rep_generic_id_mods_builder: Individual level Moderators (Generic) Model Builder

In Coryc3133/ReputationAnalyses: Functions to model and analyze triadic (T-P1-P2) reputational data

Description

This is a generic function for building a lavaan model for individual-level moderators on two distinguishable ratings on the same target. This could be P1- and P2- reports, P2- and self-reports, P1- and self-reports, or any other sets of distinguishable ratings.

Usage

rep_generic_id_mods_builder(rating_1, rating_2, id_mod_variable,
  interaction_term, n_triads = length(rating_1), n_r1_per_r2 = 1,
  n_r2_per_r1 = 1)

Arguments

rating_1	Quoted column names that contain the first rating variable. This might be P1 reports if investigating moderation of hearsay consensus or self-reports for moderation of hearsay accuracy. If more than one is supplied, the target-wise order must match across variables.
rating_2	Quoted column names that contain second rating variable. For hearsay consensus or accuracy, this would be P2 reports. If more than one is supplied, the target-wise order must match across variables.
id_mod_variable	Quoted column names that contain the individual-level moderator of interest. If more than one is supplied from multiple exchangeable dyads/triads, the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered to facilitate interpretability.
interaction_term	Quoted column names that contain the interaction term, or the product of the mean-centered P2-report and the mean-centered moderator variable. If more than one is supplied from multiple exchangeable dyads/triads, the target-wise order must match the order of the ratings.
n_triads	The number of exchangeable triads in each group. By default, this is determined by counting the number of P1 reports. This parameter rarely needs to be changed.
n_r1_per_r2	The number of first ratings for each second rating. Currently, only 1:1 is supported.
n_r2_per_r1	The number of second ratings for each first rating. Currently, only 1:1 is supported.

Details

The parameters for the individual-level moderator analyses are:

rating_me

main effect of other rating; this should correspond to correlation between ratings at average level of moderator variable (if data were properly mean-centered).

mod_me

The meain effect of the moderator variable; it can be interpreted as the difference in rating_1 to differences in the individual-level moderator variable.

interaction

This is the interaction term. It indicates the extent to which the correlation between ratings depends on the moderator variable

v_rating_1

variance for first rating

v_rating_2

variance for second rating

v_mod

variance for moderator variable

v_interaction

variance for interaction term

int_rating_1

intercept for first rating

int_rating_2

intercept for second rating

int_mod

intercept for moderator variable

int_interaction

intercept for interaction term

The function can handle up to n exchangeable triads.

Value

The function returns a list containing an object of class tbl_df and a string object of the model in lavaan syntax. Model information includes the type of model, the number of exchangeable triads, and the number of p1s per p2s, and the number of p2s per p1s.

Examples

data("rep_sim_data")
          # Prepare data
         moderator_data <- rep_sim_data %>%
           dplyr::mutate(B_C_agreeableness_cent = scale(B_C_agreeableness, scale = FALSE),
                  D_A_agreeableness_cent = scale(D_A_agreeableness, scale = FALSE),
                  B_iri_perspective_cent = scale(B_iri_perspective, scale = FALSE),
                  D_iri_perspective_cent = scale(D_iri_perspective, scale = FALSE),
                  B_ptXagree_interaction = B_C_agreeableness_cent*B_iri_perspective_cent,
                  D_ptXagree_interaction = D_A_agreeableness_cent*D_iri_perspective_cent)

          # Buld Model
          agree_pt_mod_model <- rep_generic_id_mods_builder (rating_1 = c("A_C_agreeableness", "C_A_agreeableness"),
                                rating_2 = c("B_C_agreeableness_cent", "D_A_agreeableness_cent"),
                                id_mod_variable = c("B_iri_perspective_cent", "D_iri_perspective_cent"),
                                interaction_term = c("B_ptXagree_interaction", "D_ptXagree_interaction"))
         # view model
         cat(agree_pt_mod_model$model)

         # view model Information
         agree_pt_mod_model$rep_model_info

Coryc3133/ReputationAnalyses documentation built on July 31, 2019, 8:35 a.m.