R/id_mods.R
In Coryc3133/ReputationAnalyses: Functions to model and analyze triadic (T-P1-P2) reputational data
Defines functions
rep_generic_id_mods_builder
rep_generic_id_mods
rep_consensus_id_mods_builder
rep_consensus_id_mods
rep_accuracy_id_mods_builder
rep_accuracy_id_mods
rep_auto_id_mods
Documented in
rep_accuracy_id_mods
rep_accuracy_id_mods_builder
rep_auto_id_mods
rep_consensus_id_mods
rep_consensus_id_mods_builder
rep_generic_id_mods
rep_generic_id_mods_builder
#' Individual level Moderators (Generic) Model Builder
#'
#' 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.
#'
#' The parameters for the individual-level moderator analyses are:
#'
#' \describe{
#' \item{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).}
#' \item{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.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which
#' the correlation between ratings depends on the moderator variable}
#' \item{v_rating_1}{variance for first rating}
#' \item{v_rating_2}{variance for second rating}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_rating_1}{intercept for first rating}
#' \item{int_rating_2}{intercept for second rating}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param 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.
#' @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_r1_per_r2 The number of first ratings for each second rating. Currently, only 1:1 is supported.
#' @param n_r2_per_r1 The number of second ratings for each first rating. Currently, only 1:1 is supported.
#' @import magrittr stringr lavaan
#' @export
#' @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
#'
#' @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_generic_id_mods_builder <- function(rating_1, rating_2, id_mod_variable,
interaction_term, n_triads = length(rating_1),
n_r1_per_r2 = 1, n_r2_per_r1 = 1){
if(n_triads > 0 &
n_r1_per_r2 == 1 &
n_r2_per_r1 == 1){
model <-
# moderated regression model
paste(paste(rating_1, "~", "rating_me*", rating_2, "+",
"mod_me*", id_mod_variable, "+",
"interaction*", interaction_term),
# variances
paste(rating_1, "~~ v_rating_1*", rating_1),
paste(rating_2, " ~~ v_rating_2*", rating_2),
paste(id_mod_variable, "~~ v_mod*", id_mod_variable),
paste(interaction_term, "~~ v_interaction*", interaction_term),
# intercepts
paste(rating_1, "~ int_rating_1*1"),
paste(rating_2, " ~ int_rating_2*1"),
paste(id_mod_variable, "~ int_mod*1"),
paste(interaction_term, "~ int_interaction*1"),
sep = "\n")
# Put the model info together.
rep_model_info <- tibble::as_tibble(list(model_type = "Individual-level moderator (generic function)",
ex_triads = n_triads,
r1_per_r2 = n_r1_per_r2,
r2_per_r1 = n_r2_per_r1))
return(list(model = model,
rep_model_info = rep_model_info))
}}
#' Individual level Moderators (Generic)
#'
#' This is a generic function 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. It requires a dataframe and either a model from the relevant model builder
#' function or names of columns for rating_1, rating_2, and id_mod_variable.
#' The estimated parameters are:
#'
#' \describe{
#' \item{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).}
#' \item{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.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which
#' the correlation between ratings depends on the moderator variable}
#' \item{v_rating_1}{variance for first rating}
#' \item{v_rating_2}{variance for second rating}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_rating_1}{intercept for first rating}
#' \item{int_rating_2}{intercept for second rating}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains the ratings, moderator variable, and the interaction term.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain four columns: two for ratings (of the same target),
#' one for the mean-centered moderator variable, and one for the interaction term.
#' @param model Optional. A model from the corresponding ReputationModelR model builder function. If this
#' is supplied, no additional arguments need to be specified.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param 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.
#' @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_r1_per_r2 The number of first ratings for each second rating. Currently, only 1:1 is supported.
#' @param n_r2_per_r1 The number of second ratings for each first rating. Currently, only 1:1 is supported.
#' @import magrittr stringr lavaan
#' @export
#' @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)
#'
#' # fit model by specifying the variables / column names
#' agree_pt_mod <- rep_generic_id_mods(data = moderator_data,
#' 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"))
#'
#' # alternatively, you can 'build a model' first
#' 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"))
#' # Then pass the built model on to the fit function
#' agree_pt_mod <- rep_generic_id_mods(data = moderator_data,
#' model = agree_pt_mod_model)
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_generic_id_mods <- function(data, model = NULL, rating_1, rating_2,
id_mod_variable,
interaction_term, n_triads = length(rating_1),
n_r1_per_r2 = 1,n_r2_per_r1 = 1){
# Global Variable Binding Issue
. <- NULL
if(is.null(model)){
rep_id_mods_model <- 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)
if(round(mean(colMeans(data[,rating_2], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
else{
# save model
rep_id_mods_model <- model
# Get rating_2 variable labels to check
# that data are mean-centered
rating_2 <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "rating_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# Fer id_mod variable labels to check
# that data were mean-centered
id_mod_variable <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "mod_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# check that data were mean-centered
if(round(mean(colMeans(data[,rating_2], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
fitted_model <- lavaan::sem(rep_id_mods_model$model, data = data, missing = "FIML", fixed.x = FALSE)
return(fitted_model)
}
#' Individual level Moderators of Hearsay Consensus Model Builder
#'
#' This takes the variables needed to assess an individual-level moderator on hearsay consensus, and builds
#' a model for lavaan estimating the corresponding parameters. At a minimum, it requires P1-reports, P2-reports,
#' an individual-level moderator, and the interaction term. Note that the P2-reports and moderator variable should
#' be mean-centered.
