R/reputation_model.R

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

# This file contains the functions for building and fitting the unmoderated reputation models.

#' Reputation Consensus Model Builder
#'
#' This takes a vector of P1 and P2 reports and builds
#' a model of lavaan syntax for estimating the possible hearsay reputation parameters.
#' Those parameters are:
#' \describe{
#' \item{hc}{hearsay consensus; the correlation between P1(T) & P2(T)}
#' \item{int_p1}{Intercept for P1(T)}
#' \item{int_p2}{Intercept for P2(T)}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{p1_p2_rel_el}{P1-P2 Relative Elevation (i.e., Mean P1(T) - Mean P2(T))}
#' }
#' \emph{If n exchangeable triads > 1:}
#' \describe{
#' \item{rec}{direct reciprocity; the correlation between opposit P1(T)s (e.g., A(C) <-> C(A))}
#' \item{h}{hearsay reciprocity; the correlation between exchangeable P2(T)s (e.g., B(C) <-> D(A))}
#' \item{m}{unnamed parameter; The correlation between P2(T) and the opposite P1(T) in a group. (e.g., B(C) <-> C(A))}
#' }
#' The function can handle up to n exchangeable triads.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param n_p1s_per_p2s The number of P1s for every P2. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_p1s The number of P2s for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @import magrittr stringr lavaan
#' @export
#' @examples
#'          # build the model
#'           agree_consensus_model <- rep_consensus_builder(p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#'           p2_reports = c("B_C_agreeableness", "D_A_agreeableness"))
#'
#'          # view the model
#'          agree_consensus_model$model
#'
#'          # view the model information
#'          # agree_consensus_model$rep_model_info
#'
#' @return The function returns a list containing an
#' object of class \code{\link[tibble:tbl_df-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.

rep_consensus_builder <- function(p1_reports, p2_reports, n_triads = length(p1_reports),
                                      n_p1s_per_p2s = 1, n_p2s_per_p1s = 1){
  if(n_triads > 0 &
     n_p1s_per_p2s == 1 &
     n_p2s_per_p1s == 1){

    # code for 1 triad - much simpler
    if(n_triads == 1){

      model <-
        # hearsay (P1-P2) consensus
        paste(paste(p1_reports, "~~ hc*", p2_reports),
              # intercepts
              paste(p1_reports, "~ int_p1*1"),
              paste(p2_reports, "~ int_p2*1"),

              # variances
              paste(p1_reports, "~~ v_p1*", p1_reports),

              paste(p2_reports, " ~~ v_p2*", p2_reports),

              # P1-P2 relative elevation
              "p1_p2_rel_el := 1*int_p1 - (1*int_p2)", sep = "\n")}

    if(n_triads > 1 &
       n_p1s_per_p2s == 1 &
       n_p2s_per_p1s == 1){
      # create empty model
      model <- ""
      for(i in 1:n_triads){
        # cross-target correlations
        if(i < n_triads){
          prev_i <- i:1
          m   <- paste(p1_reports[i], "~~ m*", p2_reports[-prev_i]) %>% stringr::str_flatten(collapse = "\n")
          rec <- paste(p1_reports[i], "~~ rec*", p1_reports[-prev_i]) %>% stringr::str_flatten(collapse = "\n")
          h   <-  paste(p2_reports[i], "~~ h*", p2_reports[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          xtrs <- paste(m, rec, h, sep = "\n")
          model <- paste(model, xtrs)}}
      hc <- paste(p1_reports, "~~ hc*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      int_p1 <- paste(p1_reports, "~ int_p1*1") %>% stringr::str_flatten(collapse = "\n")
      int_p2 <- paste(p2_reports, "~ int_p2*1") %>% stringr::str_flatten(collapse = "\n")
      v_p1 <- paste(p1_reports, "~~ v_p1*", p1_reports) %>% stringr::str_flatten(collapse = "\n")
      v_p2 <- paste(p2_reports, " ~~ v_p2*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      #  P1-P2 relative elevation
      p1p2_el <- "p1_p2_rel_el := 1*int_p1 - (1*int_p2)"

