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inst/doc/overview.R
In psychmeta: Psychometric Meta-Analysis Toolkit
## ----setup, include=FALSE-------------------------------------------------------------------------------------------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
library(psychmeta)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# install.packages("psychmeta")
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# devtools::install_github("psychmeta/psychmeta")
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# library(psychmeta)
## ---- echo=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# First 10 observations from the the database of Gonzalez-Mulé, Mount, and Oh (2014; JAP)
head(data_r_gonzalezmule_2014[,c("Study", "n", "rxyi", "Rating source",
"rxxi", "ryyi", "ux")])
## ---- include=FALSE-------------------------------------------------------------------------------------------------------------------------------------------
dat_matrix <- data.frame(var_names = c("X", "Y", "Z"),
n = c(100, 100, 100),
mean = c(4, 5, 3),
sd = c(2.4, 2.6, 2),
rel = c(.8, .7, .85),
reshape_vec2mat(cov = c(.3, .4, .5),
var_names = c("X", "Y", "Z")))
rownames(dat_matrix) <- NULL
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
dat_matrix
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
reshape_mat2dat(
var_names = "var_names", # Column of variable names
cor_data = c("X", "Y", "Z"), # Names of correlation columns
common_data = "n", # Names of columns shared among relationships
unique_data = c("mean", "sd", "rel"), # Names of columns unique to relationships
data = dat_matrix)
## ---- include=FALSE-------------------------------------------------------------------------------------------------------------------------------------------
dat_wide <- data.frame(sample_id = c(1, 2, 3),
ni = c(66, 74, 93),
rxyi_X_Y = c(-.29, -.16, -.34),
rxyi_X_Z = c(.18, .18, .02),
rxyi_Y_Z = c(.15, .00, .00),
rel_X = c(.95, .93, .97),
rel_Y = c(.85, .86, .82),
rel_Z = c(.91, .90, .89))
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
dat_wide
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
common_vars <- c("sample_id") # Column names for variables common to all
# relationships
var_names <- c("X", "Y", "Z")
es_design = matrix(NA, 3, 3) # Matrix containing the column names
es_design[lower.tri(es_design)] <- # for the intercorrelations among variables
c("rxyi_X_Y", "rxyi_X_Z", "rxyi_Y_Z") # in the lower triangle of the matrix
rownames(es_design) <-
colnames(es_design) <-
var_names
n_design <- "ni" # Sample size column name or es_design-like
# matrix
other_design <- # Matrix with variable names as row names,
cbind(rel = c("rel_X", # names of long-format variables as column names,
"rel_Y", # and column names of dat_wide as elements
"rel_Z"))
rownames(other_design) <- var_names
reshape_wide2long(common_vars = common_vars,
es_design = es_design,
n_design = n_design,
other_design = other_design,
es_name = "rxyi", # Type of effect size in dat_wide
data = dat_wide)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# convert_es(es = 1, input_es = "d", output_es = "r", n1 = 50, n2 = 50)
# convert_es(es = -1.3, input_es = "t", output_es = "r", n1 = 100, n2 = 140)
# convert_es(es = 10.3, input_es = "F", output_es = "r", n1 = 100, n2 = 150)
# convert_es(es = 1.3, input_es = "chisq", output_es = "r", n1 = 100, n2 = 100)
# convert_es(es = .021, input_es = "p.chisq", output_es = "r", n1 = 100, n2 = 100)
# convert_es(es = 4.37, input_es = "or", output_es = "r", n1 = 100, n2 = 100)
# convert_es(es = 1.47, input_es = "lor", output_es = "r", n1 = 100, n2 = 100)
#
# convert_es(es = .2, input_es = "r", output_es = "d", n1 = 50, n2 = 50)
# convert_es(es = -1.3, input_es = "t", output_es = "d", n1 = 100, n2 = 140)
# convert_es(es = 10.3, input_es = "F", output_es = "d", n1 = 100, n2 = 150)
# convert_es(es = 1.3, input_es = "chisq", output_es = "d", n1 = 100, n2 = 100)
# convert_es(es = .021, input_es = "p.chisq", output_es = "d", n1 = 100, n2 = 100)
# convert_es(es = 4.37, input_es = "or", output_es = "d", n1 = 100, n2 = 100)
# convert_es(es = 1.47, input_es = "lor", output_es = "d", n1 = 100, n2 = 100)
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
convert_es(es = c(.4, .3, .25),
input_es = "r", output_es = "d",
n1 = c(50, 110, 65), n2 = c(50, 70, 65)
)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# correct_r_dich(r = c(.3, .5), px = .5, py = .5, n = 100)
