z_scored_mlm_categorical.RdThis is a specialized function for mean centering categorical variables. There are two cases where this function should be used instead of the generic `center_mlm`. 1. This function should be used when you need group mean centering for non-dummy-coded variables at L1. Variables at L2 are always dummy-coded as they represent the percentage of subjects in that group. 2. This function should be used whenever you want to z-score the aggregated L2 means
z_scored_mlm_categorical(
data,
cols,
dummy_coded = NA,
group,
keep_original = TRUE
)A data.frame or a data.frame extension (e.g. a tibble).
Dummy-coded or effect-coded columns for group-mean centering. Support `dplyr::dplyr_tidy_select` options.
Dummy-coded variables (cannot be effect-coded) for L2 aggregated means. Support `dplyr::dplyr_tidy_select` options.
the grouping variable. Must be character
default is `FALSE`. Set to `TRUE` to keep original columns
An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved 2. Columns with L1 scores that are group-mean centered 3. Columns with L2 aggregated means (i.e., percentage) that are z-scored
z_scored_mlm_categorical(mlbook_data,cols='female_eff',dummy_coded='female_dum','schoolnr')
#> # A tibble: 3,758 × 19
#> schoolnr pupilNR_new langPOST ses IQ_verb sex Minority denomina
#> <int> <int> <int> <dbl> <dbl> <int> <int> <int>
#> 1 1 3 46 -4.73 3.13 0 0 1
#> 2 1 4 45 -17.7 2.63 0 1 1
#> 3 1 5 33 -12.7 -2.37 0 0 1
#> 4 1 6 46 -4.73 -0.87 0 0 1
#> 5 1 7 20 -17.7 -3.87 0 0 1
#> 6 1 8 30 -17.7 -2.37 0 1 1
#> 7 1 9 30 -4.73 -2.37 0 1 1
#> 8 1 10 57 -17.7 1.13 0 0 1
#> 9 1 11 36 -14.7 -2.37 0 1 1
#> 10 1 12 36 -12.7 -0.87 0 1 1
#> # ℹ 3,748 more rows
#> # ℹ 11 more variables: female_dum <int>, female_eff <int>, female_CMC <dbl>,
#> # fempct_agg <dbl>, Zfempct_agg <dbl>, ses_CMC <dbl>, Zses_CMC <dbl>,
#> # ses_agg <dbl>, Zses_agg <dbl>, female_eff_group_c <dbl>,
#> # female_dum_mean_z <dbl>