      model <- paste(hc, model,  int_p1, int_p2, v_p1, v_p2, p1p2_el, sep = "\n")}
 # Put the model info together.
  rep_model_info <- tibble::as_tibble(list(model_type = "Simple Hearsay Consensus (P1-P2)",
                                         ex_triads = n_triads,
                                         p1s_per_p2s = n_p1s_per_p2s,
                                         p2s_per_p1s = n_p2s_per_p1s))
  return(list(model = model,
              rep_model_info = rep_model_info)) }
  if(n_p1s_per_p2s > 1){print("I'm sorry, this function can only handle designs with 1 P1 per P2; check back for changes")}
  if(n_p2s_per_p1s > 1){print("I'm sorry, this function can only handle designs with 1 P1 per P2; check back for changes")}
}
#' Reputation Consensus Model
#'
#' This fits a model estimating the possible hearsay reputation parameters from
#' a combination of P1- and P2-reports (no self-reports or accuracy criterion). It requires a
#' dataframe and either a model from the relevant model builder function or names of columns
#' with P1- and P2- ratings.
#' The estimated parameters are:
#' \describe{
#' \item{hc}{hearsay consensus; the correlation between P1(T) & P2(T)}
#' \item{int_p1}{Intercept for P1(T)}
#' \item{int_p2}{Intercept for P2(T)}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{p1_p2_rel_el}{P1-P2 Relative Elevation (i.e., Mean P1(T) - Mean P2(T))}
#' }
#' \emph{If n exchangeable triads > 1:}
#' \describe{
#' \item{rec}{direct reciprocity; the correlation between opposit P1(T)s (e.g., A(C) <-> C(A))}
#' \item{h}{hearsay reciprocity; the correlation between exchangeable P2(T)s (e.g., B(C) <-> D(A))}
#' \item{m}{unnamed parameter; The correlation between P2(T) and the opposite P1(T) in a group. (e.g., B(C) <-> C(A))}
#' }
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains P1 and P2 ratings.
#' 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.
#' @param model Optional. A model from the corresponding ReputationModelR model builder function. If this
#' is supplied, no additional arguments need to be specified.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param n_p1s_per_p2s The number of P1s for every P2. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_p1s The number of P2s for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'           agree_consensus <- rep_consensus(data = rep_sim_data,
#'                                            p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#'                                            p2_reports = c("B_C_agreeableness", "D_A_agreeableness"))
#'          # alternatively
#'          # build the model
#'           agree_consensus_model <- rep_consensus_builder(p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#'                                                              p2_reports = c("B_C_agreeableness", "D_A_agreeableness"))
#'          # then fit it
#'          agree_consensus <- rep_consensus(data = rep_sim_data,
#'                                           agree_consensus_model)
#'
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.

rep_consensus <- function(data, model = NULL, p1_reports, p2_reports, n_triads = length(p1_reports),
                          n_p1s_per_p2s = 1, n_p2s_per_p1s = 1){
  if(is.null(model)){
  rep_consensus_model <- rep_consensus_builder(p1_reports, p2_reports, n_triads = length(p1_reports),
                            n_p1s_per_p2s = 1, n_p2s_per_p1s = 1)}

  else{rep_consensus_model <- model}
    fitted_model <- lavaan::sem(rep_consensus_model$model, data = data, missing = "FIML")
    return(fitted_model)}

# rep_consensus_accuracy documentation

#' Reputation Consensus & Accuracy Model Builder
#'
#' This takes a vector of P1-, P2-, and self-reports and builds
#' a model of lavaan syntax for estimating the possible hearsay reputation parameters.
#' Those parameters are:
#' \describe{
#' \item{hc}{hearsay consensus; the correlation between P1(T) & P2(T)}
#' \item{int_p1}{Intercept for P1(T)}
#' \item{int_p2}{Intercept for P2(T)}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{p1_p2_rel_el}{P1-P2 Relative Elevation (i.e., Mean P1(T) - Mean P2(T))}
#' }
#' \emph{If n exchangeable triads > 1:}
#' \describe{
#' \item{rec}{direct reciprocity; the correlation between opposit P1(T)s (e.g., A(C) <-> C(A))}
#' \item{h}{hearsay reciprocity; the correlation between exchangeable P2(T)s (e.g., B(C) <-> D(A))}
#' \item{m}{unnamed parameter; The correlation between P2(T) and the opposite P1(T) in a group. (e.g., B(C) <-> C(A))}
#' }
#' The function can handle up to n exchangeable triads.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param n_p1s_per_p2s The number of P1s for every P2. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_p1s The number of P2s for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p1s_per_ts The number of P1s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p1s The number of targets for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p2s The number of targets for every P2;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @import magrittr stringr lavaan
#' @export
#' @examples
#' agree_con_acc_model <- rep_consensus_accuracy_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"))
#' # view model
#' agree_con_acc_model$model
#'
#' # View model information
#' agree_con_acc_model$rep_model_info
#'
#' @return The function returns a list containing an
#' object of class \code{\link[tibble:tbl_df-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, the number of p2s per target, and the number of targets per p2s,
#' the number of targets per p1s, and the number of p1s per target.

rep_consensus_accuracy_builder <- function(p1_reports, p2_reports, target_self, 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){
  if(n_triads > 0 &
     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 ){

    # code for 1 triad - much simpler
    if(n_triads == 1){

      model <-
        # hearsay (P1-P2) consensus
        paste(paste(p1_reports, "~~ hc*", p2_reports),
              paste(target_self, "~~ ha*", p2_reports),
              paste(target_self, "~~ da*", p1_reports),
              # intercepts
              paste(p1_reports, "~ int_p1*1"),
              paste(p2_reports, "~ int_p2*1"),
              paste(target_self, "~ int_self*1"),

              # variances
              paste(p1_reports, "~~ v_p1*", p1_reports),
              paste(p2_reports, " ~~ v_p2*", p2_reports),
              paste(target_self, " ~~ v_self*", target_self),

