corresponding to loadings.

## Examples

library(parameters)
library(psych)

pca <- model_parameters(psych::fa(attitude, nfactors = 3))

#> ----------------------------------------------------------
#> MR1       |     rating |    0.90 |       1.02 |       0.23
#> MR1       | complaints |    0.97 |       1.01 |       0.10
#> MR1       | privileges |    0.44 |       1.64 |       0.65
#> MR1       |   learning |    0.47 |       2.51 |       0.24
#> MR1       |     raises |    0.55 |       2.35 |       0.23
#> MR1       |   critical |    0.16 |       1.46 |       0.67
#> MR1       |    advance |   -0.11 |       1.04 |       0.22
#> MR2       |     rating |   -0.07 |       1.02 |       0.23
#> MR2       | complaints |   -0.06 |       1.01 |       0.10
#> MR2       | privileges |    0.25 |       1.64 |       0.65
#> MR2       |   learning |    0.54 |       2.51 |       0.24
#> MR2       |     raises |    0.43 |       2.35 |       0.23
#> MR2       |   critical |    0.17 |       1.46 |       0.67
#> MR2       |    advance |    0.91 |       1.04 |       0.22
#> MR3       |     rating |   -0.05 |       1.02 |       0.23
#> MR3       | complaints |    0.04 |       1.01 |       0.10
#> MR3       | privileges |   -0.05 |       1.64 |       0.65
#> MR3       |   learning |   -0.28 |       2.51 |       0.24
#> MR3       |     raises |    0.25 |       2.35 |       0.23
#> MR3       |   critical |    0.48 |       1.46 |       0.67
#> MR3       |    advance |    0.07 |       1.04 |       0.22