# correct_r_split(r = c(.3, .5), pi = .2, n = 100)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(
# #### Commonly used arguments: ####
# # Which data set would you like to use?
# data = NULL,
#
# # Specify essential effect-size information.
# rxyi, n, n_adj = NULL,
#
# # Differentiate sources of data.
# sample_id = NULL, citekey = NULL,
#
# # Specify methodological parameters.
# ma_method = "bb", ad_type = "tsa",
# correction_method = "auto",
#
# # Specify constructs and measures of constructs.
# construct_x = NULL, construct_y = NULL,
# measure_x = NULL, measure_y = NULL,
#
# # Weighting method
# wt_type = "sample_size",
#
# # Correct for small-sample bias?
# correct_bias = TRUE,
# # Correct for measurement error?
# correct_rxx = TRUE, correct_ryy = TRUE,
# # Correct for range restriction?
# correct_rr_x = TRUE, correct_rr_y = TRUE,
# # Is the range restriction indirect (vs. direct) in nature?
# indirect_rr_x = TRUE, indirect_rr_y = TRUE,
#
# # What are your reliability coefficients?
# rxx = NULL, ryy = NULL,
# # Are your reliability coefficients range restricted?
# rxx_restricted = TRUE, ryy_restricted = TRUE,
# # What types of reliability estimates are you using?
# rxx_type = "alpha", ryy_type = "alpha",
#
# # What are your range-restriction u (SD) ratios?
# ux = NULL, uy = NULL,
# # Are your u ratios computed from observed (vs. true-score) SDs?
# ux_observed = TRUE, uy_observed = TRUE,
#
# # What are your moderators and which ones are categorical?
# moderators = NULL, cat_moderators = TRUE,
#
# # What type of moderator analysis do you want (simple vs. hierarchical)?
# moderator_type = "simple",
#
# # If correcting for bivariate indirect RR, how do constructs correlate
# # with selection mechanisms? (only need to specify the +1 or -1 sign of the relationships)
# sign_rxz = 1, sign_ryz = 1,
#
# # Do you have any artifact distributions to include that are not in your database?
# supplemental_ads = NULL,
#
# #### Other arguments to know about: ####
# # Specify the order in which constructs should be displayed in output.
# construct_order = NULL,
# # If analyzing multiple relationships, how should each constructs's measurement
# # error be handled?
# correct_rel = NULL,
# # If analyzing multiple relationships, how should each construct's
# # range restriction be handled?
# correct_rr = NULL,
# # If analyzing multiple relationships, which constructs are affected by indirect
# # range restriction?
# indirect_rr = NULL,
# # If analyzing multiple relationships, how does each construct correlate with
# # the selection mechanism?
# sign_rz = NULL,
#
# # Additional methological parameters can be modified using the "control" argument.
# control = control_psychmeta()
# )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# control_psychmeta(
# # Should the mean or the sample-specific effect sizes be used to estimate error variance?
# error_type = c("mean", "sample"),
# # What proportion of the distribution of means should be included in confidence intervals?
# conf_level = .95,
# # What proportion of the residual distribution of observations should be included in credibility intervals?
# cred_level = .8,
# # How should confidence and credibility intervals be computed? With the t or normal distribution?
# conf_method = c("t", "norm"),
# cred_method = c("t", "norm"),
# # Should weighted variance estimates be computed as unbiased (i.e., multiplied by k / [k-1])?
# var_unbiased = TRUE,
# # Should overall artifaction distributions be computed for each construct (pairwise_ads == FALSE) or should
# # artifact distributions be computed separately for each construct pair (pairwise_ads == TRUE)?
# pairwise_ads = FALSE,
# # Should artifact distributions be computed by collapsing across moderator
# # levels (moderated_ads == FALSE) or should artifact distributions be computed
# # separately for each moderator combination (moderated_ads == TRUE)?
