R/ez_tables.R
In Coryc3133/ReputationAnalyses: Functions to model and analyze triadic (T-P1-P2) reputational data
Defines functions
ez_differential_table
ez_differential_group_table
ez_id_mod_table
ez_group_id_mod_table
ez_elevation_table
ez_elevation_group_table
ez_tables
Documented in
ez_differential_group_table
ez_differential_table
ez_elevation_group_table
ez_elevation_table
ez_group_id_mod_table
ez_id_mod_table
ez_tables
#' Easily Table Differential Results
#'
#' This takes output from one of the ReputationModelR model functions
#' (e.g., rep_analyses_auto) and returns a tibble of differential
#' (correlational) parameters. It works with any combination of P1-,
#' P2-, and 3rd person Meta-perceptions.
#'
#' The function can handle up to n exchangeable triads.
#' @param rep_model The results from one of the ReputationModelR
#' Models (e.g., rep_analyses_auto).
#' @param what The parameters you want in the table. Current options are
#' main and all. If what = "main", then just the 'main' model parameters are provided.
#' This will include, when avaiable, hearsay consensus, hearsay accuracy,
#' direct accuracy (P1-P1 agreement), P1 Meta-Accuracy, and P2 Meta-Accuracy.
#' @export
#' @import magrittr stringr lavaan
#' @importFrom rlang .data
#' @examples data("rep_sim_data")
#' # Consensus only Model
#' agree_rep_consensus <- 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"))
#'
#' ez_differential_table(agree_rep_consensus, what = "main")
#'
#' ez_differential_table(agree_rep_consensus, what = "all")
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc <- 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"))
#'
#' ez_differential_table(agree_rep_con_acc, what = "main")
#'
#' ez_differential_table(agree_rep_con_acc, what = "all")
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_rep_all <- 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"))
#'
#' ez_differential_table(agree_rep_all, what = "main")
#'
#' ez_differential_table(agree_rep_all, what = "all")
#'
#' @return The function returns an object of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_differential_table <- function(rep_model,
what = "main"){
# First save out labels
# we need these to remove the repeats (from equality constraints)
labels <-
rep_model@ParTable %>%
tibble::as_tibble() %>%
dplyr::select(.data$lhs, .data$op, .data$rhs, .data$label)
# Main parameters only
if(what == "main"){
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$label == "hc" |
.data$label == "ha" |
.data$label == "da" |
.data$label == "p1ma"|
.data$label == "p2ma") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$label == "ha", "hearsay accuracy",
ifelse(.data$label == "hc", "hearsay consensus",
ifelse(.data$label == "da", "direct accuracy",
ifelse(.data$label == "p1ma", "P1 Meta-Accuracy",
ifelse(.data$label == "p2ma", "P2 Meta-Accuracy", NA)))))) %>%
dplyr::select(.data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(r = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)}
if(what == "all"){
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$label == "ha"|
.data$label == "hc"|
.data$label == "da"|
.data$label == "p1ma"|
.data$label == "p2ma"|
.data$label == "as_ac1"|
.data$label == "as_con1"|
.data$label == "mp_rec"|
.data$label == "as_ac2"|
.data$label == "as_con2"|
.data$label == "rec"|
.data$label == "h"|
.data$label == "m"|
.data$label == "tru_sim"|
.data$label == "as_sim_3p"|
.data$label == "as_sim_1p"|
.data$label == "as_sim_p1m"|
.data$label == "ukp1m1"|
.data$label == "p1meta_sim"|
.data$label == "ukp2m1"|
.data$label == "ukp2m3"|
.data$label == "p2meta_sim"|
.data$label == "ukm1") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$label == "ha", "hearsay accuracy",
ifelse(.data$label == "hc", "hearsay consensus",
ifelse(.data$label == "da", "direct accuracy",
ifelse(.data$label == "p1ma", "P1 Meta-Accuracy",
ifelse(.data$label == "p2ma", "P2 Meta-Accuracy",
ifelse(.data$label == "as_ac1", "P1 Assumed Accuracy",
ifelse(.data$label == "as_con1", "P1 Assumed Consensus",
ifelse(.data$label == "mp_rec", "P1-P2 Meta-Perception Reciprocity",
ifelse(.data$label == "as_ac2", "P2 Assumed Accuracy",
ifelse(.data$label == "as_con2", "P2 Assumed Consensus",
ifelse(.data$label == "rec", "direct reciprocity",
ifelse(.data$label == "h", "hearsay reciprocity",
ifelse(.data$label == "m", "P2(T) <-> opposite P1(T)",
ifelse(.data$label == "tru_sim", "True Target Similarity",
ifelse(.data$label == "as_sim_3p", "Third-Person Assumed Similarity",
ifelse(.data$label == "as_sim_1p", "First-Person Assumed Similarity",
ifelse(.data$label == "as_sim_p1m", "P1 Meta- Assumed Similarity",
ifelse(.data$label == "ukp1m1", "P1 Meta <-> opposite P1-Report",
ifelse(.data$label == "p1meta_sim", "P1 meta-similarity",
ifelse(.data$label == "ukp2m1", "P2 Meta <-> opposite target self-report",
ifelse(.data$label == "ukp2m3", "P2 meta <-> opposite P1-report",
ifelse(.data$label == "p2meta_sim", "P2 Meta-similarity",
ifelse(.data$label == "ukm1", "P1 Meta <-> opposite P2 Meta",
NA)))))))))))))))))))))))) %>%
dplyr::select(.data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(r = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)}
return(rep_parameter_table)}
#' Easily Table Differential Results from Group Moderated Models
#'
#' This takes output from one of the group moderated reputation models
#' (e.g., rep_analyses_auto) and returns a tibble of differential
#' (correlational) parameters. It works with any of the group moderated models.
#' @param rep_model The results from one of the ReputationModelR
#' group Models (e.g., rep_auto_group_mod). The model can have between-group equality
#' constraints; those are marked by missing values (NA) in the group_label column.
#' @param what The parameters you want in the table. Current options are
#' main and all. If what = "main", then just the 'main' model parameters are provided.
#' This will include, when avaiable, hearsay consensus, hearsay accuracy,
#' direct accuracy (P1-P1 agreement), P1 Meta-Accuracy, and P2 Meta-Accuracy.
