center_predictors: Center predictor variables for dyadic models
In dyadMLM: Tools for Dyadic Multilevel Models
View source: R/data_prep_centering.R
center_predictors R Documentation
Center predictor variables for dyadic models
Description
Adds centered predictor columns to a dyadMLM_data object. It currently
supports two-level temporal centering for intensive longitudinal predictors:
a within-person component and a between-person component. The original
predictor remains available as a raw component for model-specific column
construction.
It can also add GMC source components for numeric APIM predictors.
For two-level temporal centering, the between-person component is centered
around the grand mean of person means, not the grand mean of all observed rows.
This gives each person equal weight even when people have different numbers of
observed measurement occasions.
Usage
center_predictors(data, add_apim_gmc_predictors = FALSE)
Arguments
data
A dyadMLM_data object returned by prepare_dyad_data().
add_apim_gmc_predictors
Whether to add APIM GMC source components.
Details
The function uses the structural metadata stored by
prepare_dyad_data().
Value
A dyadMLM_data object with centered predictor columns added and
updated predictor metadata.
dyadMLM documentation built on Oct. 28, 2026, 1:06 a.m.
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View source: R/data_prep_centering.R
| center_predictors | R Documentation |
Center predictor variables for dyadic models
Description
Adds centered predictor columns to a dyadMLM_data object. It currently
supports two-level temporal centering for intensive longitudinal predictors:
a within-person component and a between-person component. The original
predictor remains available as a raw component for model-specific column
construction.
It can also add GMC source components for numeric APIM predictors.
For two-level temporal centering, the between-person component is centered
around the grand mean of person means, not the grand mean of all observed rows.
This gives each person equal weight even when people have different numbers of
observed measurement occasions.
Usage
center_predictors(data, add_apim_gmc_predictors = FALSE)
Arguments
data |
A |
add_apim_gmc_predictors |
Whether to add APIM GMC source components. |
Details
The function uses the structural metadata stored by
prepare_dyad_data().
Value
A dyadMLM_data object with centered predictor columns added and
updated predictor metadata.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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