# moderated_ads = FALSE,
# # Should artifact-distribution corrections be computed using artifact distributions
# # that have had sampling error removed (residual_ads == TRUE) or should the observed
# # distributions be used (residual_ads == TRUE)?
# residual_ads = TRUE,
# # Should dependent observations (i.e., multiple observations of the same relationship in a single sample) be
# # consolidated so that there is just one independent observation per sample?
# check_dependence = TRUE,
# # If check_dependence is TRUE, how should dependency be resolved? Options are to compute a composite
# # effect size (default), compute the average of the effect sizes, or to stop with an error message.
# collapse_method = c("composite", "average", "stop"),
# # The intercor argument uses the control_intercor() function to control how the intercorrelations
# # among variables are handled with collapse_method == "composite"
# intercor = control_intercor(),
# # Should artifact information be cleaned to resolve discrepancies among values recorded for multiple
# # relationsips involving a given construct in a given sample?
# clean_artifacts = TRUE,
# # Should missing artifact information be imputed? (For use with individual-correction method only)
# impute_artifacts = TRUE,
# # If impute_artifacts is TRUE, how should imputation be performed? See the documentation for the
# # control_psychmeta() function for descriptions of the available options.
# impute_method = c("bootstrap_mod", "bootstrap_full", "simulate_mod", "simulate_full",
# "wt_mean_mod", "wt_mean_full", "unwt_mean_mod", "unwt_mean_full",
# "replace_unity", "stop"),
# # What seed value should be set for the imputation process? (This makes the imputation reproducible)
# seed = 42,
# # Should artifact information from observations not included in meta-analyses be harvested from "data"
# # and included in artifact distributions?
# use_all_arts = TRUE,
# # For meta-analyses of d values, should the proportionality of membership in the unrestricted sample
# # be estimated from the range-restricted proportions and the range-restriction correction factor?
# estimate_pa = FALSE,
# # To what number of decimal places should interactive artifact distributions be rounded prior to use?
# # Rounding reduces the computational burden of creating multi-dimensional arrays of artifact information.
# decimals = 2,
# # Should the "Hunter-Schmidt" override settings be used? When TRUE, this will override settings for:
# # - wt_type will set to "sample_size"
# # - error_type will set to "mean"
# # - correct_bias will set to TRUE
# # - conf_method will set to "norm"
# # - cred_method will set to "norm"
# # - var_unbiased will set to FALSE
# # - residual_ads will be set to FALSE
# # - use_all_arts will set to FALSE
# hs_override = FALSE
# )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = data_r_meas_multi$rxyi,
# n = data_r_meas_multi$n)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = "rxyi", n = "n",
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# moderators = moderator,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# moderators = c("Rating source", "Published", "Complexity"),