              # P1-P2 relative elevation
              "p1_p2_rel_el := 1*int_p1 - (1*int_p2)",
              "self_p2_rel_el := 1*int_self - (1*int_p2)",
              "self_p1_rel_el := 1*int_self - (1*int_p1)", sep = "\n")}

    if(n_triads > 1 &
       n_p1s_per_p2s == 1 &
       n_p2s_per_p1s == 1){
      # create empty model
      model <- ""
      for(i in 1:n_triads){
        # cross-target correlations
        if(i < n_triads){
          prev_i <- i:1

          m   <- paste(p1_reports[i], "~~ m*", p2_reports[-prev_i]) %>% stringr::str_flatten(collapse = "\n")
          rec <- paste(p1_reports[i], "~~ rec*", p1_reports[-prev_i]) %>% stringr::str_flatten(collapse = "\n")
          h   <-  paste(p2_reports[i], "~~ h*", p2_reports[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          tru_sim <- paste(target_self[i], "~~ tru_sim*", target_self[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          as_sim_3p <- paste(p2_reports[i], "~~ as_sim_3p*", target_self[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          as_sim_1p <- paste(p1_reports[i], "~~ as_sim_1p*", target_self[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          xtrs <- paste(m, rec, h, tru_sim, as_sim_3p, as_sim_1p, sep = "\n")
          model <- paste(model, xtrs)}}

      hc <- paste(p1_reports, "~~ hc*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      ha <- paste(target_self, "~~ ha*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      da <- paste(target_self, "~~ da*", p1_reports) %>% stringr::str_flatten(collapse = "\n")

      int_p1 <- paste(p1_reports, "~ int_p1*1") %>% stringr::str_flatten(collapse = "\n")
      int_p2 <- paste(p2_reports, "~ int_p2*1") %>% stringr::str_flatten(collapse = "\n")
      int_self <- paste(target_self, "~ int_self*1") %>% stringr::str_flatten(collapse = "\n")
      v_p1 <- paste(p1_reports, "~~ v_p1*", p1_reports) %>% stringr::str_flatten(collapse = "\n")
      v_p2 <- paste(p2_reports, " ~~ v_p2*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      v_self <- paste(target_self, " ~~ v_self*", target_self) %>% stringr::str_flatten(collapse = "\n")
      #  P1-P2 relative elevation
      p1p2_el <- "p1_p2_rel_el := 1*int_p1 - (1*int_p2)"
      selfp2_el <- "self_p2_rel_el := 1*int_self - (1*int_p2)"
      selfp1_el <- "self_p1_rel_el := 1*int_self - (1*int_p1)"

      model <- paste(hc, ha, da, model,  int_self, int_p1,
                     int_p2, v_self, v_p1, v_p2, p1p2_el, selfp2_el, selfp1_el, sep = "\n")}

    rep_model_info <- tibble::as_tibble(list(model_type = "Full Triadic Model: Hearsay Consensus and Accuracy",
                                             ex_triads = n_triads,
                                             p1s_per_p2s = n_p1s_per_p2s,
                                             p2s_per_p1s = n_p2s_per_p1s,
                                             p1s_per_ts = n_p1s_per_ts,
                                             p2s_per_ts = n_p2s_per_ts,
                                             ts_per_p1s = n_ts_per_p1s,
                                             ts_per_p2s = n_ts_per_p2s))
    return(list(model = model,
                rep_model_info = rep_model_info)) }
  if(n_p1s_per_p2s > 1){print("I'm sorry, this function can only handle designs with 1 P1 per P2; check back for changes")}
  if(n_p2s_per_p1s > 1){print("I'm sorry, this function can only handle designs with 1 P1 per P2; check back for changes")}
  if(n_ts_per_p1s  > 1){print("I'm sorry, this function can only handle designs with 1 target per P1; check back for changes")}
  if(n_ts_per_p2s  > 1){print("I'm sorry, this function can only handle designs with 1 target per P2; check back for changes")}
  if(n_p1s_per_ts > 1){print("I'm sorry, this function can only handle designs with 1 P1 per target; check back for changes")}
  if(n_p2s_per_ts > 1){print("I'm sorry, this function can only handle designs with 1 P2 per target; check back for changes")}
}