#'
#' The parameters for the individual-level moderator analyses are:
#'
#' \describe{
#' \item{hc_me}{hearsay consensus main effect; this should correspond to hearsay consensus at average
#' level of moderator variable (if data were properly mean-centered).}
#' \item{mod_me}{The meain effect of the moderator variable; it can be interpreted as the difference in
#' P1-reports related to differences in the individual-level moderator variable.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which hearsay consensus,
#' depends on the moderator variable}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_p1}{intercept for P1(T)}
#' \item{int_p2}{intercept for P2(T)}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' 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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param 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 triads, the target-wise order must match the order of the ratings.
#' @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")
#' # 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)
#'
#' # Build a consensus model
#' agree_pt_mod_consensus_model <- rep_consensus_id_mods_builder (p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = 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_consensus_model$model)
#'
#' # view model Information
#' agree_pt_mod_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_id_mods_builder <- function(p1_reports, p2_reports, id_mod_variable,
interaction_term, 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){
model <-
# moderated regression model
paste(paste(p1_reports, "~", "hc_me*", p2_reports, "+",
"mod_me*", id_mod_variable, "+",
"interaction*", interaction_term),
# variances
paste(p1_reports, "~~ v_p1*", p1_reports),
paste(p2_reports, " ~~ v_p2*", p2_reports),
paste(id_mod_variable, "~~ v_mod*", id_mod_variable),
paste(interaction_term, "~~ v_interaction*", interaction_term),
# intercepts
paste(p1_reports, "~ int_p1*1"),
paste(p2_reports, " ~ int_p2*1"),
paste(id_mod_variable, "~ int_mod*1"),
paste(interaction_term, "~ int_interaction*1"),
sep = "\n")
# Put the model info together.
rep_model_info <- tibble::as_tibble(list(model_type = "Individual-level moderator on Hearsay Consensus",
ex_triads = n_triads,
p1_per_p2 = n_p1s_per_p2s,
p2_per_p1 = n_p2s_per_p1s))
return(list(model = model,
rep_model_info = rep_model_info))
}
if(n_p1s_per_p2s > 1){warning("I'm sorry, this function can only handle designs with 1 P1 per P2; check back for changes")}
if(n_p2s_per_p1s > 1){warning("I'm sorry, this function can only handle designs with 1 P2 per P1; check back for changes")}
}
#' Individual level Moderators of Hearsay Consensus Model
#'
#' This function fits a model for individual-level moderator on hearsay consensus, and builds
#' a model for lavaan estimating the corresponding parameters.
#' It requires a dataframe and either a model from the relevant model builder
#' function or names of columns for p1_reports, p2_reports, and id_mod_variable.
#' The estimated parameters are:
#'
#' \describe{
#' \item{hc_me}{hearsay consensus main effect; this should correspond to hearsay consensus at average
#' level of moderator variable (if data were properly mean-centered).}
#' \item{mod_me}{The meain effect of the moderator variable; it can be interpreted as the difference in
#' P1-reports related to differences in the individual-level moderator variable.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which hearsay consensus,
#' depends on the moderator variable}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_p1}{intercept for P1(T)}
#' \item{int_p2}{intercept for P2(T)}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains the ratings, moderator variable, and the interaction term.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain four columns: one for P1 reports, one for mean-centered P2 reports,
#' one for the mean-centered moderator variable, and one for the interaction term.
#' @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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param 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 triads, the target-wise order must match the order of the ratings.
#' @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")
#' # 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)
#'
#' # Fitting by supplying variable/column names
#' agree_pt_mod <- rep_consensus_id_mods(data = moderator_data,
#' p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = 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"))
#'
#' # alternatively, build the model frst
#' agree_pt_mod_model <- rep_consensus_id_mods_builder (p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = 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"))
#' # Then fit the model you just built
#' agree_pt_mod <- rep_consensus_id_mods(data = moderator_data,
#' model = agree_pt_mod_model)
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_consensus_id_mods <- function(data, model = NULL, p1_reports, p2_reports, id_mod_variable,
interaction_term, n_triads = length(p1_reports),
n_p1s_per_p2s = 1, n_p2s_per_p1s = 1){
# dealing with global variable binding issue
. <- NULL
if(is.null(model)){
rep_id_mods_model <- rep_consensus_id_mods_builder(p1_reports, p2_reports, id_mod_variable,
interaction_term, n_triads = length(p1_reports),
n_p1s_per_p2s = n_p1s_per_p2s, n_p2s_per_p1s = n_p1s_per_p2s)
if(round(mean(colMeans(data[,p2_reports], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
else{
# save model
rep_id_mods_model <- model
# Get P2 reports variable labels to check
# that data are mean-centered
p2_reports <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "hc_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# Get id_mod variable labels to check
# that data were mean-centered
id_mod_variable <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "mod_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# check that data were mean-centered
if(round(mean(colMeans(data[,p2_reports], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
fitted_model <- lavaan::sem(rep_id_mods_model$model, data = data, missing = "FIML", fixed.x = FALSE)
return(fitted_model)
}
#' Individual level Moderators of Hearsay Accuracy Model Builder
#'
#' This takes the variables needed to assess an individual-level moderator on hearsay acuracy, and builds
#' a model for lavaan estimating the corresponding parameters. At a minimum, it requires target self-reports, P2-reports,
#' an individual-level moderator, and the interaction term.