#' @export
#' @import magrittr stringr lavaan
#' @examples data("rep_sim_data")
#' # Consensus only Model
#' agree_rep_consensus_grpmod <- rep_auto_group_mod(data = rep_sim_data,
#' p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = c("B_C_agreeableness", "D_A_agreeableness"),
#' group_mod = "study",
#' groups_eql = "all",
#' params_eql = "all")
#'
#' ez_differential_group_table(agree_rep_consensus_grpmod, what = "main")
#'
#' ez_differential_group_table(agree_rep_consensus_grpmod, what = "all")
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc_grpmod <- rep_auto_group_mod(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"),
#' group_mod = "study", groups_eql = "all",params_eql = "all")
#'
#' ez_differential_group_table(agree_rep_con_acc_grpmod, what = "main")
#'
#' ez_differential_group_table(agree_rep_con_acc_grpmod, what = "all")
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_full_3pmeta_grpmod <- rep_auto_group_mod(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"),
#' group_mod = "study",
#' groups_eql = "all",
#' params_eql = "all")
#'
#' ez_differential_group_table(agree_full_3pmeta_grpmod, what = "main")
#'
#' ez_differential_group_table(agree_full_3pmeta_grpmod, what = "all")
#' @return The function returns an object of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_differential_group_table <- function(rep_model,
what = "main"){
# First save out labels
# we need these to remove the repeats (from equality constraints)
labels <- rep_model@ParTable %>%
tibble::as_tibble() %>%
dplyr::select(.data$lhs, .data$op, .data$rhs, .data$group, .data$label) %>%
tidyr::separate(.data$label, c("group_label", "param_label"),
extra = "merge", fill = "left") %>%
dplyr::filter(.data$group_label != "")
# Main parameters only
if(what == "main"){
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$group_label, .data$param_label, .keep_all = TRUE) %>%
dplyr::filter(.data$param_label == "hc" |
.data$param_label == "ha" |
.data$param_label == "da" |
.data$param_label == "p1ma"|
.data$param_label == "p2ma") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$param_label == "ha", "hearsay accuracy",
ifelse(.data$param_label == "hc", "hearsay consensus",
ifelse(.data$param_label == "da", "direct accuracy",
ifelse(.data$param_label == "p1ma", "P1 Meta-Accuracy",
ifelse(.data$param_label == "p2ma", "P2 Meta-Accuracy", NA)))))) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(r = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)}
if(what == "all"){
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$param_label == "ha"|
.data$param_label == "hc"|
.data$param_label == "da"|
.data$param_label == "p1ma"|
.data$param_label == "p2ma"|
.data$param_label == "as_ac1"|
.data$param_label == "as_con1"|
.data$param_label == "mp_rec"|
.data$param_label == "as_ac2"|
.data$param_label == "as_con2"|
.data$param_label == "rec"|
.data$param_label == "h"|
.data$param_label == "m"|
.data$param_label == "tru_sim"|
.data$param_label == "as_sim_3p"|
.data$param_label == "as_sim_1p"|
.data$param_label == "as_sim_p1m"|
.data$param_label == "ukp1m1"|
.data$param_label == "p1meta_sim"|
.data$param_label == "ukp2m1"|
.data$param_label == "ukp2m3"|
.data$param_label == "p2meta_sim"|
.data$param_label == "ukm1") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$param_label == "ha", "hearsay accuracy",
ifelse(.data$param_label == "hc", "hearsay consensus",
ifelse(.data$param_label == "da", "direct accuracy",
ifelse(.data$param_label == "p1ma", "P1 Meta-Accuracy",
ifelse(.data$param_label == "p2ma", "P2 Meta-Accuracy",
ifelse(.data$param_label == "as_ac1", "P1 Assumed Accuracy",
ifelse(.data$param_label == "as_con1", "P1 Assumed Consensus",
ifelse(.data$param_label == "mp_rec", "P1-P2 Meta-Perception Reciprocity",
ifelse(.data$param_label == "as_ac2", "P2 Assumed Accuracy",
ifelse(.data$param_label == "as_con2", "P2 Assumed Consensus",
ifelse(.data$param_label == "rec", "direct reciprocity",
ifelse(.data$param_label == "h", "hearsay reciprocity",
ifelse(.data$param_label == "m", "P2(T) <-> opposite P1(T)",
ifelse(.data$param_label == "tru_sim", "True Target Similarity",
ifelse(.data$param_label == "as_sim_3p", "Third-Person Assumed Similarity",
ifelse(.data$param_label == "as_sim_1p", "First-Person Assumed Similarity",
ifelse(.data$param_label == "as_sim_p1m", "P1 Meta- Assumed Similarity",
ifelse(.data$param_label == "ukp1m1", "P1 Meta <-> opposite P1-Report",
ifelse(.data$param_label == "p1meta_sim", "P1 meta-similarity",
ifelse(.data$param_label == "ukp2m1", "P2 Meta <-> opposite target self-report",
ifelse(.data$param_label == "ukp2m3", "P2 meta <-> opposite P1-report",
ifelse(.data$param_label == "p2meta_sim", "P2 Meta-similarity",
ifelse(.data$param_label == "ukm1", "P1 Meta <-> opposite P2 Meta",
NA)))))))))))))))))))))))) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(r = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)
}
# the function above will only get parameters without equality constraints
# which sort of makes sense for this (it only gives you the results of group moderated analyses).
# However, you might have a case in which some (but not all) groups or parameters are equal, and you
# want to table them all together. This adds the equality constrained rows
if(nrow(rep_parameter_table) == 0){
rep_parameter_table <- ez_differential_table(rep_model = rep_model,
what = what)
}
else{
rep_parameter_table_eqls <- NULL
if(max(rep_model@ParTable[["group"]]) != nrow(unique(rep_parameter_table["group_label"]))){
rep_parameter_table_eqls <- ez_differential_table(rep_model = rep_model,
what = what)
}
if(!is.null(rep_parameter_table_eqls) && nrow(rep_parameter_table_eqls) > 0){
rep_parameter_table <- dplyr::full_join(rep_parameter_table, rep_parameter_table_eqls)
rep_parameter_table <- dplyr::mutate(rep_parameter_table, group_label = ifelse(is.na(.data$group_label), "eql", .data$group_label))
message("The Model you provided had some between-group equality constraints.
Those pooled estimates are in the rows where group is marked eql")
}
}
return(rep_parameter_table)}
#' Easily Table Individual Difference Moderator Results
#'
#' This takes output from one of the individual-level moderated reputation models
#' (e.g., rep_auto_id_mods) and returns a tibble of model
#' (regression) parameters. It works with any of the Individual-level moderated models.
#' @param rep_model The results from one of the ReputationModelR
#' individual-level moderator Models (e.g., rep_auto_id_mods).
#' @export
#' @import magrittr stringr lavaan
#' @examples data("rep_sim_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
#' agree_ha_p2ptmod_model <- 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"))
#' ez_id_mod_table(agree_ha_p2ptmod_model)
#'
#'
#' @return The function returns an object of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_id_mod_table <- function(rep_model){
# First save out labels
# we need these to remove the repeats (from equality constraints)
labels <- rep_model@ParTable %>%
tibble::as_tibble() %>%
dplyr::select(.data$lhs, .data$op, .data$rhs, .data$label)
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$label == "hc_me" |
.data$label == "ha_me" |
.data$label == "mod_me" |
.data$label == "interaction") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$label == "hc_me", "hearsay consensus (main effect)",
ifelse(.data$label == "ha_me", "hearsay accuracy (main effect)",
ifelse(.data$label == "mod_me", "moderator (main effect)",
ifelse(.data$label == "interaction", "interaction effect",NA))))) %>%
dplyr::select(.data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(beta = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)
return(rep_parameter_table)}
#' Easily Table Model with Group and Individual Difference Moderators
#'
#' This takes output from one of the group- and individual-level moderated reputation models
#' (e.g., rep_accuracy_group_id_mods) and returns a tibble of model
#' (regression) parameters. It works with any of the Group- and Individual-level moderated models.
#' @param rep_model The results from one of the ReputationModelR
#' group- and individual-level moderator Models (e.g., rep_accuracy_group_id_mods).
#' @export
#' @examples data("rep_sim_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 with no equality constraints
#' agree_pt_mod_fit <- rep_accuracy_group_id_mods(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"),
#' group_mod = "study")
#' ez_group_id_mod_table(agree_pt_mod_fit)
#'
#' # Example for hearsay accuracy with all parameters equal across all groups
#' agree_pt_mod_fit_alleq <- rep_accuracy_group_id_mods(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"),
#' group_mod = "study", groups_eql = "all", params_eql = "all")
#' ez_group_id_mod_table(agree_pt_mod_fit_alleq)
#'
#' # Example with some equality constraints
#'
#' agree_pt_mod_fit_someeql <- rep_accuracy_group_id_mods(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"),
#' group_mod = "group_var", groups_eql = c(1, 4), params_eql = "all")
#'
#' ez_group_id_mod_table(agree_pt_mod_fit_someeql)
#' @return The function returns an object of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_group_id_mod_table <- function(rep_model){
# First save out labels
# we need these to remove the repeats (from equality constraints)
labels <- rep_model@ParTable%>%
tibble::as_tibble() %>%
dplyr::select(.data$lhs, .data$op, .data$rhs, .data$group, .data$label) %>%
tidyr::separate(.data$label, c("group_label", "param_label"), extra = "merge", fill = "left") %>%
dplyr::filter(.data$group_label != "")
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::filter(.data$lhs != .data$rhs &
.data$op != "~1") %>%
dplyr::distinct(.data$group_label, .data$param_label, .keep_all = TRUE) %>%
dplyr::filter(.data$param_label == "hc_me" |
.data$param_label == "ha_me" |
.data$param_label == "mod_me" |
.data$param_label == "interaction") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$param_label == "hc_me", "hearsay consensus (main effect)",
ifelse(.data$param_label == "ha_me", "hearsay accuracy (main effect)",
ifelse(.data$param_label == "mod_me", "moderator (main effect)",
ifelse(.data$param_label == "interaction", "interaction effect",NA))))) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(beta = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)
# the function above will only get parameters without equality constraints
# which sort of makes sense for this (it only gives you the results of group moderated analyses).
# However, you might have a case in which some (but not all) groups or parameters are equal, and you
# want to table them all together. This adds the equality constrained rows
if(nrow(rep_parameter_table) == 0){
rep_parameter_table <- ez_id_mod_table(rep_model = rep_model)
}
else{
rep_parameter_table_eqls <- NULL
if(max(rep_model@ParTable[["group"]]) != nrow(unique(rep_parameter_table["group_label"]))){
rep_parameter_table_eqls <- ez_id_mod_table(rep_model = rep_model)
}
if(!is.null(rep_parameter_table_eqls) && nrow(rep_parameter_table_eqls) > 0){
rep_parameter_table <- dplyr::full_join(rep_parameter_table, rep_parameter_table_eqls)
rep_parameter_table <- dplyr::mutate(rep_parameter_table, group_label = ifelse(is.na(.data$group_label), "eql", .data$group_label))
message("The Model you provided had some between-group equality constraints.