#' Reputation Consensus & Accuracy Model
#'
#' This takes a dataset containing P1-, P2-, and target self-reports and either the
#' names of columns containing those reports or a model from one of the ReputationModelR
#' model builder function, and fits a model estimating the possible hearsay reputation parameters.
#' Those parameters are:
#' \describe{
#' \item{hc}{hearsay consensus; the correlation between P1(T) & P2(T)}
#' \item{ha}{hearsay accuracy; the correation between P2(T) & T(T)}
#' \item{da}{direct accuracy; the correlation between P1(T) & T(T)}
#' \item{int_p1}{Intercept for P1(T)}
#' \item{int_p2}{Intercept for P2(T)}
#' \item{int_self}{Intercept for T(T)}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_self}{variance for T(T)}
#' \item{p1_p2_rel_el}{P1-P2 Relative Elevation (i.e., Mean P1(T) - Mean P2(T))}
#' \item{self_p2_rel_el}{Self-P2 Relative Elevation (i.e., Mean T(T) - Mean P2(T))}
#' \item{self_p1_rel_el}{Self-P1 Relative Elevation (i.e., Mean T(T) - Mean P1(T))}
#' }
#' \emph{If n exchangeable triads > 1:}
#' \describe{
#' \item{rec}{direct reciprocity; the correlation between opposit P1(T)s (e.g., A(C) <-> C(A))}
#' \item{h}{hearsay reciprocity; the correlation between exchangeable P2(T)s (e.g., B(C) <-> D(A))}
#' \item{m}{unnamed parameter; The correlation between P2(T) and the opposite P1(T) in a group. (e.g., B(C) <-> C(A))}
#' \item{tru_sim}{True Similarity; the correlation between targets' self-reports. (e.g., A(A) <-> C(C))}
#' \item{as_sim_3p}{Third-person assumed similarity; correlation between P2(T) and P1's self-report (e.g., B(C) <- A(A))}
#' \item{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.
#' @param data The dataframe.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain three columns: one for P1-reports, one for P2-reports, and
#' one for targets' self-ratings.
#' @param model Optional. A model from the corresponding ReputationModelR model builder function. If this
#' is supplied, no additional arguments need to be specified.
#' @param  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.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param n_p1s_per_p2s The number of P1s for every P2. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_p1s The number of P2s for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p1s_per_ts The number of P1s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p1s The number of targets for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p2s The number of targets for every P2;. This defaults to 1.
#' Currently, only values of 1 are supported.
#'
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'           agree_con_acc <- rep_consensus_accuracy(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"))
#'          # alternatively
#'          agree_con_acc_model <- rep_consensus_accuracy_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"))
#'
#'          agree_con_acc <- rep_consensus_accuracy(data = rep_sim_data,
#'                                                  model = agree_con_acc_model)
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_consensus_accuracy <- function(data, model = NULL, p1_reports, p2_reports, target_self, 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){
  if(is.null(model)){
    rep_consensus_accuracy_model <- rep_consensus_accuracy_builder(p1_reports, p2_reports, target_self,
                                                              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)
  }
  else{rep_consensus_accuracy_model <- model}
  fitted_model <- lavaan::sem(rep_consensus_accuracy_model$model, data = data, missing = "FIML")
  return(fitted_model)
}