#' Note that the P2-reports and moderator variable should be mean-centered.
#'
#' The parameters for the individual-level moderator analyses are:
#'
#' \describe{
#' \item{ha_me}{hearsay accuracy main effect; this should correspond to hearsay accuracy at average
#' level of moderator variable (if data were properly mean-centered).}
#' \item{mod_me}{The meain effect of the moderator variable; it can be interpreted as the difference in
#' target self-reports related to differences in the individual-level moderator variable.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which hearsay accuracy,
#' depends on the moderator variable}
#' \item{v_self}{variance for T(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_self}{intercept for T(T)}
#' \item{int_p2}{intercept for P2(T)}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param target_self Quoted column names that contain target self-reports.
#' If more than one is supplied, the 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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param 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 triads, the target-wise order must match the order of the ratings.
#' @param n_triads The number of exchangeable triads in each group. By default, this is determined by
#' counting the number of target self-reports. This parameter rarely needs to be changed.
#' @param n_ts_per_p2s The number of targets that each P2 rated. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s that rated each target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @import magrittr stringr lavaan
#' @export
#' @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)
#'
#' agree_pt_mods_hearacc_model <- rep_accuracy_id_mods_builder(target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = 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_mods_hearacc_model$model)
#'
#' # view model information
#' agree_pt_mods_hearacc_model$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_accuracy_id_mods_builder <- function(target_self, p2_reports, id_mod_variable,
interaction_term, n_triads = length(target_self),
n_ts_per_p2s = 1, n_p2s_per_ts = 1){
if(n_triads > 0 &
n_ts_per_p2s == 1 &
n_p2s_per_ts == 1){
model <-
# moderated regression model
paste(paste(target_self, "~", "ha_me*", p2_reports, "+",
"mod_me*", id_mod_variable, "+",
"interaction*", interaction_term),
# variances
paste(target_self, "~~ v_self*", target_self),
paste(p2_reports, " ~~ v_p2*", p2_reports),
paste(id_mod_variable, "~~ v_mod*", id_mod_variable),
paste(interaction_term, "~~ v_interaction*", interaction_term),
# intercepts
paste(target_self, "~ int_self*1"),
paste(p2_reports, " ~ int_p2*1"),
paste(id_mod_variable, "~ int_mod*1"),
paste(interaction_term, "~ int_interaction*1"),
sep = "\n")
rep_model_info <- tibble::as_tibble(list(model_type = "Individual-level moderator on Hearsay Accuracy",
ex_triads = n_triads,
p2_per_t = n_p2s_per_ts,
t_per_p2 = n_ts_per_p2s))
return(list(model = model,
rep_model_info = rep_model_info))
}
}
#' Individual level Moderators of Hearsay Accuracy
#'
#' This function fits a model for individual-level moderator on hearsay accuracy.
#' It requires a dataframe and either a model from the relevant model builder
#' function or names of columns for self-reports, p2_reports, and id_mod_variable.
#' The estimated parameters are:
#'
#' The parameters for the individual-level moderator analyses are:
#'
#' \describe{
#' \item{ha_me}{hearsay accuracy main effect; this should correspond to hearsay accuracy at average
#' level of moderator variable (if data were properly mean-centered).}
#' \item{mod_me}{The meain effect of the moderator variable; it can be interpreted as the difference in
#' target self-reports related to differences in the individual-level moderator variable.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which hearsay accuracy,
#' depends on the moderator variable}
#' \item{v_self}{variance for T(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_self}{intercept for T(T)}
#' \item{int_p2}{intercept for P2(T)}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains the ratings, moderator variable, and the interaction term.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain four columns: one for target self-reports, one for mean-centered P2 reports,
#' one for the mean-centered moderator variable, and one for the interaction term.
#' @param model Optional. A model from the corresponding ReputationModelR model builder function. If this
#' is supplied, no additional arguments need to be specified.
#' @param target_self Quoted column names that contain target self-reports.
#' If more than one is supplied, the 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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param 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 triads, the target-wise order must match the order of the ratings.
#' @param n_triads The number of exchangeable triads in each group. By default, this is determined by
#' counting the number of target self-reports. This parameter rarely needs to be changed.
#' @param n_ts_per_p2s The number of targets that each P2 rated. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s that rated each target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @import magrittr stringr lavaan
#' @export
#' @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)
#'
#' # Fitting by supplying variable/column names
#' agree_pt_mods_hearacc <- rep_accuracy_id_mods(data = moderator_data,
#' target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = 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"))
#'
#' # Alternatively, first build the model
#' agree_pt_mods_hearacc_model <- rep_accuracy_id_mods_builder(target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = 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"))
#'
#' # Then fit the model you just built
#' agree_pt_mods_hearacc <- rep_accuracy_id_mods(data = moderator_data,
#' model = agree_pt_mods_hearacc_model)
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_accuracy_id_mods <- function(data, model = NULL, target_self, p2_reports, id_mod_variable,
interaction_term, n_triads = length(target_self),
n_ts_per_p2s = 1, n_p2s_per_ts = 1){
# dealing with global variable binding issue
. <- NULL
if(is.null(model)){
rep_id_mods_model <- rep_accuracy_id_mods_builder(target_self = target_self, p2_reports = p2_reports,
id_mod_variable = id_mod_variable, interaction_term = interaction_term,
n_triads = length(target_self),
n_ts_per_p2s = n_ts_per_p2s, n_p2s_per_ts = n_p2s_per_ts)
if(round(mean(colMeans(data[,p2_reports], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
else{
# save model
rep_id_mods_model <- model
# Get P2 reports variable labels to check
# that data are mean-centered
p2_reports <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "ha_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# Get id_mod variable labels to check
# that data were mean-centered
id_mod_variable <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "mod_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# check that data were mean-centered
if(round(mean(colMeans(data[,p2_reports], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
fitted_model <- lavaan::sem(rep_id_mods_model$model, data = data, missing = "FIML", fixed.x = FALSE)
return(fitted_model)
}
#' Individual level Moderators (automatic)
#'
#' This is a wrapper function around the Individual-level moderator functions.