Those pooled estimates are in the rows where group is marked eql")
}
}
return(rep_parameter_table)}
#' Easily Table Elevation Results
#'
#' This takes output from one of the reputational analysis models
#' (e.g., rep_analyses_auto) and returns a list of two tibbles corresponding to
#' elevation (mean-differences) estimates and the relevant pooled Means and SDs respectively.
#' The elevation table contains the raw difference, 95% CI around the raw difference,
#' z scores, p values, and a cohen's d (difference / pooled SD). It has only 1 parameter.
#'
#' @param rep_model The results from one of the ReputationModelR
#' Models (e.g., rep_analyses_auto).
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'
#' # Consensus only Model
#' agree_rep_consensus <- 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"))
#'
#' ez_elevation_table(agree_rep_consensus)
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc <- 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"))
#'
#' ez_elevation_table(agree_rep_con_acc)
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_rep_all <- 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"))
#'
#' ez_elevation_table(agree_rep_all)
#'
#' @return The function returns a list of 2 objects of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_elevation_table <- function(rep_model){
# dealing with global binding issue
label <- cohen_d <- variable <- value <- NULL
rep_elevation_table <- rep_model %>%
parameterestimates() %>%
tibble::as_tibble() %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$label == "p1_p2_rel_el"|
.data$label == "self_p2_rel_el"|
.data$label == "self_p1_rel_el"|
.data$label == "p1_meta_rel_el"|
.data$label == "p2_meta_rel_el"|
# and get the variances for calculating a
# cohen's d
stringr::str_detect(.data$label, "v_") |
stringr::str_detect(.data$label, "int_")) %>%
dplyr::select(.data$label, .data$est) %>%
tidyr::spread(.data$label, .data$est)
# calculate d's
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_p2_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p1_p2_std_d = .data$p1_p2_rel_el / sqrt((.data$v_p1 + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "self_p2_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
self_p2_std_d = .data$self_p2_rel_el / sqrt((.data$v_self + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "self_p1_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
self_p1_std_d = .data$self_p1_rel_el / sqrt((.data$v_self + .data$v_p1) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_meta_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p1_meta_std_d = .data$p1_meta_rel_el / sqrt((.data$v_mp1 + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p2_meta_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p2_meta_std_d = .data$p2_meta_rel_el / sqrt((.data$v_mp2 + .data$v_p1) / 2))}
rep_elevation_table <- rep_elevation_table %>%
dplyr::select(dplyr::ends_with("std_d")) %>%
tidyr::gather(label, cohen_d) %>%
dplyr::mutate(label = stringr::str_replace_all(.data$label, "std_d", "rel_el")) %>%
dplyr::left_join(parameterestimates(rep_model)) %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$label == "p1_p2_rel_el", "P1-P2 Relative Elevation",
ifelse(.data$label == "self_p2_rel_el", "Self-P2 Relative Elevation",
ifelse(.data$label == "self_p1_rel_el", "Self-P1 Relative Elevation",
ifelse(.data$label == "p1_meta_rel_el", "P1 Meta-Elevation",
ifelse(.data$label == "p2_meta_rel_el", "P2 Meta-Elevation", NA)))))) %>%
dplyr::select(.data$parameter, .data$est,
.data$ci.lower, .data$ci.upper,
.data$cohen_d, .data$z, .data$pvalue) %>%
dplyr::rename(raw_diff =.data$est,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper) %>%
dplyr::distinct(.data$parameter, .keep_all = TRUE)
rep_descrips <- rep_model %>%
parameterestimates() %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::select(.data$label, .data$est) %>%
tidyr::spread(.data$label, .data$est)
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_self"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
self_sd = sqrt(.data$v_self))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_p1"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
p1_sd = sqrt(.data$v_p1))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_p2"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
p2_sd = sqrt(.data$v_p2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp1"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
mp1_sd = sqrt(.data$v_mp1))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp2"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
mp2_sd = sqrt(.data$v_mp2))}
rep_descrips <- rep_descrips %>%
dplyr::select(dplyr::starts_with("int"), dplyr::ends_with("sd"))
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_p2_rel_el"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p1_t_mean = .data$int_p1,
p1_t_sd = .data$p1_sd,
p2_t_mean = .data$int_p2,
p2_t_sd = .data$p2_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_self"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
t_t_mean = .data$int_self,
t_t_sd = .data$self_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp1"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p1_p2t_mean = .data$int_mp1,
p1_p2t_sd = .data$mp1_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp2"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p2_p1t_mean = .data$int_mp2,
p2_p1t_sd = .data$mp2_sd)}
rep_descrips <- rep_descrips %>%
tidyr::gather(variable, value) %>%
tidyr::separate(.data$variable, c("perceiver", "target", "stat"), extra = "merge") %>%
dplyr::mutate(target = stringr::str_replace(.data$target, "p1t", "p1_t"),
target = stringr::str_replace(.data$target, "p2t", "p2_t")) %>%
tidyr::spread(.data$stat, .data$value)
return(list(elevation_table = rep_elevation_table,
pooled_means_sd = rep_descrips))}
#' Easily Table Elevation Results from Group Moderated Models
#'
#' This takes output from one of the reputation group moderated models
#' (e.g., rep_auto_group_mod) and returns a list of two tibbles corresponding to
#' elevation (mean-differences) estimates and the relevant pooled Means and SDs respectively.
#' The elevation table contains the raw difference, 95% CI around the raw difference,
#' z scores, p values, and a cohen's d (difference / pooled SD). It has only 1 parameter.
#'
#' @param rep_model The results from one of the ReputationModelR group moderated models
#' Models (e.g., rep_auto_group_mod).
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'
#' # Consensus only Model
#' agree_rep_consensus_grpmod <- rep_auto_group_mod(data = rep_sim_data,
#' p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = c("B_C_agreeableness", "D_A_agreeableness"),
#' group_mod = "study",
#' groups_eql = "all",
#' params_eql = "all")
#'
#' ez_elevation_group_table(agree_rep_consensus_grpmod)
#'
#' ez_elevation_group_table(agree_rep_consensus_grpmod)
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc_grpmod <- rep_auto_group_mod(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"),
#' group_mod = "study", groups_eql = "all",params_eql = "all")
#'
#' ez_elevation_group_table(agree_rep_con_acc_grpmod)
#'
#' ez_elevation_group_table(agree_rep_con_acc_grpmod)
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_full_3pmeta_grpmod <- rep_auto_group_mod(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"),
#' group_mod = "study",
#' groups_eql = "all",
#' params_eql = "all")
#'
#' ez_elevation_group_table(agree_full_3pmeta_grpmod)
#'
#' ez_elevation_group_table(agree_full_3pmeta_grpmod)
#'
#' @return The function returns a list of 2 objects of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_elevation_group_table <- function(rep_model){
# dealing with binding issue
label <- cohen_d <- variable <- value <- parameter <- NULL
rep_elevation_table <- rep_model %>%
parameterestimates() %>%
tibble::as_tibble() %>%
tidyr::separate(.data$label, c("group_label", "parameter"),
extra = "merge", fill = "left") %>%
dplyr::distinct(.data$group_label, .data$parameter, .keep_all = TRUE) %>%
dplyr::filter(.data$parameter == "p1_p2_rel_el"|
.data$parameter == "self_p2_rel_el"|
.data$parameter == "self_p1_rel_el"|
.data$parameter == "p1_meta_rel_el"|
.data$parameter == "p2_meta_rel_el"|
# and get the variances for calculating a
# cohen's d
stringr::str_detect(.data$parameter, "v_") |
stringr::str_detect(.data$parameter, "int_")) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est) %>%
tidyr::spread(.data$parameter, .data$est)