#' Reputation Consensus, Accuracy, and 3rd-Person Meta-Perception Model Builder
#'
#' This takes a vector of P1-, P2-, target self-reports, and
#' third-person meta-perceptions (for P1 and P2) and builds
#' a model estimating the possible hearsay reputation parameters in lavaan syntax.
#' Those parameters are:
#' \describe{
#' \item{hc}{hearsay consensus; the correlation between P1(T) & P2(T)}
#' \item{ha}{hearsay accuracy; the correation between P2(T) & T(T)}
#' \item{da}{direct accuracy; the correlation between P1(T) & T(T)}
#' \item{p1ma}{P1 Meta-Accuracy; the correlation between P1(P2(T)) & P2(T)}
#' \item{p2ma}{P2 Meta-Accuracy; the correlation between P2(P1(T)) & P1(T)}
#' \item{as_ac1}{ P1 Assumed Accuracy; the correlation between P1(P2(T)) & T(T)}
#' \item{as_con1}{P1 Assumed Consensus; the correlation between P1(P2(T)) & P1(T)}
#' \item{mp_rec}{ Meta-Perception Reciprocity; the correlation between P1(P2(T)) & P2(P1(T))}
#' \item{as_ac2 }{P2 Assumed Accuracy; the correlation between P2(P1(T)) & T(T)}
#' \item{as_con2}{P2 Assumed Consensus; the correlation between P2(P1(T)) & P2(T)}
#' \item{int_p1}{Intercept for P1(T)}
#' \item{int_p2}{Intercept for P2(T)}
#' \item{int_self}{Intercept for T(T)}
#' \item{int_mp1}{Intercept for P1(P2(T))}
#' \item{int_mp2}{Intercept for P2(P1(T))}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_self}{variance for T(T)}
#' \item{v_mp1}{variance for P1(P2(T))}
#' \item{v_mp2}{variance for P2(P1(T))}
#' \item{p1_p2_rel_el}{P1-P2 Relative Elevation (i.e., Mean P1(T) - Mean P2(T))}
#' \item{self_p2_rel_el}{Self-P2 Relative Elevation (i.e., Mean T(T) - Mean P2(T))}
#' \item{self_p1_rel_el}{Self-P1 Relative Elevation (i.e., Mean T(T) - Mean P1(T))}
#' \item{p1_meta_rel_el}{P1 Meta Relative Elevation (i.e., mean P2(T) - Mean P1(P2(T)))}
#' \item{p2_meta_rel_el}{P2 Meta Relative Elevation (i.e., mean P1(T) - Mean P2(P1(T)))}
#' }
#' \emph{If n exchangeable triads > 1:}
#' \describe{
#' \item{rec}{direct reciprocity; the correlation between opposit P1(T)s (e.g., A(C) <-> C(A))}
#' \item{h}{hearsay reciprocity; the correlation between exchangeable P2(T)s (e.g., B(C) <-> D(A))}
#' \item{m}{unnamed parameter; The correlation between P2(T) and the opposite P1(T) in a group. (e.g., B(C) <-> C(A))}
#' \item{tru_sim}{True Similarity; the correlation between targets' self-reports. (e.g., A(A) <-> C(C))}
#' \item{as_sim_3p}{Third-person assumed similarity; correlation between P2(T) and P1's self-report (e.g., B(C) <- A(A))}
#' \item{as_sim_1p}{First-person assumed similarity (i.e., interpersonal assumed similarity); correlation between P1(T) and P1's self-report (e.g., A(C) <-> A(A))}
#' \item{as_sim_p1m}{P1 Meta-assumed similarity (e.g., A(B(C)) <-> A(A))}
#' \item{ukp1m1}{unknown p1-meta 1}{P1 meta-perception with opposite P1-report (e.g., A(B(C)) <-> C(A))).}
#' \item{p1meta_sim}{P1 Meta-Similarity}{correlation between exchangeable P1 meta-perceptions (e.g., A(B(C)) <-> C(D(A))).}
#' \item{ukp2m1}{unknown P2-meta 1}{P2 Meta-perception with exchangeable P2-reports (e.g., B(A(C)) <-> D(A))}
#' \item{ukp2m2}{unknown P2-meta 2}{P2 Meta-perception with exchangeable target self-report (e.g., B(A(C) <-> A(A)))}
#' \item{ukp2m3}{unknown P2-meta 3}{P2 Meta-perception with exchangeable P1-reports (e.g., B(A(C)) <-> C(A))}
#' \item{p2meta_sim}{P2 Meta-Similarity}{correlation between exchangeable P2 meta-perceptions (e.g., B(A(C)) <-> D(C(A))).}
#' \item{ukm1}{unknown Meta-perception}{P1 Meta-perception with exchangeable P2 Meta-Perception (e.g., A(B(C)) <-> D(C(A)))}}
#'
#' The function can handle up to n exchangeable triads.
#' @param  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.
#' @param 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.
#' @param 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.
#' @param  p1_meta 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.
#' @param p2_meta 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.
#' @param 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.
#' @param n_p1s_per_p2s The number of P1s for every P2. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_p1s The number of P2s for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p1s_per_ts The number of P1s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p1s The number of targets for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p2s The number of targets for every P2;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'           rep_full_3pmeta_model <- rep_full_w_3pmeta_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"),
#'                             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"))
#' @return The function returns a list containing an
#' object of class \code{\link[tibble:tbl_df-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, the number of p2s per target, and the number of targets per p2s,
#' the number of targets per p1s, and the number of p1s per target.

rep_full_w_3pmeta_builder <- function(p1_reports, p2_reports, target_self, p1_meta, p2_meta,
                                          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){
  if(n_triads > 0 &
     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 ){

    # code for 1 triad - much simpler
    if(n_triads == 1){

      model <-
        # hearsay (P1-P2) consensus
        paste(paste(p1_reports, "~~ hc*", p2_reports),
              paste(target_self, "~~ ha*", p2_reports),
              paste(target_self, "~~ da*", p1_reports),
              paste(p1_meta, "~~p1ma*", p2_reports),
              paste(p2_meta, "~~p2ma*", p1_reports),
              paste(p1_meta, "~~as_ac1*", target_self),
              paste(p1_meta, "~~as_con1*", p1_reports),
              paste(p1_meta, "~~mp_rec*", p2_meta),
              paste(p2_meta, "~~as_ac2*", target_self),
              paste(p2_meta, "~~as_con2*", p2_reports),

              # intercepts
              paste(p1_reports, "~ int_p1*1"),
              paste(p2_reports, "~ int_p2*1"),
              paste(target_self, "~ int_self*1"),
              paste(p1_meta, "~ int_mp1*1"),
              paste(p2_meta, "~ int_mp2*1"),

              # variances
              paste(p1_reports, "~~ v_p1*", p1_reports),
              paste(p2_reports, "~~ v_p2*", p2_reports),
              paste(target_self, "~~ v_self*", target_self),
              paste(p1_meta, "~~ v_mp1*", p1_meta),
              paste(p2_meta, "~~ v_mp2*", p2_meta),