#'
#' This chooses a function depending on which
#' variables are supplied. At a minimum, it requires two ratings,
#' an individual-level moderator variable, and the interaction term.
#'
#' The estimated parameters depend upon which variables are supplied.
#'
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains the ratings, moderator variable, and the interaction term.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain four columns: two ratings (one which is mean-centered),
#' one for the mean-centered moderator variable, and one for the interaction term.
#' @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_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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param interaction_term Quoted column names that contain the interaction term, or the product of the
#' mean-centered rating (often P2-reports) and the mean-centered moderator variable.
#' If more than one is supplied from multiple exchangeable triads,
#' the target-wise order must match the order of the ratings.
#' @param n_triads The number of exchangeable triads in each group. By default, this is determined by
#' the method used by whichever specific model funciton is called.
#' For consensus, this is done by counting P1-reports; foraccuracy, this is done by counting
#' target self-reports. This parameter rarely needs to be changed.
#' @param n_ts_per_p2s The number of targets that each P2 rated. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s that rated each target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @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_r1_per_r2 The number of first ratings for each second rating. Currently, only 1:1 is supported.
#' @param n_r2_per_r1 The number of second ratings for each first rating. Currently, only 1:1 is supported.
#' @export
#' @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)
#'
#' # Example for hearsay accuracy
#' rep_auto_id_mods(data = moderator_data,
#' target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = 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"))
#'
#' # Example for hearsay consensus
#' rep_auto_id_mods(data = moderator_data,
#' p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = 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"))
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_auto_id_mods <- function(data, target_self = NULL, p1_reports = NULL, p2_reports = NULL,
id_mod_variable = NULL, interaction_term = NULL,
n_triads = NULL, n_ts_per_p2s = 1, n_p2s_per_ts = 1,
n_p1s_per_p2s = 1, n_p2s_per_p1s = 1, n_r1_per_r2 = 1, n_r2_per_r1 = 1){
# Check What variables are entered
if(is.null(id_mod_variable)){
stop("You need to enter a individual-level moderator for this function to run.
Either enter a moderator variable or use one of the non-moderator functions.")
}
# setup for hearsay consensus moderation model
if(is.null(target_self) &
!is.null(p1_reports) &
!is.null(p2_reports) &
!is.null(id_mod_variable)){
fitted_model <- rep_consensus_id_mods(data = data, p1_reports = p1_reports,
p2_reports = p2_reports,
id_mod_variable = id_mod_variable,
interaction_term = interaction_term,
n_triads = length(p1_reports),
n_p1s_per_p2s = n_p1s_per_p2s, n_p2s_per_p1s = n_p2s_per_p1s)
}
# setup for hearsay accuracy moderation model
else if(is.null(p1_reports) &
!is.null(target_self) &
!is.null(p2_reports) &
!is.null(id_mod_variable)){
fitted_model <- rep_accuracy_id_mods(data = data, target_self = target_self,
p2_reports = p2_reports,
id_mod_variable = id_mod_variable,
interaction_term = interaction_term,
n_triads = length(target_self),
n_ts_per_p2s = n_ts_per_p2s, n_p2s_per_ts = n_p2s_per_ts)
}
# setup for direct accuracy moderation model
else if(is.null(p2_reports) &
!is.null(target_self) &
!is.null(p1_reports) &
!is.null(id_mod_variable)){
fitted_model <- rep_generic_id_mods(data = data, rating_1 = target_self, rating_2 = p1_reports,
id_mod_variable = id_mod_variable,
interaction_term = interaction_term,
n_triads = length(target_self),
n_r1_per_r2 = n_r1_per_r2, n_r2_per_r1 = n_r1_per_r2)
} else{
stop("There is no default for the variables you entered.")
}
}
Coryc3133/ReputationAnalyses documentation built on July 31, 2019, 8:35 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions rep_generic_id_mods_builder rep_generic_id_mods rep_consensus_id_mods_builder rep_consensus_id_mods rep_accuracy_id_mods_builder rep_accuracy_id_mods rep_auto_id_mods
Documented in rep_accuracy_id_mods rep_accuracy_id_mods_builder rep_auto_id_mods rep_consensus_id_mods rep_consensus_id_mods_builder rep_generic_id_mods rep_generic_id_mods_builder
#' Individual level Moderators (Generic) Model Builder
#'
#' 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.
#'
#' The parameters for the individual-level moderator analyses are:
#'
#' \describe{
#' \item{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).}
#' \item{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.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which
#' the correlation between ratings depends on the moderator variable}
#' \item{v_rating_1}{variance for first rating}
#' \item{v_rating_2}{variance for second rating}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_rating_1}{intercept for first rating}
#' \item{int_rating_2}{intercept for second rating}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param 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.