# When all parameters are constrained to be equal, the above
# will produce an empty tibble. In that case, we just want to
# run the non-group elevation table function.
if(nrow(rep_elevation_table) == 0){
rep_elevation_table <- ez_elevation_table(rep_model = rep_model)[[1]]
rep_descrips <- ez_elevation_table(rep_model = rep_model)[[2]]
# provide a label of 'eql' for group_labs if there is a group variable
if(max(rep_model@ParTable[["group"]]) > 1){
rep_elevation_table <- rep_elevation_table %>%
dplyr::mutate(group_label = "eql") %>%
dplyr::select(.data$group_label, dplyr::everything())
rep_descrips <- rep_descrips %>%
dplyr::mutate(group_label = "eql") %>%
dplyr::select(.data$group_label, dplyr::everything())
}
else{
rep_elevation_table <- rep_elevation_table %>%
dplyr::mutate(group_label = "No Group Moderator in Model") %>%
dplyr::select(.data$group_label, dplyr::everything())
rep_descrips <- rep_descrips %>%
dplyr::mutate(group_label = "No Group Moderator in Model") %>%
dplyr::select(.data$group_label, dplyr::everything())
}
}
else{
# calculate d's
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_p2_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p1_p2_std_d = .data$p1_p2_rel_el / sqrt((.data$v_p1 + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "self_p2_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
self_p2_std_d = .data$self_p2_rel_el / sqrt((.data$v_self + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "self_p1_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
self_p1_std_d = .data$self_p1_rel_el / sqrt((.data$v_self + .data$v_p1) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_meta_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p1_meta_std_d = .data$p1_meta_rel_el / sqrt((.data$v_mp1 + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p2_meta_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p2_meta_std_d = .data$p2_meta_rel_el / sqrt((.data$v_mp2 + .data$v_p1) / 2))}
rel_el_only <- rep_model %>%
parameterestimates() %>%
tibble::as_tibble() %>%
tidyr::separate(.data$label, c("group_label", "parameter"),
extra = "merge", fill = "left") %>%
dplyr::distinct(.data$group_label, .data$parameter, .keep_all = TRUE) %>%
dplyr::filter(.data$parameter == "p1_p2_rel_el"|
.data$parameter == "self_p2_rel_el"|
.data$parameter == "self_p1_rel_el"|
.data$parameter == "p1_meta_rel_el"|
.data$parameter == "p2_meta_rel_el") %>%
dplyr::select(.data$group_label, .data$parameter, .data$est,
.data$se, .data$z, .data$pvalue, .data$ci.lower, .data$ci.upper)
rep_elevation_table <-
rep_elevation_table %>%
dplyr::select(.data$group_label, dplyr::ends_with("std_d")) %>%
tidyr::gather(parameter, cohen_d, -.data$group_label) %>%
dplyr::mutate(parameter = stringr::str_replace_all(.data$parameter, "std_d", "rel_el")) %>%
dplyr::left_join(rel_el_only) %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$parameter == "p1_p2_rel_el", "P1-P2 Relative Elevation",
ifelse(.data$parameter == "self_p2_rel_el", "Self-P2 Relative Elevation",
ifelse(.data$parameter == "self_p1_rel_el", "Self-P1 Relative Elevation",
ifelse(.data$parameter == "p1_meta_rel_el", "P1 Meta-Elevation",
ifelse(.data$parameter == "p2_meta_rel_el", "P2 Meta-Elevation", NA)))))) %>%
dplyr::select(.data$group_label, .data$parameter,
.data$est, .data$ci.lower, .data$ci.upper,
.data$cohen_d, .data$z, .data$pvalue) %>%
dplyr::rename(raw_diff = .data$est,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)
rep_elevation_table_eqls <- NULL
if(max(rep_model@ParTable[["group"]]) != nrow(unique(rep_elevation_table["group_label"]))){
rep_elevation_table_eqls <- ez_elevation_table(rep_model = rep_model)$elevation_table
}
if(!is.null(rep_elevation_table_eqls) && nrow(rep_elevation_table_eqls) > 0){
rep_elevation_table <- dplyr::full_join(rep_elevation_table, rep_elevation_table_eqls)
rep_elevation_table <- dplyr::mutate(rep_elevation_table, group_label = ifelse(is.na(.data$group_label), "eql", .data$group_label))
message("The Model you provided had some between-group equality constraints.
Those pooled estimates are in the rows where group is marked eql")
}
rep_descrips <- rep_model %>%
parameterestimates() %>%
tibble::as_tibble() %>%
dplyr::filter(str_detect(.data$label, "int") | str_detect(.data$label, "v")) %>%
dplyr::mutate(label = ifelse(str_detect(.data$label, "^int"),
str_replace_all(.data$label,
.data$label,
paste("eql", .data$label, sep = "_")),
ifelse(str_detect(.data$label, "^v"),
str_replace_all(.data$label,
.data$label,
paste("eql", .data$label, sep = "_")),
.data$label))) %>%
tidyr::separate(.data$label, c("group_label", "parameter"),
extra = "merge", fill = "left") %>%
dplyr::distinct(.data$group_label, .data$parameter, .keep_all = TRUE) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est) %>%
tidyr::spread(.data$parameter, .data$est)
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_self"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
self_sd = sqrt(.data$v_self))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_p1"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
p1_sd = sqrt(.data$v_p1))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_p2"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
p2_sd = sqrt(.data$v_p2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp1"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
mp1_sd = sqrt(.data$v_mp1))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp2"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
mp2_sd = sqrt(.data$v_mp2))}
rep_descrips <- rep_descrips %>%
dplyr::select(.data$group_label,
dplyr::starts_with("int"),
dplyr::ends_with("sd"))
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_p2_rel_el"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p1_t_mean = .data$int_p1,
p1_t_sd = .data$p1_sd,
p2_t_mean = .data$int_p2,
p2_t_sd = .data$p2_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_self"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
t_t_mean = .data$int_self,
t_t_sd = .data$self_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp1"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p1_p2t_mean = .data$int_mp1,
p1_p2t_sd = .data$mp1_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp2"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p2_p1t_mean = .data$int_mp2,
p2_p1t_sd = .data$mp2_sd)}
rep_descrips <- rep_descrips %>%
tidyr::gather(variable, value, -.data$group_label) %>%
tidyr::separate(.data$variable,
c("perceiver", "target", "stat"), extra = "merge") %>%
dplyr::mutate(target = stringr::str_replace(.data$target, "p1t", "p1_t"),
target = stringr::str_replace(.data$target, "p2t", "p2_t")) %>%
tidyr::spread(.data$stat, .data$value)
}
return(list(elevation_table = dplyr::arrange(rep_elevation_table, .data$group_label),
pooled_means_sd = rep_descrips))}
#' Easy Differential & Elevation Tables
#'
#' This takes output from one of the reputational analysis models
#' (e.g., rep_analyses_auto) and returns a list of length three. The first element of the list is a
#' a tibble of differential (correlational) parameters.
#' The second element is a table of elevation results. The third element is a tibble containing
#' the table of descriptives (means and SDs, pooling across exchangeable roles).
#'
#' @param rep_model The results from one of the ReputationModelR
#' Models (e.g., rep_analyses_auto).
#' @param what The parameters you want in the differential table. Current options are
#' main and all. If what = "main", then just the 'main' model parameters are provided.
#' This will include, when avaiable, hearsay consensus, hearsay accuracy,
#' direct accuracy (P1-P1 agreement), P1 Meta-Accuracy, and P2 Meta-Accuracy.
#'
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'
#' # Consensus only Model
#' agree_rep_consensus <- 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"))
#'
#' ez_tables(agree_rep_consensus, what = "main")
#' ez_tables(agree_rep_consensus, what = "all")
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc <- 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"))
#'
#' ez_tables(agree_rep_con_acc, what = "main")
#' ez_tables(agree_rep_con_acc, what = "all")
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_rep_all <- 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"))
#'
#' ez_tables(agree_rep_all, what = "main")
#' ez_tables(agree_rep_all, what = "all")
#'
#' @return The function returns a list of 2 objects of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_tables <- function(rep_model, what = "main"){
if(max(rep_model@ParTable[["group"]]) > 1){
differential_table <- ez_differential_group_table(rep_model = rep_model, what = what)
elevation_table <- ez_elevation_group_table(rep_model = rep_model)[[1]]
pooled_means_sd <- ez_elevation_group_table(rep_model = rep_model)[[2]]
}
else{
differential_table <- ez_differential_table(rep_model = rep_model, what = what)
elevation_table <- ez_elevation_table(rep_model = rep_model)[[1]]
pooled_means_sd <- ez_elevation_table(rep_model = rep_model)[[2]]
}
return(list(differential_table = differential_table,
elevation_table = elevation_table,
pooled_means_sd = pooled_means_sd))
}
Coryc3133/ReputationAnalyses documentation built on July 31, 2019, 8:35 a.m.
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Defines functions ez_differential_table ez_differential_group_table ez_id_mod_table ez_group_id_mod_table ez_elevation_table ez_elevation_group_table ez_tables
Documented in ez_differential_group_table ez_differential_table ez_elevation_group_table ez_elevation_table ez_group_id_mod_table ez_id_mod_table ez_tables
#' Easily Table Differential Results
#'
#' This takes output from one of the ReputationModelR model functions
#' (e.g., rep_analyses_auto) and returns a tibble of differential
#' (correlational) parameters. It works with any combination of P1-,
#' P2-, and 3rd person Meta-perceptions.
#'
#' The function can handle up to n exchangeable triads.
#' @param rep_model The results from one of the ReputationModelR
#' Models (e.g., rep_analyses_auto).
#' @param what The parameters you want in the table. Current options are
#' main and all. If what = "main", then just the 'main' model parameters are provided.
#' This will include, when avaiable, hearsay consensus, hearsay accuracy,
#' direct accuracy (P1-P1 agreement), P1 Meta-Accuracy, and P2 Meta-Accuracy.