              # P1-P2 relative elevation
              "p1_p2_rel_el   := 1*int_p1 - (1*int_p2)",
              "self_p2_rel_el := 1*int_self - (1*int_p2)",
              "self_p1_rel_el := 1*int_self - (1*int_p1)",
              "p1_meta_rel_el := 1*int_p2 - (1*int_mp1)",
              "p2_meta_rel_el := 1*int_p1 - (1*int_mp2)", sep = "\n")}

    if(n_triads > 1 &
       n_p1s_per_p2s == 1 &
       n_p2s_per_p1s == 1){
      # create empty model
      model <- ""
      for(i in 1:n_triads){
        # cross-target correlations
        if(i < n_triads){
          prev_i <- i:1

          m   <- paste(p1_reports[i], "~~ m*", p2_reports[-prev_i]) %>% stringr::str_flatten(collapse = "\n")
          rec <- paste(p1_reports[i], "~~ rec*", p1_reports[-prev_i]) %>% stringr::str_flatten(collapse = "\n")
          h   <-  paste(p2_reports[i], "~~ h*", p2_reports[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          tru_sim <- paste(target_self[i], "~~ tru_sim*", target_self[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          as_sim_3p <- paste(p2_reports[i], "~~ as_sim_3p*", target_self[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          as_sim_1p <- paste(p1_reports[i], "~~ as_sim_1p*", target_self[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          as_sim_p1m <- paste(p1_meta[i], "~~ as_sim_p1m*", target_self[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          ukp1m1 <- paste(p1_meta[i], "~~ ukp1m1*", p1_reports[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          p1meta_sim <- paste(p1_meta[i], "~~ p1meta_sim*", p1_meta[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          ukp2m1 <- paste(p2_meta[i], "~~ ukp2m1*", p2_reports[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          ukp2m2 <- paste(p2_meta[i], "~~ ukp2m2*", target_self[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          ukp2m3 <- paste(p2_meta[i], "~~ ukp2m3*", p1_reports[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          p2meta_sim <- paste(p2_meta[i], "~~ p2meta_sim*", p2_meta[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          ukm1 <- paste(p1_meta[i], "~~ ukm1*", p2_meta[-prev_i], "\n") %>% stringr::str_flatten(collapse = "\n")
          xtrs <- paste(m, rec, h, tru_sim, as_sim_3p, as_sim_1p,
                        ukp1m1, p1meta_sim, ukp2m1, ukp2m2, ukp2m3, p2meta_sim, ukm1, sep = "\n")
          model <- paste(model, xtrs)}}

      hc <- paste(p1_reports, "~~ hc*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      ha <- paste(target_self, "~~ ha*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      da <- paste(target_self, "~~ da*", p1_reports) %>% stringr::str_flatten(collapse = "\n")
      p1ma <- paste(p1_meta, "~~p1ma*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      p2ma <- paste(p2_meta, "~~p2ma*", p1_reports) %>% stringr::str_flatten(collapse = "\n")
      as_ac1 <- paste(p1_meta, "~~as_ac1*", target_self) %>% stringr::str_flatten(collapse = "\n")
      as_con1 <- paste(p1_meta, "~~as_con1*", p1_reports) %>% stringr::str_flatten(collapse = "\n")
      mp_rec <- paste(p1_meta, "~~mp_rec*", p2_meta) %>% stringr::str_flatten(collapse = "\n")
      as_ac2  <- paste(p2_meta, "~~as_ac2*", target_self) %>% stringr::str_flatten(collapse = "\n")
      as_con2 <- paste(p2_meta, "~~as_con2*", p2_reports) %>% stringr::str_flatten(collapse = "\n")

      int_p1 <- paste(p1_reports, "~ int_p1*1") %>% stringr::str_flatten(collapse = "\n")
      int_p2 <- paste(p2_reports, "~ int_p2*1") %>% stringr::str_flatten(collapse = "\n")
      int_self <- paste(target_self, "~ int_self*1") %>% stringr::str_flatten(collapse = "\n")
      int_mp1 <- paste(p1_meta, "~ int_mp1*1") %>% stringr::str_flatten(collapse = "\n")
      int_mp2 <- paste(p2_meta, "~ int_mp2*1") %>% stringr::str_flatten(collapse = "\n")