#' @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_r1_per_r2 The number of first ratings for each second rating. Currently, only 1:1 is supported.
#' @param n_r2_per_r1 The number of second ratings for each first rating. Currently, only 1:1 is supported.
#' @import magrittr stringr lavaan
#' @export
#' @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
#'
#' @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_generic_id_mods_builder <- function(rating_1, rating_2, id_mod_variable,
interaction_term, n_triads = length(rating_1),
n_r1_per_r2 = 1, n_r2_per_r1 = 1){
if(n_triads > 0 &
n_r1_per_r2 == 1 &
n_r2_per_r1 == 1){
model <-
# moderated regression model
paste(paste(rating_1, "~", "rating_me*", rating_2, "+",
"mod_me*", id_mod_variable, "+",
"interaction*", interaction_term),
# variances
paste(rating_1, "~~ v_rating_1*", rating_1),
paste(rating_2, " ~~ v_rating_2*", rating_2),
paste(id_mod_variable, "~~ v_mod*", id_mod_variable),
paste(interaction_term, "~~ v_interaction*", interaction_term),
# intercepts
paste(rating_1, "~ int_rating_1*1"),
paste(rating_2, " ~ int_rating_2*1"),
paste(id_mod_variable, "~ int_mod*1"),
paste(interaction_term, "~ int_interaction*1"),
sep = "\n")
# Put the model info together.
rep_model_info <- tibble::as_tibble(list(model_type = "Individual-level moderator (generic function)",
ex_triads = n_triads,
r1_per_r2 = n_r1_per_r2,
r2_per_r1 = n_r2_per_r1))
return(list(model = model,
rep_model_info = rep_model_info))
}}
#' Individual level Moderators (Generic)
#'
#' This is a generic function 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. It requires a dataframe and either a model from the relevant model builder
#' function or names of columns for rating_1, rating_2, and id_mod_variable.
#' The estimated parameters are:
#'
#' \describe{
#' \item{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).}
#' \item{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.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which
#' the correlation between ratings depends on the moderator variable}
#' \item{v_rating_1}{variance for first rating}
#' \item{v_rating_2}{variance for second rating}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_rating_1}{intercept for first rating}
#' \item{int_rating_2}{intercept for second rating}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains the ratings, moderator variable, and the interaction term.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain four columns: two for ratings (of the same target),
#' one for the mean-centered moderator variable, and one for the interaction term.
#' @param model Optional. A model from the corresponding ReputationModelR model builder function. If this
#' is supplied, no additional arguments need to be specified.
#' @param 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.
#' @param 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.
#' @param 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.
#' @param 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.
#' @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_r1_per_r2 The number of first ratings for each second rating. Currently, only 1:1 is supported.
#' @param n_r2_per_r1 The number of second ratings for each first rating. Currently, only 1:1 is supported.
#' @import magrittr stringr lavaan
#' @export
#' @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)
#'
#' # fit model by specifying the variables / column names
#' agree_pt_mod <- rep_generic_id_mods(data = moderator_data,
#' 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"))
#'
#' # alternatively, you can 'build a model' first
#' 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"))
#' # Then pass the built model on to the fit function
#' agree_pt_mod <- rep_generic_id_mods(data = moderator_data,
#' model = agree_pt_mod_model)
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_generic_id_mods <- function(data, model = NULL, rating_1, rating_2,
id_mod_variable,
interaction_term, n_triads = length(rating_1),
n_r1_per_r2 = 1,n_r2_per_r1 = 1){
# Global Variable Binding Issue
. <- NULL
if(is.null(model)){
rep_id_mods_model <- 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)
if(round(mean(colMeans(data[,rating_2], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
else{
# save model
rep_id_mods_model <- model
# Get rating_2 variable labels to check
# that data are mean-centered
rating_2 <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "rating_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# Fer id_mod variable labels to check
# that data were mean-centered
id_mod_variable <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "mod_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# check that data were mean-centered
if(round(mean(colMeans(data[,rating_2], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
fitted_model <- lavaan::sem(rep_id_mods_model$model, data = data, missing = "FIML", fixed.x = FALSE)
return(fitted_model)
}
#' Individual level Moderators of Hearsay Consensus Model Builder
#'
#' This takes the variables needed to assess an individual-level moderator on hearsay consensus, and builds
#' a model for lavaan estimating the corresponding parameters. At a minimum, it requires P1-reports, P2-reports,
#' an individual-level moderator, and the interaction term. Note that the P2-reports and moderator variable should
#' be mean-centered.
#'
#' The parameters for the individual-level moderator analyses are:
#'
#' \describe{
#' \item{hc_me}{hearsay consensus main effect; this should correspond to hearsay consensus at average
#' level of moderator variable (if data were properly mean-centered).}
#' \item{mod_me}{The meain effect of the moderator variable; it can be interpreted as the difference in
#' P1-reports related to differences in the individual-level moderator variable.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which hearsay consensus,
#' depends on the moderator variable}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_p1}{intercept for P1(T)}
#' \item{int_p2}{intercept for P2(T)}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' 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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param 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 triads, the target-wise order must match the order of the ratings.