#' @export
#' @import magrittr stringr lavaan
#' @importFrom rlang .data
#' @examples data("rep_sim_data")
#' # Consensus only Model
#' agree_rep_consensus <- 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"))
#'
#' ez_differential_table(agree_rep_consensus, what = "main")
#'
#' ez_differential_table(agree_rep_consensus, what = "all")
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc <- 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"))
#'
#' ez_differential_table(agree_rep_con_acc, what = "main")
#'
#' ez_differential_table(agree_rep_con_acc, what = "all")
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_rep_all <- 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"))
#'
#' ez_differential_table(agree_rep_all, what = "main")
#'
#' ez_differential_table(agree_rep_all, what = "all")
#'
#' @return The function returns an object of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_differential_table <- function(rep_model,
what = "main"){
# First save out labels
# we need these to remove the repeats (from equality constraints)
labels <-
rep_model@ParTable %>%
tibble::as_tibble() %>%
dplyr::select(.data$lhs, .data$op, .data$rhs, .data$label)
# Main parameters only
if(what == "main"){
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$label == "hc" |
.data$label == "ha" |
.data$label == "da" |
.data$label == "p1ma"|
.data$label == "p2ma") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$label == "ha", "hearsay accuracy",
ifelse(.data$label == "hc", "hearsay consensus",
ifelse(.data$label == "da", "direct accuracy",
ifelse(.data$label == "p1ma", "P1 Meta-Accuracy",
ifelse(.data$label == "p2ma", "P2 Meta-Accuracy", NA)))))) %>%
dplyr::select(.data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(r = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)}
if(what == "all"){
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$label == "ha"|
.data$label == "hc"|
.data$label == "da"|
.data$label == "p1ma"|
.data$label == "p2ma"|
.data$label == "as_ac1"|
.data$label == "as_con1"|
.data$label == "mp_rec"|
.data$label == "as_ac2"|
.data$label == "as_con2"|
.data$label == "rec"|
.data$label == "h"|
.data$label == "m"|
.data$label == "tru_sim"|
.data$label == "as_sim_3p"|
.data$label == "as_sim_1p"|
.data$label == "as_sim_p1m"|
.data$label == "ukp1m1"|
.data$label == "p1meta_sim"|
.data$label == "ukp2m1"|
.data$label == "ukp2m3"|
.data$label == "p2meta_sim"|
.data$label == "ukm1") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$label == "ha", "hearsay accuracy",
ifelse(.data$label == "hc", "hearsay consensus",
ifelse(.data$label == "da", "direct accuracy",
ifelse(.data$label == "p1ma", "P1 Meta-Accuracy",
ifelse(.data$label == "p2ma", "P2 Meta-Accuracy",
ifelse(.data$label == "as_ac1", "P1 Assumed Accuracy",
ifelse(.data$label == "as_con1", "P1 Assumed Consensus",
ifelse(.data$label == "mp_rec", "P1-P2 Meta-Perception Reciprocity",
ifelse(.data$label == "as_ac2", "P2 Assumed Accuracy",
ifelse(.data$label == "as_con2", "P2 Assumed Consensus",
ifelse(.data$label == "rec", "direct reciprocity",
ifelse(.data$label == "h", "hearsay reciprocity",
ifelse(.data$label == "m", "P2(T) <-> opposite P1(T)",
ifelse(.data$label == "tru_sim", "True Target Similarity",
ifelse(.data$label == "as_sim_3p", "Third-Person Assumed Similarity",
ifelse(.data$label == "as_sim_1p", "First-Person Assumed Similarity",
ifelse(.data$label == "as_sim_p1m", "P1 Meta- Assumed Similarity",
ifelse(.data$label == "ukp1m1", "P1 Meta <-> opposite P1-Report",
ifelse(.data$label == "p1meta_sim", "P1 meta-similarity",
ifelse(.data$label == "ukp2m1", "P2 Meta <-> opposite target self-report",
ifelse(.data$label == "ukp2m3", "P2 meta <-> opposite P1-report",
ifelse(.data$label == "p2meta_sim", "P2 Meta-similarity",
ifelse(.data$label == "ukm1", "P1 Meta <-> opposite P2 Meta",
NA)))))))))))))))))))))))) %>%
dplyr::select(.data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(r = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)}
return(rep_parameter_table)}
#' Easily Table Differential Results from Group Moderated Models
#'
#' This takes output from one of the group moderated reputation models
#' (e.g., rep_analyses_auto) and returns a tibble of differential
#' (correlational) parameters. It works with any of the group moderated models.
#' @param rep_model The results from one of the ReputationModelR
#' group Models (e.g., rep_auto_group_mod). The model can have between-group equality
#' constraints; those are marked by missing values (NA) in the group_label column.
#' @param what The parameters you want in the table. Current options are
#' main and all. If what = "main", then just the 'main' model parameters are provided.
#' This will include, when avaiable, hearsay consensus, hearsay accuracy,
#' direct accuracy (P1-P1 agreement), P1 Meta-Accuracy, and P2 Meta-Accuracy.
#' @export
#' @import magrittr stringr lavaan
#' @examples data("rep_sim_data")
#' # Consensus only Model
#' agree_rep_consensus_grpmod <- rep_auto_group_mod(data = rep_sim_data,
#' p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = c("B_C_agreeableness", "D_A_agreeableness"),
#' group_mod = "study",
#' groups_eql = "all",
#' params_eql = "all")
#'
#' ez_differential_group_table(agree_rep_consensus_grpmod, what = "main")
#'
#' ez_differential_group_table(agree_rep_consensus_grpmod, what = "all")
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc_grpmod <- rep_auto_group_mod(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"),
#' group_mod = "study", groups_eql = "all",params_eql = "all")
#'
#' ez_differential_group_table(agree_rep_con_acc_grpmod, what = "main")
#'
#' ez_differential_group_table(agree_rep_con_acc_grpmod, what = "all")
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_full_3pmeta_grpmod <- rep_auto_group_mod(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"),
#' group_mod = "study",
#' groups_eql = "all",
#' params_eql = "all")
#'
#' ez_differential_group_table(agree_full_3pmeta_grpmod, what = "main")
#'
#' ez_differential_group_table(agree_full_3pmeta_grpmod, what = "all")
#' @return The function returns an object of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_differential_group_table <- function(rep_model,
what = "main"){
# First save out labels
# we need these to remove the repeats (from equality constraints)
labels <- rep_model@ParTable %>%
tibble::as_tibble() %>%
dplyr::select(.data$lhs, .data$op, .data$rhs, .data$group, .data$label) %>%
tidyr::separate(.data$label, c("group_label", "param_label"),
extra = "merge", fill = "left") %>%
dplyr::filter(.data$group_label != "")
# Main parameters only
if(what == "main"){
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$group_label, .data$param_label, .keep_all = TRUE) %>%
dplyr::filter(.data$param_label == "hc" |
.data$param_label == "ha" |
.data$param_label == "da" |
.data$param_label == "p1ma"|
.data$param_label == "p2ma") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$param_label == "ha", "hearsay accuracy",
ifelse(.data$param_label == "hc", "hearsay consensus",
ifelse(.data$param_label == "da", "direct accuracy",
ifelse(.data$param_label == "p1ma", "P1 Meta-Accuracy",
ifelse(.data$param_label == "p2ma", "P2 Meta-Accuracy", NA)))))) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(r = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)}
if(what == "all"){
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$param_label == "ha"|
.data$param_label == "hc"|
.data$param_label == "da"|
.data$param_label == "p1ma"|
.data$param_label == "p2ma"|
.data$param_label == "as_ac1"|
.data$param_label == "as_con1"|
.data$param_label == "mp_rec"|
.data$param_label == "as_ac2"|
.data$param_label == "as_con2"|
.data$param_label == "rec"|
.data$param_label == "h"|
.data$param_label == "m"|
.data$param_label == "tru_sim"|
.data$param_label == "as_sim_3p"|
.data$param_label == "as_sim_1p"|
.data$param_label == "as_sim_p1m"|
.data$param_label == "ukp1m1"|
.data$param_label == "p1meta_sim"|
.data$param_label == "ukp2m1"|
.data$param_label == "ukp2m3"|
.data$param_label == "p2meta_sim"|
.data$param_label == "ukm1") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$param_label == "ha", "hearsay accuracy",
ifelse(.data$param_label == "hc", "hearsay consensus",
ifelse(.data$param_label == "da", "direct accuracy",
ifelse(.data$param_label == "p1ma", "P1 Meta-Accuracy",
ifelse(.data$param_label == "p2ma", "P2 Meta-Accuracy",
ifelse(.data$param_label == "as_ac1", "P1 Assumed Accuracy",
ifelse(.data$param_label == "as_con1", "P1 Assumed Consensus",
ifelse(.data$param_label == "mp_rec", "P1-P2 Meta-Perception Reciprocity",
ifelse(.data$param_label == "as_ac2", "P2 Assumed Accuracy",
ifelse(.data$param_label == "as_con2", "P2 Assumed Consensus",
ifelse(.data$param_label == "rec", "direct reciprocity",
ifelse(.data$param_label == "h", "hearsay reciprocity",
ifelse(.data$param_label == "m", "P2(T) <-> opposite P1(T)",
ifelse(.data$param_label == "tru_sim", "True Target Similarity",
ifelse(.data$param_label == "as_sim_3p", "Third-Person Assumed Similarity",
ifelse(.data$param_label == "as_sim_1p", "First-Person Assumed Similarity",
ifelse(.data$param_label == "as_sim_p1m", "P1 Meta- Assumed Similarity",
ifelse(.data$param_label == "ukp1m1", "P1 Meta <-> opposite P1-Report",
ifelse(.data$param_label == "p1meta_sim", "P1 meta-similarity",
ifelse(.data$param_label == "ukp2m1", "P2 Meta <-> opposite target self-report",
ifelse(.data$param_label == "ukp2m3", "P2 meta <-> opposite P1-report",
ifelse(.data$param_label == "p2meta_sim", "P2 Meta-similarity",
ifelse(.data$param_label == "ukm1", "P1 Meta <-> opposite P2 Meta",
NA)))))))))))))))))))))))) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(r = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)
