#' @rdname ma_d
#' @export
#' @import dplyr
#' @aliases ma_d_barebones
ma_d_bb <- ma_d_barebones <- function(d, n1, n2 = rep(NA, length(d)), n_adj = NULL, sample_id = NULL, citekey = NULL,
wt_type = c("n_effective", "sample_size", "inv_var_mean", "inv_var_sample",
"DL", "HE", "HS", "SJ", "ML", "REML", "EB", "PM"),
correct_bias = TRUE,
moderators = NULL, cat_moderators = TRUE,
moderator_type = c("simple", "hierarchical", "none"),
data = NULL, control = control_psychmeta(), ...){
.dplyr.show_progress <- options()$dplyr.show_progress
.psychmeta.show_progress <- psychmeta.show_progress <- options()$psychmeta.show_progress
if(is.null(psychmeta.show_progress)) psychmeta.show_progress <- TRUE
options(dplyr.show_progress = psychmeta.show_progress)
warn_obj1 <- record_warnings()
call <- match.call()
wt_type <- match.arg(wt_type, choices = c("n_effective", "sample_size", "inv_var_mean", "inv_var_sample",
"DL", "HE", "HS", "SJ", "ML", "REML", "EB", "PM"))
moderator_type <- match.arg(moderator_type, choices = c("simple", "hierarchical", "none"))
control <- control_psychmeta(.psychmeta_ellipse_args = list(...),
.control_psychmeta_arg = control)
error_type <- control$error_type
conf_level <- control$conf_level
cred_level <- control$cred_level
conf_method <- control$conf_method
cred_method <- control$cred_method
var_unbiased <- control$var_unbiased
hs_override <- control$hs_override
if(hs_override){
wt_type <- "sample_size"
error_type <- "mean"
correct_bias <- TRUE
conf_method <- cred_method <- "norm"
var_unbiased <- FALSE
}
correct_bias <- scalar_arg_warning(arg = correct_bias, arg_name = "correct_bias")
moderator_type <- scalar_arg_warning(arg = moderator_type, arg_name = "moderator_type")
wt_type <- scalar_arg_warning(arg = wt_type, arg_name = "wt_type")
error_type <- scalar_arg_warning(arg = error_type, arg_name = "error_type")
conf_method <- scalar_arg_warning(arg = conf_method, arg_name = "conf_method")
cred_method <- scalar_arg_warning(arg = cred_method, arg_name = "cred_method")
conf_level <- interval_warning(interval = conf_level, interval_name = "conf_level", default = .95)
cred_level <- interval_warning(interval = cred_level, interval_name = "cred_level", default = .8)
formal_args <- formals(ma_d_bb)
formal_args[["..."]] <- NULL
for(i in names(formal_args)) if(i %in% names(call)) formal_args[[i]] <- NULL
call_full <- as.call(append(as.list(call), formal_args))
if(!is.null(data)){
data <- as.data.frame(data, stringsAsFactors = FALSE)
d <- match_variables(call = call_full[[match("d", names(call_full))]], arg = d, arg_name = "d", data = data)
n1 <- match_variables(call = call_full[[match("n1", names(call_full))]], arg = n1, arg_name = "n1", data = data)
n2 <- match_variables(call = call_full[[match("n2", names(call_full))]], arg = n2, arg_name = "n2", data = data)
n_adj <- match_variables(call = call_full[[match("n_adj", names(call_full))]], arg = n_adj, arg_name = "n_adj", data = data)
if(deparse(substitute(sample_id))[1] != "NULL")
sample_id <- match_variables(call = call_full[[match("sample_id", names(call_full))]], arg = sample_id, arg_name = "sample_id", data = data)
if(deparse(substitute(citekey))[1] != "NULL")
citekey <- match_variables(call = call_full[[match("citekey", names(call_full))]], arg = citekey, arg_name = "citekey", data = data)
if(deparse(substitute(moderators))[1] != "NULL")
moderators <- match_variables_df({{moderators}}, data = as_tibble(data, .name_repair = "minimal"), name = deparse(substitute(moderators)))
}
if(!is.null(moderators)){
if(is.null(dim(moderators))){
moderators <- as.data.frame(moderators, stringsAsFactors = FALSE)
colnames(moderators) <- "Moderator"
}
moderator_names <- list(all = colnames(moderators),
cat = colnames(moderators)[cat_moderators],
noncat = colnames(moderators)[!cat_moderators])
moderator_names <- lapply(moderator_names, function(x) if(length(x) == 0){NULL}else{x})
if(any(cat_moderators)){
moderator_levels <- lapply(as_tibble(moderators, .name_repair = "minimal")[,cat_moderators], function(x){
lvls <- levels(x)
if(is.null(lvls)) lvls <- levels(factor(x))
lvls
})
names(moderator_levels) <- colnames(as_tibble(moderators, .name_repair = "minimal")[,cat_moderators])
}else{
moderator_levels <- NULL
}
moderators <- as.data.frame(moderators, stringsAsFactors = FALSE)
}else{
moderator_names <- list(all = NULL,
cat = NULL,
noncat = NULL)
moderator_levels <- NULL
}
additional_args <- list(...)
