rep_analyses_auto: Reputation Analyses (automatic)

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

Description

This is a wrapper function around the reputation model functions.

Usage

rep_analyses_auto(data, p1_reports, p2_reports, target_self = NULL,
  p1_meta = NULL, p2_meta = NULL, 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

data	The dataframe that contains ratings (P1, P2, target self-report, P1, and P2 meta-perceptions). Data should be wide, with a row for every group of participants. At a minimum, it must contain two columns: one for P1 reports and one for P2 reports.
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	Optional. Quoted column names that contain target self-reports. If more than one is supplied, the target-wise order must match the other rating types.
p1_meta	Optional. Quoted column names that contain P1 3rd person Meta-perceptions, or P1's ratings of how they think P2 sees the target. If more than one is supplied, the target-wise order must match the other rating types.
p2_meta	Optional. Quoted column names that contain P2 3rd person Meta-perceptions, or P2's ratings of how they think P1 sees the target. If more than one is supplied, the target-wise order must match the other rating types.
n_triads	The number of exhangeable 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 manually.
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.

Details

This chooses a function depending on which variables are supplied. At a minimum, it requires a P1- & P2-ratings. It can optionally take target self-reports and third-person meta-perceptions (for P1 and P2). It fits a model estimating the possible hearsay reputation parameters given the input. See specific model functions for which parameters are estimated for each model.

The function can handle up to n exchangeable triads.

Value

The function returns an object of class lavaan.

Examples

data("rep_sim_data")
 rep_analyses_auto(data = rep_sim_data,
                  p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
                  p2_reports = c("B_C_agreeableness", "D_A_agreeableness"))

 rep_analyses_auto(data = rep_sim_data,
                  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"))

 rep_analyses_auto(data = rep_sim_data,
                  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"),
                  p1_meta = c("A_B_C_agree_meta", "C_D_A_agree_meta"),
                  p2_meta = c("B_A_C_agree_meta", "D_C_A_agree_meta"))

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