rep_con_acc_group_mod_builder: Reputation Consensus & Accuracy with Group Moderator Model...

Description Usage Arguments Value Examples

Description

This builds a model of lavaan syntax for estimating the possible hearsay reputation parameters as a multi-group path model given vectors of P1, P2, and target self-reports (vectors of quoted variable names) and a group-level categorical variable. The baseline model estimates each parameter seperately, labelling parameters based on the labels of moderator. Those parameters are:

hc

hearsay consensus; the correlation between P1(T) & P2(T)

ha

hearsay accuracy; the correation between P2(T) & T(T)

da

direct accuracy; the correlation between P1(T) & T(T)

int_p1

Intercept for P1(T)

int_p2

Intercept for P2(T)

int_self

Intercept for T(T)

v_p1

variance for P1(T)

v_p2

variance for P2(T)

v_self

variance for T(T)

p1_p2_rel_el

P1-P2 Relative Elevation (i.e., Mean P1(T) - Mean P2(T))

self_p2_rel_el

Self-P2 Relative Elevation (i.e., Mean T(T) - Mean P2(T))

self_p1_rel_el

Self-P1 Relative Elevation (i.e., Mean T(T) - Mean P1(T))

If n exchangeable triads > 1:

rec

direct reciprocity; the correlation between opposit P1(T)s (e.g., A(C) <-> C(A))

h

hearsay reciprocity; the correlation between exchangeable P2(T)s (e.g., B(C) <-> D(A))

m

unnamed parameter; The correlation between P2(T) and the opposite P1(T) in a group. (e.g., B(C) <-> C(A))

tru_sim

True Similarity; the correlation between targets' self-reports. (e.g., A(A) <-> C(C))

as_sim_3p

Third-person assumed similarity; correlation between P2(T) and P1's self-report (e.g., B(C) <- A(A))

as_sim_1p

First-person assumed similarity (i.e., interpersonal assumed similarity); correlation betweenP1(T) and P1's self-report (e.g., A(C) <-> A(A))

The function can handle up to n exchangeable triads.

Usage

1
2
3
4
rep_con_acc_group_mod_builder(p1_reports, p2_reports, target_self,
  groups = NULL, use_labs = TRUE, n_triads = length(p1_reports),
  n_p1s_per_p2s = 1, n_p2s_per_p1s = 1, n_p1s_per_ts = 1,
  n_p2s_per_ts = 1, n_ts_per_p1s = 1, n_ts_per_p2s = 1)

Arguments

p1_reports

Quoted column names that contain P1 reports, or ratings made by the person that knows the target directly. If more than one is supplied, the target-wise order must match the other rating types.

p2_reports

Quoted column names that contain P2 reports, or ratings made by the person that knows the target indirectly through the corresponding P1. If more than one is supplied, the target-wise order must match the other rating types.

target_self

Quoted column names that contain target self-reports. If more than one is supplied, the target-wise order must match the other rating types.

groups

Vector of quoted group labels (from group-level categorical moderator).

use_labs

Logical indicating whether or not to use the group labels to create the parameter labels. If FALSE, generic labels (grp1 to grpk, where k is the number of groups) are used.

n_triads

The number of exchangeable triads in each group. By default, this is determined by counting the number of P1 reports. It is rare that this parameter would need to be changed.

n_p1s_per_p2s

The number of P1s for every P2. This defaults to 1. Currently, only values of 1 are supported.

n_p2s_per_p1s

The number of P2s for every P1;. This defaults to 1. Currently, only values of 1 are supported.

n_p1s_per_ts

The number of P1s for every target;. This defaults to 1. Currently, only values of 1 are supported.

n_p2s_per_ts

The number of P2s for every target;. This defaults to 1. Currently, only values of 1 are supported.

n_ts_per_p1s

The number of targets for every P1;. This defaults to 1. Currently, only values of 1 are supported.

n_ts_per_p2s

The number of targets for every P2;. This defaults to 1. Currently, only values of 1 are supported.

Value

The function returns a list containing an object of class tbl_df with model information 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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
 # build the model
  agree_con_acc_model_grpmod <- rep_con_acc_group_mod_builder(p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
                                                              p2_reports = c("B_C_agreeableness", "D_A_agreeableness"),
                                                              target_self = c("C_C_agreeableness", "A_A_agreeableness"),
                                                              groups = c("Study_1", "Study_2"))

 # view the model
 agree_con_acc_model_grpmod$model

 # view the model information
 # agree_con_acc_model_grpmod$rep_model_info

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