}
# the function above will only get parameters without equality constraints
# which sort of makes sense for this (it only gives you the results of group moderated analyses).
# However, you might have a case in which some (but not all) groups or parameters are equal, and you
# want to table them all together. This adds the equality constrained rows
if(nrow(rep_parameter_table) == 0){
rep_parameter_table <- ez_differential_table(rep_model = rep_model,
what = what)
}
else{
rep_parameter_table_eqls <- NULL
if(max(rep_model@ParTable[["group"]]) != nrow(unique(rep_parameter_table["group_label"]))){
rep_parameter_table_eqls <- ez_differential_table(rep_model = rep_model,
what = what)
}
if(!is.null(rep_parameter_table_eqls) && nrow(rep_parameter_table_eqls) > 0){
rep_parameter_table <- dplyr::full_join(rep_parameter_table, rep_parameter_table_eqls)
rep_parameter_table <- dplyr::mutate(rep_parameter_table, group_label = ifelse(is.na(.data$group_label), "eql", .data$group_label))
message("The Model you provided had some between-group equality constraints.
Those pooled estimates are in the rows where group is marked eql")
}
}
return(rep_parameter_table)}
#' Easily Table Individual Difference Moderator Results
#'
#' This takes output from one of the individual-level moderated reputation models
#' (e.g., rep_auto_id_mods) and returns a tibble of model
#' (regression) parameters. It works with any of the Individual-level moderated models.
#' @param rep_model The results from one of the ReputationModelR
#' individual-level moderator Models (e.g., rep_auto_id_mods).
#' @export
#' @import magrittr stringr lavaan
#' @examples data("rep_sim_data")
#' moderator_data <- rep_sim_data %>%
#' dplyr::mutate(B_C_agreeableness_cent = scale(B_C_agreeableness, scale = FALSE),
#' D_A_agreeableness_cent = scale(D_A_agreeableness, scale = FALSE),
#' B_iri_perspective_cent = scale(B_iri_perspective, scale = FALSE),
#' D_iri_perspective_cent = scale(D_iri_perspective, scale = FALSE),
#' B_ptXagree_interaction = B_C_agreeableness_cent*B_iri_perspective_cent,
#' D_ptXagree_interaction = D_A_agreeableness_cent*D_iri_perspective_cent)
#'
#' # Example for hearsay accuracy
#' agree_ha_p2ptmod_model <- rep_auto_id_mods(data = moderator_data,
#' target_self = c("C_C_agreeableness", "A_A_agreeableness"),
#' p2_reports = c("B_C_agreeableness_cent", "D_A_agreeableness_cent"),
#' id_mod_variable = c("B_iri_perspective_cent", "D_iri_perspective_cent"),
#' interaction_term = c("B_ptXagree_interaction", "D_ptXagree_interaction"))
#' ez_id_mod_table(agree_ha_p2ptmod_model)
#'
#'
#' @return The function returns an object of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_id_mod_table <- function(rep_model){
# First save out labels
# we need these to remove the repeats (from equality constraints)
labels <- rep_model@ParTable %>%
tibble::as_tibble() %>%
dplyr::select(.data$lhs, .data$op, .data$rhs, .data$label)
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$label == "hc_me" |
.data$label == "ha_me" |
.data$label == "mod_me" |
.data$label == "interaction") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$label == "hc_me", "hearsay consensus (main effect)",
ifelse(.data$label == "ha_me", "hearsay accuracy (main effect)",
ifelse(.data$label == "mod_me", "moderator (main effect)",
ifelse(.data$label == "interaction", "interaction effect",NA))))) %>%
dplyr::select(.data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(beta = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)
return(rep_parameter_table)}
#' Easily Table Model with Group and Individual Difference Moderators
#'
#' This takes output from one of the group- and individual-level moderated reputation models
#' (e.g., rep_accuracy_group_id_mods) and returns a tibble of model
#' (regression) parameters. It works with any of the Group- and Individual-level moderated models.
#' @param rep_model The results from one of the ReputationModelR
#' group- and individual-level moderator Models (e.g., rep_accuracy_group_id_mods).
#' @export
#' @examples data("rep_sim_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 with no equality constraints
#' agree_pt_mod_fit <- rep_accuracy_group_id_mods(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"),
#' group_mod = "study")
#' ez_group_id_mod_table(agree_pt_mod_fit)
#'
#' # Example for hearsay accuracy with all parameters equal across all groups
#' agree_pt_mod_fit_alleq <- rep_accuracy_group_id_mods(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"),
#' group_mod = "study", groups_eql = "all", params_eql = "all")
#' ez_group_id_mod_table(agree_pt_mod_fit_alleq)
#'
#' # Example with some equality constraints
#'
#' agree_pt_mod_fit_someeql <- rep_accuracy_group_id_mods(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"),
#' group_mod = "group_var", groups_eql = c(1, 4), params_eql = "all")
#'
#' ez_group_id_mod_table(agree_pt_mod_fit_someeql)
#' @return The function returns an object of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_group_id_mod_table <- function(rep_model){
# First save out labels
# we need these to remove the repeats (from equality constraints)
labels <- rep_model@ParTable%>%
tibble::as_tibble() %>%
dplyr::select(.data$lhs, .data$op, .data$rhs, .data$group, .data$label) %>%
tidyr::separate(.data$label, c("group_label", "param_label"), extra = "merge", fill = "left") %>%
dplyr::filter(.data$group_label != "")
rep_parameter_table <- rep_model %>%
standardizedsolution() %>%
tibble::as_tibble() %>%
dplyr::full_join(labels) %>%
dplyr::filter(.data$lhs != .data$rhs &
.data$op != "~1") %>%
dplyr::distinct(.data$group_label, .data$param_label, .keep_all = TRUE) %>%
dplyr::filter(.data$param_label == "hc_me" |
.data$param_label == "ha_me" |
.data$param_label == "mod_me" |
.data$param_label == "interaction") %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$param_label == "hc_me", "hearsay consensus (main effect)",
ifelse(.data$param_label == "ha_me", "hearsay accuracy (main effect)",
ifelse(.data$param_label == "mod_me", "moderator (main effect)",
ifelse(.data$param_label == "interaction", "interaction effect",NA))))) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est.std,
.data$ci.lower, .data$ci.upper, .data$pvalue) %>%
dplyr::rename(beta = .data$est.std,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)
# the function above will only get parameters without equality constraints
# which sort of makes sense for this (it only gives you the results of group moderated analyses).
# However, you might have a case in which some (but not all) groups or parameters are equal, and you
# want to table them all together. This adds the equality constrained rows
if(nrow(rep_parameter_table) == 0){
rep_parameter_table <- ez_id_mod_table(rep_model = rep_model)
}
else{
rep_parameter_table_eqls <- NULL
if(max(rep_model@ParTable[["group"]]) != nrow(unique(rep_parameter_table["group_label"]))){
rep_parameter_table_eqls <- ez_id_mod_table(rep_model = rep_model)
}
if(!is.null(rep_parameter_table_eqls) && nrow(rep_parameter_table_eqls) > 0){
rep_parameter_table <- dplyr::full_join(rep_parameter_table, rep_parameter_table_eqls)
rep_parameter_table <- dplyr::mutate(rep_parameter_table, group_label = ifelse(is.na(.data$group_label), "eql", .data$group_label))
message("The Model you provided had some between-group equality constraints.