      v_p1 <- paste(p1_reports, "~~ v_p1*", p1_reports) %>% stringr::str_flatten(collapse = "\n")
      v_p2 <- paste(p2_reports, " ~~ v_p2*", p2_reports) %>% stringr::str_flatten(collapse = "\n")
      v_mp1 <- paste(p1_meta, "~~ v_mp1*", p1_meta) %>% stringr::str_flatten(collapse = "\n")
      v_mp2 <- paste(p2_meta, "~~ v_mp2*", p2_meta) %>% stringr::str_flatten(collapse = "\n")
      v_self <- paste(target_self, " ~~ v_self*", target_self) %>% stringr::str_flatten(collapse = "\n")
      #  P1-P2 relative elevation
      p1p2_el <- "p1_p2_rel_el := 1*int_p1 - (1*int_p2)"
      selfp2_el <- "self_p2_rel_el := 1*int_self - (1*int_p2)"
      selfp1_el <- "self_p1_rel_el := 1*int_self - (1*int_p1)"
      p1_meta_el <- "p1_meta_rel_el := 1*int_p2 - (1*int_mp1)"
      p2_meta_el <- "p2_meta_rel_el := 1*int_p1 - (1*int_mp2)"
      model <- paste(hc, ha, da, p1ma, p2ma, as_ac1, as_con1,
                     mp_rec, as_ac2, as_con2, model, int_self, int_p1,
                     int_p2, int_mp1, int_mp2, v_self, v_p1, v_p2, v_mp1, v_mp2,
                     p1p2_el, selfp2_el, selfp1_el, p1_meta_el, p2_meta_el,
                     sep = "\n")}
    # fit model and print some informaiton about it
    rep_model_info <- tibble::as_tibble(list(model_type = "Full Triadic Model with 3rd Person Meta-Perceptions",
                                             ex_triads = n_triads,
                                             p1s_per_p2s = n_p1s_per_p2s,
                                             p2s_per_p1s = n_p2s_per_p1s,
                                             p1s_per_ts = n_p1s_per_ts,
                                             p2s_per_ts = n_p2s_per_ts,
                                             ts_per_p1s = n_ts_per_p1s,
                                             ts_per_p2s = n_ts_per_p2s))

    return(list(model = model,
                rep_model_info = rep_model_info))}
  if(n_p1s_per_p2s > 1){print("I'm sorry, this function can only handle designs with 1 P1 per P2; check back for changes")}
  if(n_p2s_per_p1s > 1){print("I'm sorry, this function can only handle designs with 1 P1 per P2; check back for changes")}
  if(n_ts_per_p1s  > 1){print("I'm sorry, this function can only handle designs with 1 target per P1; check back for changes")}
  if(n_ts_per_p2s  > 1){print("I'm sorry, this function can only handle designs with 1 target per P2; check back for changes")}
  if(n_p1s_per_ts > 1){print("I'm sorry, this function can only handle designs with 1 P1 per target; check back for changes")}
  if(n_p2s_per_ts > 1){print("I'm sorry, this function can only handle designs with 1 P2 per target; check back for changes")}
}