#' @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")
#' # 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)
#'
#' # Build a consensus model
#' agree_pt_mod_consensus_model <- rep_consensus_id_mods_builder (p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = 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_consensus_model$model)
#'
#' # view model Information
#' agree_pt_mod_consensus_model$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_id_mods_builder <- function(p1_reports, p2_reports, id_mod_variable,
interaction_term, 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){
model <-
# moderated regression model
paste(paste(p1_reports, "~", "hc_me*", p2_reports, "+",
"mod_me*", id_mod_variable, "+",
"interaction*", interaction_term),
# variances
paste(p1_reports, "~~ v_p1*", p1_reports),
paste(p2_reports, " ~~ v_p2*", p2_reports),
paste(id_mod_variable, "~~ v_mod*", id_mod_variable),
paste(interaction_term, "~~ v_interaction*", interaction_term),
# intercepts
paste(p1_reports, "~ int_p1*1"),
paste(p2_reports, " ~ int_p2*1"),
paste(id_mod_variable, "~ int_mod*1"),
paste(interaction_term, "~ int_interaction*1"),
sep = "\n")
# Put the model info together.
rep_model_info <- tibble::as_tibble(list(model_type = "Individual-level moderator on Hearsay Consensus",
ex_triads = n_triads,
p1_per_p2 = n_p1s_per_p2s,
p2_per_p1 = n_p2s_per_p1s))
return(list(model = model,
rep_model_info = rep_model_info))
}
if(n_p1s_per_p2s > 1){warning("I'm sorry, this function can only handle designs with 1 P1 per P2; check back for changes")}
if(n_p2s_per_p1s > 1){warning("I'm sorry, this function can only handle designs with 1 P2 per P1; check back for changes")}
}
#' Individual level Moderators of Hearsay Consensus Model
#'
#' This function fits a model for individual-level moderator on hearsay consensus, and builds
#' a model for lavaan estimating the corresponding parameters.
#' It requires a dataframe and either a model from the relevant model builder
#' function or names of columns for p1_reports, p2_reports, and id_mod_variable.
#' The estimated parameters are:
#'
#' \describe{
#' \item{hc_me}{hearsay consensus main effect; this should correspond to hearsay consensus at average
#' level of moderator variable (if data were properly mean-centered).}
#' \item{mod_me}{The meain effect of the moderator variable; it can be interpreted as the difference in
#' P1-reports related to differences in the individual-level moderator variable.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which hearsay consensus,
#' depends on the moderator variable}
#' \item{v_p1}{variance for P1(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_p1}{intercept for P1(T)}
#' \item{int_p2}{intercept for P2(T)}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains the ratings, moderator variable, and the interaction term.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain four columns: one for P1 reports, one for mean-centered P2 reports,
#' one for the mean-centered moderator variable, and one for the interaction term.
#' @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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param 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 triads, the target-wise order must match the order of the ratings.
#' @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")
#' # 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)
#'
#' # Fitting by supplying variable/column names
#' agree_pt_mod <- rep_consensus_id_mods(data = moderator_data,
#' p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = 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"))
#'
#' # alternatively, build the model frst
#' agree_pt_mod_model <- rep_consensus_id_mods_builder (p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = 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"))
#' # Then fit the model you just built
#' agree_pt_mod <- rep_consensus_id_mods(data = moderator_data,
#' model = agree_pt_mod_model)
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_consensus_id_mods <- function(data, model = NULL, p1_reports, p2_reports, id_mod_variable,
interaction_term, n_triads = length(p1_reports),
n_p1s_per_p2s = 1, n_p2s_per_p1s = 1){
# dealing with global variable binding issue
. <- NULL
if(is.null(model)){
rep_id_mods_model <- rep_consensus_id_mods_builder(p1_reports, p2_reports, id_mod_variable,
interaction_term, n_triads = length(p1_reports),
n_p1s_per_p2s = n_p1s_per_p2s, n_p2s_per_p1s = n_p1s_per_p2s)
if(round(mean(colMeans(data[,p2_reports], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
else{
# save model
rep_id_mods_model <- model
# Get P2 reports variable labels to check
# that data are mean-centered
p2_reports <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "hc_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# Get id_mod variable labels to check
# that data were mean-centered
id_mod_variable <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "mod_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# check that data were mean-centered
if(round(mean(colMeans(data[,p2_reports], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
fitted_model <- lavaan::sem(rep_id_mods_model$model, data = data, missing = "FIML", fixed.x = FALSE)
return(fitted_model)
}
#' Individual level Moderators of Hearsay Accuracy Model Builder
#'
#' This takes the variables needed to assess an individual-level moderator on hearsay acuracy, and builds
#' a model for lavaan estimating the corresponding parameters. At a minimum, it requires target self-reports, P2-reports,
#' an individual-level moderator, and the interaction term.
#' Note that the P2-reports and moderator variable should be mean-centered.
#'
#' The parameters for the individual-level moderator analyses are:
#'
#' \describe{
#' \item{ha_me}{hearsay accuracy main effect; this should correspond to hearsay accuracy at average
#' level of moderator variable (if data were properly mean-centered).}
#' \item{mod_me}{The meain effect of the moderator variable; it can be interpreted as the difference in
#' target self-reports related to differences in the individual-level moderator variable.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which hearsay accuracy,
#' depends on the moderator variable}
#' \item{v_self}{variance for T(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_self}{intercept for T(T)}
#' \item{int_p2}{intercept for P2(T)}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param target_self Quoted column names that contain target self-reports.