as_worker <- additional_args$as_worker
if(is.null(as_worker)) as_worker <- FALSE
inputs <- list(wt_type = wt_type, error_type = error_type, correct_bias = correct_bias,
conf_level = conf_level, cred_level = cred_level, conf_method = conf_method, cred_method = cred_method,
var_unbiased = var_unbiased)
es_data <- data.frame(d = d, n1 = n1, n2 = n2, stringsAsFactors = FALSE)
es_data$n_adj <- n_adj
if(is.null(sample_id)) sample_id <- paste0("Sample #", 1:nrow(es_data))
if(!is.null(citekey)) es_data <- cbind(citekey = citekey, es_data) %>% mutate(citekey = as.character(citekey))
es_data <- cbind(sample_id = sample_id, es_data) %>% mutate(sample_id = as.character(sample_id))
out <- ma_wrapper(es_data = es_data, es_type = "d", ma_type = "bb", ma_fun = .ma_d_bb,
moderator_matrix = moderators, moderator_type = moderator_type, cat_moderators = cat_moderators,
ma_arg_list = list(error_type = error_type, correct_bias = correct_bias, conf_level = conf_level, cred_level = cred_level,
conf_method = conf_method, cred_method = cred_method, var_unbiased = var_unbiased, wt_type = wt_type),
presorted_data = additional_args$presorted_data, analysis_id_variables = additional_args$analysis_id_variables,
moderator_levels = moderator_levels, moderator_names = moderator_names)
if(!as_worker){
out <- bind_cols(analysis_id = 1:nrow(out), out)
attributes(out) <- append(attributes(out), list(call_history = list(call),
inputs = inputs,
ma_methods = "bb",
ma_metric = "d_as_d",
default_print = "bb",
warnings = clean_warning(warn_obj1 = warn_obj1, warn_obj2 = record_warnings()),
fyi = record_fyis(neg_var_res = sum(unlist(map(out$meta_tables, function(x) x$barebones$var_res < 0)), na.rm = TRUE))))
out <- namelists.ma_psychmeta(ma_obj = out)
}
class(out) <- c("ma_psychmeta", class(out))
options(psychmeta.show_progress = .psychmeta.show_progress)
options(dplyr.show_progress = .dplyr.show_progress)
return(out)
}
#' Internal function for computing bare-bones meta-analyses of d values
#'
#' @param data Data frame of bare-bones information.
#' @param run_lean If TRUE, the meta-analysis will not generate an escalc object. Meant to speed up bootstrap analyses that do not require supplmental output.
#' @param ma_arg_list List of arguments to be used in the meta-analysis function.
#'
#' @return A list object containing the results of bare-bones meta-analyses of d values.
#'