#' Reputation Consensus, Accuracy, and 3rd-Person Meta-Perception Model
#'
#' This fits a model estimating the possible hearsay reputation parameters for
#' a design with P1-, P2-, target-self-reports, and P1- and P2-Meta-perception reports. It requires a
#' dataset with those ratings, and either a model from the relevant model builder function or the names
#' of the columns with each rating type.
#' The estimated parameters are:
#' \describe{
#' \item{hc}{hearsay consensus; the correlation between P1(T) & P2(T)}
#' \item{ha}{hearsay accuracy; the correation between P2(T) & T(T)}
#' \item{da}{direct accuracy; the correlation between P1(T) & T(T)}
#' \item{p1ma}{P1 Meta-Accuracy; the correlation between P1(P2(T)) & P2(T)}
#' \item{p2ma}{P2 Meta-Accuracy; the correlation between P2(P1(T)) & P1(T)}
#' \item{as_ac1}{ P1 Assumed Accuracy; the correlation between P1(P2(T)) & T(T)}
#' \item{as_con1}{P1 Assumed Consensus; the correlation between P1(P2(T)) & P1(T)}
#' \item{mp_rec}{ Meta-Perception Reciprocity; the correlation between P1(P2(T)) & P2(P1(T))}
#' \item{as_ac2 }{P2 Assumed Accuracy; the correlation between P2(P1(T)) & T(T)}
#' \item{as_con2}{P2 Assumed Consensus; the correlation between P2(P1(T)) & P2(T)}
#' \item{int_p1}{Intercept for P1(T)}
#' \item{int_p2}{Intercept for P2(T)}
#' \item{int_self}{Intercept for T(T)}
#' \item{int_mp1}{Intercept for P1(P2(T))}
#' \item{int_mp2}{Intercept for P2(P1(T))}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_self}{variance for T(T)}
#' \item{v_mp1}{variance for P1(P2(T))}
#' \item{v_mp2}{variance for P2(P1(T))}
#' \item{p1_p2_rel_el}{P1-P2 Relative Elevation (i.e., Mean P1(T) - Mean P2(T))}
#' \item{self_p2_rel_el}{Self-P2 Relative Elevation (i.e., Mean T(T) - Mean P2(T))}
#' \item{self_p1_rel_el}{Self-P1 Relative Elevation (i.e., Mean T(T) - Mean P1(T))}
#' \item{p1_meta_rel_el}{P1 Meta Relative Elevation (i.e., mean P2(T) - Mean P1(P2(T)))}
#' \item{p2_meta_rel_el}{P2 Meta Relative Elevation (i.e., mean P1(T) - Mean P2(P1(T)))}
#' }
#' \emph{If n exchangeable triads > 1:}
#' \describe{
#' \item{rec}{direct reciprocity; the correlation between opposit P1(T)s (e.g., A(C) <-> C(A))}
#' \item{h}{hearsay reciprocity; the correlation between exchangeable P2(T)s (e.g., B(C) <-> D(A))}
#' \item{m}{unnamed parameter; The correlation between P2(T) and the opposite P1(T) in a group. (e.g., B(C) <-> C(A))}
#' \item{tru_sim}{True Similarity; the correlation between targets' self-reports. (e.g., A(A) <-> C(C))}
#' \item{as_sim_3p}{Third-person assumed similarity; correlation between P2(T) and P1's self-report (e.g., B(C) <- A(A))}
#' \item{as_sim_1p}{First-person assumed similarity (i.e., interpersonal assumed similarity); correlation between P1(T) and P1's self-report (e.g., A(C) <-> A(A))}
#' \item{as_sim_p1m}{P1 Meta-assumed similarity (e.g., A(B(C)) <-> A(A))}
#' \item{ukp1m1}{unknown p1-meta 1}{P1 meta-perception with opposite P1-report (e.g., A(B(C)) <-> C(A))).}
#' \item{p1meta_sim}{P1 Meta-Similarity}{correlation between exchangeable P1 meta-perceptions (e.g., A(B(C)) <-> C(D(A))).}
#' \item{ukp2m1}{unknown P2-meta 1}{P2 Meta-perception with exchangeable P2-reports (e.g., B(A(C)) <-> D(A))}
#' \item{ukp2m2}{unknown P2-meta 2}{P2 Meta-perception with exchangeable target self-report (e.g., B(A(C) <-> A(A)))}
#' \item{ukp2m3}{unknown P2-meta 3}{P2 Meta-perception with exchangeable P1-reports (e.g., B(A(C)) <-> C(A))}
#' \item{p2meta_sim}{P2 Meta-Similarity}{correlation between exchangeable P2 meta-perceptions (e.g., B(A(C)) <-> D(C(A))).}
#' \item{ukm1}{unknown Meta-perception}{P1 Meta-perception with exchangeable P2 Meta-Perception (e.g., A(B(C)) <-> D(C(A)))}
#'}
#' The function can handle up to n exchangeable triads.
#' @param 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 5 columns: 1 for each of the five rating types (P1-, P2-, self-,
#' P1-meta-, P2-meta-perceptions).
#' @param model Optional. A model from the corresponding ReputationModelR model builder function. If this
#' is supplied, no additional arguments need to be specified.
#' @param  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.
#' @param 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.
#' @param 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.
#' @param  p1_meta 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.
#' @param p2_meta 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.
#' @param 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.
#' @param n_p1s_per_p2s The number of P1s for every P2. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_p1s The number of P2s for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p1s_per_ts The number of P1s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p1s The number of targets for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p2s The number of targets for every P2;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @export
#' @examples data("rep_sim_data")
#'           agree_full <- rep_full_w_3pmeta(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"))
#'          # alternatively:
#'
#'          agree_full_model <- rep_full_w_3pmeta_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"),
#'                                                            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"))
#'          agree_full <- rep_full_w_3pmeta(data = rep_sim_data,
#'                                          model = agree_full_model)
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.

rep_full_w_3pmeta <- function(data, model = NULL, p1_reports, p2_reports, target_self, p1_meta, p2_meta,
                              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){
  if(is.null(model)){
    rep_full_w_3pmeta_model <- rep_full_w_3pmeta_builder(p1_reports, p2_reports, target_self,
                                                             p1_meta, p2_meta,
                                                             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)
  }
  else{rep_full_w_3pmeta_model <- model}
  fitted_model <- lavaan::sem(rep_full_w_3pmeta_model$model, data = data, missing = "FIML")
  return(fitted_model)
}

#' Reputation Analyses (automatic)
#'
#' This is a wrapper function around the reputation model functions.
#'
#' 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.
#'
#' @param 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.
#' @param  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.
#' @param 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.
#' @param 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.
#' @param  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.
#' @param 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.
#' @param 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.
#' @param n_p1s_per_p2s The number of P1s for every P2. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_p1s The number of P2s for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p1s_per_ts The number of P1s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s for every target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p1s The number of targets for every P1;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_ts_per_p2s The number of targets for every P2;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @export
#' @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"))
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.

rep_analyses_auto <- function(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){
  if(is.null(target_self) &
     is.null(p1_meta) &
     is.null(p2_meta)){
    fitted_model <- rep_consensus(data = data, p1_reports = p1_reports, p2_reports = p2_reports)
  }
  else if(is.null(p1_meta) &
          is.null(p2_meta)){
    fitted_model <- rep_consensus_accuracy(data = data, p1_reports = p1_reports, p2_reports = p2_reports,
                                           target_self = target_self)
  }
  else if(!is.null(target_self) &
          !is.null(p1_meta) &
          !is.null(p2_meta)){
    fitted_model <- rep_full_w_3pmeta(data = data, p1_reports = p1_reports,  p2_reports = p2_reports,
                                      target_self = target_self, p1_meta = p1_meta, p2_meta = p2_meta)
  }
  else {stop("You did not supply a workable combination of variables.")}
  return(fitted_model)
}