#' If more than one is supplied, the 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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param 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 triads, the target-wise order must match the order of the ratings.
#' @param n_triads The number of exchangeable triads in each group. By default, this is determined by
#' counting the number of target self-reports. This parameter rarely needs to be changed.
#' @param n_ts_per_p2s The number of targets that each P2 rated. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s that rated each target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @import magrittr stringr lavaan
#' @export
#' @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)
#'
#' agree_pt_mods_hearacc_model <- rep_accuracy_id_mods_builder(target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = 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_mods_hearacc_model$model)
#'
#' # view model information
#' agree_pt_mods_hearacc_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_accuracy_id_mods_builder <- function(target_self, p2_reports, id_mod_variable,
interaction_term, n_triads = length(target_self),
n_ts_per_p2s = 1, n_p2s_per_ts = 1){
if(n_triads > 0 &
n_ts_per_p2s == 1 &
n_p2s_per_ts == 1){
model <-
# moderated regression model
paste(paste(target_self, "~", "ha_me*", p2_reports, "+",
"mod_me*", id_mod_variable, "+",
"interaction*", interaction_term),
# variances
paste(target_self, "~~ v_self*", target_self),
paste(p2_reports, " ~~ v_p2*", p2_reports),
paste(id_mod_variable, "~~ v_mod*", id_mod_variable),
paste(interaction_term, "~~ v_interaction*", interaction_term),
# intercepts
paste(target_self, "~ int_self*1"),
paste(p2_reports, " ~ int_p2*1"),
paste(id_mod_variable, "~ int_mod*1"),
paste(interaction_term, "~ int_interaction*1"),
sep = "\n")
rep_model_info <- tibble::as_tibble(list(model_type = "Individual-level moderator on Hearsay Accuracy",
ex_triads = n_triads,
p2_per_t = n_p2s_per_ts,
t_per_p2 = n_ts_per_p2s))
return(list(model = model,
rep_model_info = rep_model_info))
}
}
#' Individual level Moderators of Hearsay Accuracy
#'
#' This function fits a model for individual-level moderator on hearsay accuracy.
#' It requires a dataframe and either a model from the relevant model builder
#' function or names of columns for self-reports, p2_reports, and id_mod_variable.
#' The estimated parameters are:
#'
#' The parameters for the individual-level moderator analyses are:
#'
#' \describe{
#' \item{ha_me}{hearsay accuracy main effect; this should correspond to hearsay accuracy at average
#' level of moderator variable (if data were properly mean-centered).}
#' \item{mod_me}{The meain effect of the moderator variable; it can be interpreted as the difference in
#' target self-reports related to differences in the individual-level moderator variable.}
#' \item{interaction}{This is the interaction term. It indicates the extent to which hearsay accuracy,
#' depends on the moderator variable}
#' \item{v_self}{variance for T(T)}
#' \item{v_p2}{variance for P2(T)}
#' \item{v_mod}{variance for moderator variable}
#' \item{v_interaction}{variance for interaction term}
#' \item{int_self}{intercept for T(T)}
#' \item{int_p2}{intercept for P2(T)}
#' \item{int_mod}{intercept for moderator variable}
#' \item{int_interaction}{intercept for interaction term}
#' }
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains the ratings, moderator variable, and the interaction term.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain four columns: one for target self-reports, one for mean-centered P2 reports,
#' one for the mean-centered moderator variable, and one for the interaction term.
#' @param model Optional. A model from the corresponding ReputationModelR model builder function. If this
#' is supplied, no additional arguments need to be specified.
#' @param target_self Quoted column names that contain target self-reports.
#' If more than one is supplied, the 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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param 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 triads, the target-wise order must match the order of the ratings.
#' @param n_triads The number of exchangeable triads in each group. By default, this is determined by
#' counting the number of target self-reports. This parameter rarely needs to be changed.
#' @param n_ts_per_p2s The number of targets that each P2 rated. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s that rated each target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @import magrittr stringr lavaan
#' @export
#' @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)
#'
#' # Fitting by supplying variable/column names
#' agree_pt_mods_hearacc <- rep_accuracy_id_mods(data = moderator_data,
#' target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = 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"))
#'
#' # Alternatively, first build the model
#' agree_pt_mods_hearacc_model <- rep_accuracy_id_mods_builder(target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = 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"))
#'
#' # Then fit the model you just built
#' agree_pt_mods_hearacc <- rep_accuracy_id_mods(data = moderator_data,
#' model = agree_pt_mods_hearacc_model)
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_accuracy_id_mods <- function(data, model = NULL, target_self, p2_reports, id_mod_variable,
interaction_term, n_triads = length(target_self),
n_ts_per_p2s = 1, n_p2s_per_ts = 1){
# dealing with global variable binding issue
. <- NULL
if(is.null(model)){
rep_id_mods_model <- rep_accuracy_id_mods_builder(target_self = target_self, p2_reports = p2_reports,
id_mod_variable = id_mod_variable, interaction_term = interaction_term,
n_triads = length(target_self),
n_ts_per_p2s = n_ts_per_p2s, n_p2s_per_ts = n_p2s_per_ts)
if(round(mean(colMeans(data[,p2_reports], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
else{
# save model
rep_id_mods_model <- model
# Get P2 reports variable labels to check
# that data are mean-centered
p2_reports <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "ha_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# Get id_mod variable labels to check
# that data were mean-centered
id_mod_variable <- lavaanify(rep_id_mods_model$model, fixed.x = FALSE) %>%
dplyr::filter(.data$label == "mod_me") %>%
dplyr::select(.data$rhs) %>%
dplyr::distinct() %>%
.$rhs
# check that data were mean-centered
if(round(mean(colMeans(data[,p2_reports], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center P2 reports. You might want to for interpretability's sake.")}
if(round(mean(colMeans(data[,id_mod_variable], na.rm = TRUE)), 4) != 0){warning("It looks like you didn't center the moderator variable. You might want to for interpretability's sake.")}
}
fitted_model <- lavaan::sem(rep_id_mods_model$model, data = data, missing = "FIML", fixed.x = FALSE)
return(fitted_model)
}
#' Individual level Moderators (automatic)
#'
#' This is a wrapper function around the Individual-level moderator functions.