Those pooled estimates are in the rows where group is marked eql")
}
}
return(rep_parameter_table)}
#' Easily Table Elevation Results
#'
#' This takes output from one of the reputational analysis models
#' (e.g., rep_analyses_auto) and returns a list of two tibbles corresponding to
#' elevation (mean-differences) estimates and the relevant pooled Means and SDs respectively.
#' The elevation table contains the raw difference, 95% CI around the raw difference,
#' z scores, p values, and a cohen's d (difference / pooled SD). It has only 1 parameter.
#'
#' @param rep_model The results from one of the ReputationModelR
#' Models (e.g., rep_analyses_auto).
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'
#' # Consensus only Model
#' agree_rep_consensus <- 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"))
#'
#' ez_elevation_table(agree_rep_consensus)
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc <- 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"))
#'
#' ez_elevation_table(agree_rep_con_acc)
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_rep_all <- 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"))
#'
#' ez_elevation_table(agree_rep_all)
#'
#' @return The function returns a list of 2 objects of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_elevation_table <- function(rep_model){
# dealing with global binding issue
label <- cohen_d <- variable <- value <- NULL
rep_elevation_table <- rep_model %>%
parameterestimates() %>%
tibble::as_tibble() %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::filter(.data$label == "p1_p2_rel_el"|
.data$label == "self_p2_rel_el"|
.data$label == "self_p1_rel_el"|
.data$label == "p1_meta_rel_el"|
.data$label == "p2_meta_rel_el"|
# and get the variances for calculating a
# cohen's d
stringr::str_detect(.data$label, "v_") |
stringr::str_detect(.data$label, "int_")) %>%
dplyr::select(.data$label, .data$est) %>%
tidyr::spread(.data$label, .data$est)
# calculate d's
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_p2_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p1_p2_std_d = .data$p1_p2_rel_el / sqrt((.data$v_p1 + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "self_p2_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
self_p2_std_d = .data$self_p2_rel_el / sqrt((.data$v_self + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "self_p1_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
self_p1_std_d = .data$self_p1_rel_el / sqrt((.data$v_self + .data$v_p1) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_meta_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p1_meta_std_d = .data$p1_meta_rel_el / sqrt((.data$v_mp1 + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p2_meta_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p2_meta_std_d = .data$p2_meta_rel_el / sqrt((.data$v_mp2 + .data$v_p1) / 2))}
rep_elevation_table <- rep_elevation_table %>%
dplyr::select(dplyr::ends_with("std_d")) %>%
tidyr::gather(label, cohen_d) %>%
dplyr::mutate(label = stringr::str_replace_all(.data$label, "std_d", "rel_el")) %>%
dplyr::left_join(parameterestimates(rep_model)) %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$label == "p1_p2_rel_el", "P1-P2 Relative Elevation",
ifelse(.data$label == "self_p2_rel_el", "Self-P2 Relative Elevation",
ifelse(.data$label == "self_p1_rel_el", "Self-P1 Relative Elevation",
ifelse(.data$label == "p1_meta_rel_el", "P1 Meta-Elevation",
ifelse(.data$label == "p2_meta_rel_el", "P2 Meta-Elevation", NA)))))) %>%
dplyr::select(.data$parameter, .data$est,
.data$ci.lower, .data$ci.upper,
.data$cohen_d, .data$z, .data$pvalue) %>%
dplyr::rename(raw_diff =.data$est,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper) %>%
dplyr::distinct(.data$parameter, .keep_all = TRUE)
rep_descrips <- rep_model %>%
parameterestimates() %>%
dplyr::distinct(.data$label, .keep_all = TRUE) %>%
dplyr::select(.data$label, .data$est) %>%
tidyr::spread(.data$label, .data$est)
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_self"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
self_sd = sqrt(.data$v_self))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_p1"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
p1_sd = sqrt(.data$v_p1))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_p2"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
p2_sd = sqrt(.data$v_p2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp1"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
mp1_sd = sqrt(.data$v_mp1))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp2"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
mp2_sd = sqrt(.data$v_mp2))}
rep_descrips <- rep_descrips %>%
dplyr::select(dplyr::starts_with("int"), dplyr::ends_with("sd"))
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_p2_rel_el"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p1_t_mean = .data$int_p1,
p1_t_sd = .data$p1_sd,
p2_t_mean = .data$int_p2,
p2_t_sd = .data$p2_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_self"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
t_t_mean = .data$int_self,
t_t_sd = .data$self_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp1"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p1_p2t_mean = .data$int_mp1,
p1_p2t_sd = .data$mp1_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp2"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p2_p1t_mean = .data$int_mp2,
p2_p1t_sd = .data$mp2_sd)}
rep_descrips <- rep_descrips %>%
tidyr::gather(variable, value) %>%
tidyr::separate(.data$variable, c("perceiver", "target", "stat"), extra = "merge") %>%
dplyr::mutate(target = stringr::str_replace(.data$target, "p1t", "p1_t"),
target = stringr::str_replace(.data$target, "p2t", "p2_t")) %>%
tidyr::spread(.data$stat, .data$value)
return(list(elevation_table = rep_elevation_table,
pooled_means_sd = rep_descrips))}
#' Easily Table Elevation Results from Group Moderated Models
#'
#' This takes output from one of the reputation group moderated models
#' (e.g., rep_auto_group_mod) and returns a list of two tibbles corresponding to
#' elevation (mean-differences) estimates and the relevant pooled Means and SDs respectively.
#' The elevation table contains the raw difference, 95% CI around the raw difference,
#' z scores, p values, and a cohen's d (difference / pooled SD). It has only 1 parameter.
#'
#' @param rep_model The results from one of the ReputationModelR group moderated models
#' Models (e.g., rep_auto_group_mod).
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'
#' # Consensus only Model
#' agree_rep_consensus_grpmod <- rep_auto_group_mod(data = rep_sim_data,
#' p1_reports = c("A_C_agreeableness", "C_A_agreeableness"),
#' p2_reports = c("B_C_agreeableness", "D_A_agreeableness"),
#' group_mod = "study",
#' groups_eql = "all",
#' params_eql = "all")
#'
#' ez_elevation_group_table(agree_rep_consensus_grpmod)
#'
#' ez_elevation_group_table(agree_rep_consensus_grpmod)
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc_grpmod <- rep_auto_group_mod(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"),
#' group_mod = "study", groups_eql = "all",params_eql = "all")
#'
#' ez_elevation_group_table(agree_rep_con_acc_grpmod)
#'
#' ez_elevation_group_table(agree_rep_con_acc_grpmod)
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_full_3pmeta_grpmod <- rep_auto_group_mod(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"),
#' group_mod = "study",
#' groups_eql = "all",
#' params_eql = "all")
#'
#' ez_elevation_group_table(agree_full_3pmeta_grpmod)
#'
#' ez_elevation_group_table(agree_full_3pmeta_grpmod)
#'
#' @return The function returns a list of 2 objects of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_elevation_group_table <- function(rep_model){
# dealing with binding issue
label <- cohen_d <- variable <- value <- parameter <- NULL
rep_elevation_table <- rep_model %>%
parameterestimates() %>%
tibble::as_tibble() %>%
tidyr::separate(.data$label, c("group_label", "parameter"),
extra = "merge", fill = "left") %>%
dplyr::distinct(.data$group_label, .data$parameter, .keep_all = TRUE) %>%
dplyr::filter(.data$parameter == "p1_p2_rel_el"|
.data$parameter == "self_p2_rel_el"|
.data$parameter == "self_p1_rel_el"|
.data$parameter == "p1_meta_rel_el"|
.data$parameter == "p2_meta_rel_el"|
# and get the variances for calculating a
# cohen's d
stringr::str_detect(.data$parameter, "v_") |
stringr::str_detect(.data$parameter, "int_")) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est) %>%
tidyr::spread(.data$parameter, .data$est)