#' @keywords internal
#' @noRd
.ma_d_bb <- function(data, ma_arg_list, run_lean = FALSE){
sample_id <- data$sample_id
citekey <- data$citekey
d <- data$d
n1 <- data$n1
n2 <- data$n2
n_adj <- data$n_adj
conf_level <- ma_arg_list$conf_level
cred_level <- ma_arg_list$cred_level
correct_bias <- ma_arg_list$correct_bias
wt_type <- ma_arg_list$wt_type
error_type <- ma_arg_list$error_type
conf_method <- ma_arg_list$conf_method
cred_method <- ma_arg_list$cred_method
var_unbiased <- ma_arg_list$var_unbiased
## Determine how to use sample-size information: Use total sample size or subgroup sample sizes?
if(is.null(n2)) n2 <- rep(NA, length(n1))
n_vec <- n1
use_n1_only <- is.na(n2)
n_vec[!use_n1_only] <- n1[!use_n1_only] + n2[!use_n1_only]
if(is.null(n_adj)){
n_adj <- n_vec
}else{
n_adj[is.na(n_adj)] <- n_vec[is.na(n_adj)]
}
n1[n_vec != n_adj] <- n_adj[n_vec != n_adj]
use_n1_only[n_vec != n_adj] <- TRUE
n1_i <- n1
n2_i <- n2
n1_i[use_n1_only] <- n2_i[use_n1_only] <- n_adj[use_n1_only] / 2
.d <- d
if(correct_bias) d <- correct_d_bias(d = d, n = n_vec)
wt_source <- check_wt_type(wt_type = wt_type)
if(wt_source == "psychmeta"){
if(wt_type == "n_effective") wt_vec <- n1_i * n2_i / (n1_i + n2_i)
if(wt_type == "sample_size") wt_vec <- n_adj
if(wt_type == "inv_var_mean") wt_vec <- 1 / var_error_d(d = rep(0, length(d)), n1 = n1_i, n2 = n2_i, correct_bias = FALSE)
if(wt_type == "inv_var_sample") wt_vec <- 1 / var_error_d(d = d, n1 = n1_i, n2 = n2_i, correct_bias = FALSE)
}
if(wt_source == "metafor"){
if(error_type == "mean"){
var_e_vec <- var_error_d(d = 0, n1 = n1_i, n2 = n2_i, correct_bias = FALSE)
var_e_vec <- var_error_d(d = wt_mean(x = d, wt = 1 / var_e_vec), n1 = n1_i, n2 = n2_i, correct_bias = FALSE)
}
if(error_type == "sample") var_e_vec <- var_error_d(d = d, n1 = n1_i, n2 = n2_i, correct_bias = FALSE)
wt_vec <- as.numeric(metafor::weights.rma.uni(metafor::rma(yi = d,
vi = var_e_vec,
control = list(maxiter = 1000, stepadj = .5), method = wt_type)))
}
## Estimate the weighted mean d value
mean_d <- wt_mean(x = d, wt = wt_vec)
## Estimate sampling error
if(error_type == "mean") var_e_vec <- var_error_d(d = rep(mean_d, length(d)), n1 = n1_i, n2 = n2_i, correct_bias = FALSE)
if(error_type == "sample") var_e_vec <- var_error_d(d = d, n1 = n1_i, n2 = n2_i, correct_bias = FALSE)
var_e <- wt_mean(x = var_e_vec, wt = wt_vec)
## Create escalc object
if(run_lean){
escalc_obj <- NULL
}else{
escalc_obj <- data.frame(yi = d, vi = var_e_vec,
d = .d,
n1 = n1, n2 = n2, n = n_vec, n_adj = n_adj,
n1_split = n1_i, n2_split = n2_i, stringsAsFactors = FALSE)
escalc_obj$pi <- data$pi
if(is.null(data$pa)){
escalc_obj$pi <- n1_i / (n1_i + n2_i)
}else{
escalc_obj$pi <- data$pi
}
if(is.null(data$pa)){
escalc_obj$pa <- .5
}else{
escalc_obj$pa <- data$pa
}
escalc_obj$pa <- data$pa
escalc_obj$weight <- wt_vec
escalc_obj$residual <- d - mean_d
if(!is.null(citekey)) escalc_obj <- cbind(citekey = citekey, escalc_obj)
if(!is.null(sample_id)) escalc_obj <- cbind(sample_id = sample_id, escalc_obj)
if(any(colnames(data) == "original_order")) escalc_obj <- cbind(original_order = data$original_order, escalc_obj)
class(escalc_obj) <- c("escalc", "data.frame")
}
## Estimate the weighted variance of d values
var_d <- wt_var(x = d, wt = wt_vec, unbiased = var_unbiased)
## Compute residual variance
var_res <- var_d - var_e
sd_d <- var_d^.5
sd_e <- var_e^.5
sd_res <- var_res^.5
sd_res[is.na(sd_res)] <- 0
## Compute cumulative sample size and cumulative adjusted sample size
N <- sum(n_vec[!is.na(wt_vec) & !is.na(d)])
k <- sum(!is.na(wt_vec) & !is.na(d))
## Compute uncertainty intervals
if(k == 1){
var_d <- sd_d <- NA
var_res <- sd_res <- NA
se_d <- sd_e
ci <- confidence(mean = mean_d, sd = sd_e, k = 1, conf_level = conf_level, conf_method = "norm")
}else{
se_d <- sd_d / sqrt(k)
ci <- confidence(mean = mean_d, sd = var_d^.5, k = k, conf_level = conf_level, conf_method = conf_method)
}
cr <- credibility(mean = mean_d, sd = sd_res, cred_level = cred_level, k = k, cred_method = cred_method)
ci <- setNames(c(ci), colnames(ci))
cr <- setNames(c(cr), colnames(cr))
barebones <- as.data.frame(t(c(k = k,
N = N,
mean_d = mean_d,
var_d = var_d,
var_e = var_e,
var_res = var_res,
sd_d = var_d^.5,
se_d = se_d,
sd_e = var_e^.5,
sd_res = sd_res,
ci, cr)), stringsAsFactors = FALSE)
class(barebones) <- c("ma_table", class(barebones))
attributes(barebones) <- append(attributes(barebones), list(ma_type = "d_bb"))
## Compile results
list(meta = list(barebones = barebones,
individual_correction = NULL,
artifact_distribution = NULL),
escalc = list(barebones = escalc_obj,
individual_correction = NULL,
artifact_distribution = NULL))
}
#' Internal function for computing bootstrapped bare-bones meta-analyses of d values
#'
#' @param data Data frame of bare-bones information.
#' @param i Vector of indexes to select studies from 'data'.
#' @param ma_arg_list List of arguments to be passed to the meta-analysis function.
#'
#' @return A list object containing the results of bootstrapped bare-bones meta-analyses of d values.
#'
#' @keywords internal
#' @noRd
.ma_d_bb_boot <- function(data, i, ma_arg_list){
data <- data[i,]
out <- .ma_d_bb(data = data, ma_arg_list = ma_arg_list, run_lean = TRUE)
unlist(out$meta$barebones)
}
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