#'
#' This chooses a function depending on which
#' variables are supplied. At a minimum, it requires two ratings,
#' an individual-level moderator variable, and the interaction term.
#'
#' The estimated parameters depend upon which variables are supplied.
#'
#' The function can handle up to n exchangeable triads.
#' @param data The dataframe that contains the ratings, moderator variable, and the interaction term.
#' Data should be wide, with a row for every group of participants.
#' At a minimum, it must contain four columns: two ratings (one which is mean-centered),
#' one for the mean-centered moderator variable, and one for the interaction term.
#' @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_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.
#' Ratings should be grand-mean-centered to increase the interpretibility of the model parameters.
#' If more than one is supplied, the target-wise order must match the other rating types.
#' @param id_mod_variable Quoted column names that contain the individual-level moderator of interest.
#' If more than one is supplied from multiple exchangeable triads,
#' the order must match the order of the ratings. Like P2-reports, the variable should be mean-centered
#' to facilitate interpretability.
#' @param interaction_term Quoted column names that contain the interaction term, or the product of the
#' mean-centered rating (often P2-reports) and the mean-centered moderator variable.
#' If more than one is supplied from multiple exchangeable triads,
#' the target-wise order must match the order of the ratings.
#' @param n_triads The number of exchangeable triads in each group. By default, this is determined by
#' the method used by whichever specific model funciton is called.
#' For consensus, this is done by counting P1-reports; foraccuracy, this is done by counting
#' target self-reports. This parameter rarely needs to be changed.
#' @param n_ts_per_p2s The number of targets that each P2 rated. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @param n_p2s_per_ts The number of P2s that rated each target;. This defaults to 1.
#' Currently, only values of 1 are supported.
#' @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_r1_per_r2 The number of first ratings for each second rating. Currently, only 1:1 is supported.
#' @param n_r2_per_r1 The number of second ratings for each first rating. Currently, only 1:1 is supported.
#' @export
#' @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)
#'
#' # Example for hearsay accuracy
#' rep_auto_id_mods(data = moderator_data,
#' target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = 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"))
#'
#' # Example for hearsay consensus
#' rep_auto_id_mods(data = moderator_data,
#' p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = 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"))
#'
#' @return The function returns an object of class \code{\link[lavaan:lavaan-class]{lavaan}}.
rep_auto_id_mods <- function(data, target_self = NULL, p1_reports = NULL, p2_reports = NULL,
id_mod_variable = NULL, interaction_term = NULL,
n_triads = NULL, n_ts_per_p2s = 1, n_p2s_per_ts = 1,
n_p1s_per_p2s = 1, n_p2s_per_p1s = 1, n_r1_per_r2 = 1, n_r2_per_r1 = 1){
# Check What variables are entered
if(is.null(id_mod_variable)){
stop("You need to enter a individual-level moderator for this function to run.
Either enter a moderator variable or use one of the non-moderator functions.")
}
# setup for hearsay consensus moderation model
if(is.null(target_self) &
!is.null(p1_reports) &
!is.null(p2_reports) &
!is.null(id_mod_variable)){
fitted_model <- rep_consensus_id_mods(data = data, p1_reports = p1_reports,
p2_reports = p2_reports,
id_mod_variable = id_mod_variable,
interaction_term = interaction_term,
n_triads = length(p1_reports),
n_p1s_per_p2s = n_p1s_per_p2s, n_p2s_per_p1s = n_p2s_per_p1s)
}
# setup for hearsay accuracy moderation model
else if(is.null(p1_reports) &
!is.null(target_self) &
!is.null(p2_reports) &
!is.null(id_mod_variable)){
fitted_model <- rep_accuracy_id_mods(data = data, target_self = target_self,
p2_reports = p2_reports,
id_mod_variable = id_mod_variable,
interaction_term = interaction_term,
n_triads = length(target_self),
n_ts_per_p2s = n_ts_per_p2s, n_p2s_per_ts = n_p2s_per_ts)
}
# setup for direct accuracy moderation model
else if(is.null(p2_reports) &
!is.null(target_self) &
!is.null(p1_reports) &
!is.null(id_mod_variable)){
fitted_model <- rep_generic_id_mods(data = data, rating_1 = target_self, rating_2 = p1_reports,
id_mod_variable = id_mod_variable,
interaction_term = interaction_term,
n_triads = length(target_self),
n_r1_per_r2 = n_r1_per_r2, n_r2_per_r1 = n_r1_per_r2)
} else{
stop("There is no default for the variables you entered.")
}
}
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