# When all parameters are constrained to be equal, the above
# will produce an empty tibble. In that case, we just want to
# run the non-group elevation table function.
if(nrow(rep_elevation_table) == 0){
rep_elevation_table <- ez_elevation_table(rep_model = rep_model)[[1]]
rep_descrips <- ez_elevation_table(rep_model = rep_model)[[2]]
# provide a label of 'eql' for group_labs if there is a group variable
if(max(rep_model@ParTable[["group"]]) > 1){
rep_elevation_table <- rep_elevation_table %>%
dplyr::mutate(group_label = "eql") %>%
dplyr::select(.data$group_label, dplyr::everything())
rep_descrips <- rep_descrips %>%
dplyr::mutate(group_label = "eql") %>%
dplyr::select(.data$group_label, dplyr::everything())
}
else{
rep_elevation_table <- rep_elevation_table %>%
dplyr::mutate(group_label = "No Group Moderator in Model") %>%
dplyr::select(.data$group_label, dplyr::everything())
rep_descrips <- rep_descrips %>%
dplyr::mutate(group_label = "No Group Moderator in Model") %>%
dplyr::select(.data$group_label, dplyr::everything())
}
}
else{
# calculate d's
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_p2_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p1_p2_std_d = .data$p1_p2_rel_el / sqrt((.data$v_p1 + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "self_p2_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
self_p2_std_d = .data$self_p2_rel_el / sqrt((.data$v_self + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "self_p1_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
self_p1_std_d = .data$self_p1_rel_el / sqrt((.data$v_self + .data$v_p1) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_meta_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p1_meta_std_d = .data$p1_meta_rel_el / sqrt((.data$v_mp1 + .data$v_p2) / 2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p2_meta_rel_el"))) > 0){
rep_elevation_table <- dplyr::mutate(rep_elevation_table,
p2_meta_std_d = .data$p2_meta_rel_el / sqrt((.data$v_mp2 + .data$v_p1) / 2))}
rel_el_only <- rep_model %>%
parameterestimates() %>%
tibble::as_tibble() %>%
tidyr::separate(.data$label, c("group_label", "parameter"),
extra = "merge", fill = "left") %>%
dplyr::distinct(.data$group_label, .data$parameter, .keep_all = TRUE) %>%
dplyr::filter(.data$parameter == "p1_p2_rel_el"|
.data$parameter == "self_p2_rel_el"|
.data$parameter == "self_p1_rel_el"|
.data$parameter == "p1_meta_rel_el"|
.data$parameter == "p2_meta_rel_el") %>%
dplyr::select(.data$group_label, .data$parameter, .data$est,
.data$se, .data$z, .data$pvalue, .data$ci.lower, .data$ci.upper)
rep_elevation_table <-
rep_elevation_table %>%
dplyr::select(.data$group_label, dplyr::ends_with("std_d")) %>%
tidyr::gather(parameter, cohen_d, -.data$group_label) %>%
dplyr::mutate(parameter = stringr::str_replace_all(.data$parameter, "std_d", "rel_el")) %>%
dplyr::left_join(rel_el_only) %>%
# give them their substantive labels
dplyr::mutate(parameter = ifelse(.data$parameter == "p1_p2_rel_el", "P1-P2 Relative Elevation",
ifelse(.data$parameter == "self_p2_rel_el", "Self-P2 Relative Elevation",
ifelse(.data$parameter == "self_p1_rel_el", "Self-P1 Relative Elevation",
ifelse(.data$parameter == "p1_meta_rel_el", "P1 Meta-Elevation",
ifelse(.data$parameter == "p2_meta_rel_el", "P2 Meta-Elevation", NA)))))) %>%
dplyr::select(.data$group_label, .data$parameter,
.data$est, .data$ci.lower, .data$ci.upper,
.data$cohen_d, .data$z, .data$pvalue) %>%
dplyr::rename(raw_diff = .data$est,
ci_lower = .data$ci.lower,
ci_upper = .data$ci.upper)
rep_elevation_table_eqls <- NULL
if(max(rep_model@ParTable[["group"]]) != nrow(unique(rep_elevation_table["group_label"]))){
rep_elevation_table_eqls <- ez_elevation_table(rep_model = rep_model)$elevation_table
}
if(!is.null(rep_elevation_table_eqls) && nrow(rep_elevation_table_eqls) > 0){
rep_elevation_table <- dplyr::full_join(rep_elevation_table, rep_elevation_table_eqls)
rep_elevation_table <- dplyr::mutate(rep_elevation_table, group_label = ifelse(is.na(.data$group_label), "eql", .data$group_label))
message("The Model you provided had some between-group equality constraints.
Those pooled estimates are in the rows where group is marked eql")
}
rep_descrips <- rep_model %>%
parameterestimates() %>%
tibble::as_tibble() %>%
dplyr::filter(str_detect(.data$label, "int") | str_detect(.data$label, "v")) %>%
dplyr::mutate(label = ifelse(str_detect(.data$label, "^int"),
str_replace_all(.data$label,
.data$label,
paste("eql", .data$label, sep = "_")),
ifelse(str_detect(.data$label, "^v"),
str_replace_all(.data$label,
.data$label,
paste("eql", .data$label, sep = "_")),
.data$label))) %>%
tidyr::separate(.data$label, c("group_label", "parameter"),
extra = "merge", fill = "left") %>%
dplyr::distinct(.data$group_label, .data$parameter, .keep_all = TRUE) %>%
dplyr::select(.data$group_label, .data$parameter, .data$est) %>%
tidyr::spread(.data$parameter, .data$est)
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_self"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
self_sd = sqrt(.data$v_self))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_p1"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
p1_sd = sqrt(.data$v_p1))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_p2"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
p2_sd = sqrt(.data$v_p2))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp1"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
mp1_sd = sqrt(.data$v_mp1))}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp2"))) > 0){
rep_descrips <- dplyr::mutate(rep_descrips,
mp2_sd = sqrt(.data$v_mp2))}
rep_descrips <- rep_descrips %>%
dplyr::select(.data$group_label,
dplyr::starts_with("int"),
dplyr::ends_with("sd"))
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "p1_p2_rel_el"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p1_t_mean = .data$int_p1,
p1_t_sd = .data$p1_sd,
p2_t_mean = .data$int_p2,
p2_t_sd = .data$p2_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_self"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
t_t_mean = .data$int_self,
t_t_sd = .data$self_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp1"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p1_p2t_mean = .data$int_mp1,
p1_p2t_sd = .data$mp1_sd)}
if(sum(as.numeric(stringr::str_detect(parameterestimates(rep_model)$label, "v_mp2"))) > 0){
rep_descrips <- dplyr::rename(rep_descrips,
p2_p1t_mean = .data$int_mp2,
p2_p1t_sd = .data$mp2_sd)}
rep_descrips <- rep_descrips %>%
tidyr::gather(variable, value, -.data$group_label) %>%
tidyr::separate(.data$variable,
c("perceiver", "target", "stat"), extra = "merge") %>%
dplyr::mutate(target = stringr::str_replace(.data$target, "p1t", "p1_t"),
target = stringr::str_replace(.data$target, "p2t", "p2_t")) %>%
tidyr::spread(.data$stat, .data$value)
}
return(list(elevation_table = dplyr::arrange(rep_elevation_table, .data$group_label),
pooled_means_sd = rep_descrips))}
#' Easy Differential & Elevation Tables
#'
#' This takes output from one of the reputational analysis models
#' (e.g., rep_analyses_auto) and returns a list of length three. The first element of the list is a
#' a tibble of differential (correlational) parameters.
#' The second element is a table of elevation results. The third element is a tibble containing
#' the table of descriptives (means and SDs, pooling across exchangeable roles).
#'
#' @param rep_model The results from one of the ReputationModelR
#' Models (e.g., rep_analyses_auto).
#' @param what The parameters you want in the differential table. Current options are
#' main and all. If what = "main", then just the 'main' model parameters are provided.
#' This will include, when avaiable, hearsay consensus, hearsay accuracy,
#' direct accuracy (P1-P1 agreement), P1 Meta-Accuracy, and P2 Meta-Accuracy.
#'
#' @import magrittr stringr lavaan
#' @export
#' @examples data("rep_sim_data")
#'
#' # Consensus only Model
#' agree_rep_consensus <- 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"))
#'
#' ez_tables(agree_rep_consensus, what = "main")
#' ez_tables(agree_rep_consensus, what = "all")
#'
#' # Consensus & Accuracy
#'
#' agree_rep_con_acc <- 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"))
#'
#' ez_tables(agree_rep_con_acc, what = "main")
#' ez_tables(agree_rep_con_acc, what = "all")
#'
#' # Consensus, Accuracy, 3rd Person Meta
#'
#' agree_rep_all <- 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"))
#'
#' ez_tables(agree_rep_all, what = "main")
#' ez_tables(agree_rep_all, what = "all")
#'
#' @return The function returns a list of 2 objects of class \code{\link[tibble:tbl_df-class]{tbl_df}}.
ez_tables <- function(rep_model, what = "main"){
if(max(rep_model@ParTable[["group"]]) > 1){
differential_table <- ez_differential_group_table(rep_model = rep_model, what = what)
elevation_table <- ez_elevation_group_table(rep_model = rep_model)[[1]]
pooled_means_sd <- ez_elevation_group_table(rep_model = rep_model)[[2]]
}
else{
differential_table <- ez_differential_table(rep_model = rep_model, what = what)
elevation_table <- ez_elevation_table(rep_model = rep_model)[[1]]
pooled_means_sd <- ez_elevation_table(rep_model = rep_model)[[2]]
}
return(list(differential_table = differential_table,
elevation_table = elevation_table,
pooled_means_sd = pooled_means_sd))
}
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