# data = data_r_gonzalezmule_2014)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# moderators = moderator,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- ma_r(ma_method = "ic",
# rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# rxx = rxxi,
# ryy = ryyi,
# moderators = moderator,
# clean_artifacts = FALSE,
# impute_artifacts = FALSE,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(ma_method = "ad",
# rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# rxx = rxxi,
# ryy = ryyi,
# correct_rr_x = FALSE,
# correct_rr_y = FALSE,
# moderators = moderator,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r_ad(ma_obj, correct_rr_x = FALSE, correct_rr_y = FALSE)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n, sample_id = sample_id,
# collapse_method = "composite",
# data = data_r_meas_multi)
#
# ma_r(rxyi = rxyi, n = n, sample_id = sample_id,
# collapse_method = "average",
# data = data_r_meas_multi)
## ---- eval=TRUE, echo=TRUE------------------------------------------------------------------------------------------------------------------------------------
(gonzalezmule <- ma_r(ma_method = "ic", rxyi = rxyi, n = n,
construct_x = "GMA",
construct_y = "OCB",
rxx = rxxi, ryy = ryyi, ux = ux, indirect_rr_x = TRUE,
moderators = c("Rating source", "Type", "Published"),
moderator_type = "hierarchical",
control = control_psychmeta(hs_override = TRUE),
data = data_r_gonzalezmule_2014))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# summary(gonzalezmule)
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
metatab_list <- get_metatab(gonzalezmule)
metatab_list$individual_correction$true_score
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
gonzalezmule$meta_tables$`analysis_id: 1`$individual_correction$true_score
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- sensitivity(ma_obj,
# leave1out = TRUE,
# bootstrap = TRUE,
# cumulative = TRUE,
#
# sort_method = "weight",
#
# boot_iter = 100,
# boot_conf_level = 0.95,
# boot_ci_type = "norm")
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj_gm <- ma_r(ma_method = "ic",
# rxyi = rxyi, n = n,
# rxx = rxxi,
# ryy = ryyi,
# moderators = c("Rating source", "Complexity"),
# data = data_r_gonzalezmule_2014)
#
# ma_obj_gm <- metareg(ma_obj_gm)
# get_metareg(ma_obj = ma_obj_gm)[[1]]$individual_correction$true_score
#
# ma_obj_mc <- ma_r(ma_method = "ic",
# rxyi = r, n = N,
# moderators = c("Parent", "Age"),
# cat_moderators = c(TRUE, FALSE),
# data = data_r_mcleod_2007)
#
# ma_obj_mc <- metareg(ma_obj_mc, formula_list = list("Parent*Age" = yi ~ Parent*Age))
# get_metareg(ma_obj_mc)[[1]]$individual_correction$true_score
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj_gm <- ma_r(ma_method = "ic",
# rxyi = rxyi, n = n,
# rxx = rxxi,
# ryy = ryyi,
# moderators = c("Rating source", "Complexity"),
# moderator_type = "hierarchical",
# data = data_r_gonzalezmule_2014)
#
# ma_obj_gm
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- heterogeneity(ma_obj)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- ma_r(ma_method = "ic", sample_id = sample_id,
# rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# rxx = rxxi,
# ryy = ryyi,
# moderators = moderator,
# clean_artifacts = FALSE,
# impute_artifacts = FALSE,
# data = data_r_meas_multi)
#
# ma_obj
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# filter_ma(ma_obj, analyses = list(construct = "Y") )
# filter_ma(ma_obj, analyses = list(construct_pair = list(c("X", "Z"))) )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Heterogeneity statistics
# ma_obj <- heterogeneity(ma_obj)
# out_heterogeneity <- get_heterogeneity(ma_obj)
# out_heterogeneity$`analysis id: 1`$barebones
#
# # Sensitivity analyses
# ma_obj <- sensitivity(ma_obj, bootstrap = FALSE)
# out_leave1out <- get_leave1out(ma_obj)
# out_leave1out$`analysis id: 1`$barebones
#
# out_cumulative <- get_cumulative(ma_obj)
# out_cumulative$`analysis id: 1`$barebones
#
# # Meta-regression analyses
# ma_obj <- metareg(ma_obj)
# out_metareg <- get_metareg(ma_obj)
# out_metareg$`analysis id: 1`$barebones
#
# # A summary of artifact distributions
# get_ad(ma_obj, ma_method = "ic")
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Write meta-analysis tables to a Word file called "Meta-analysis table.docx"
# metabulate(ma_obj, file = "Meta-analysis table.docx",
# output_format = "word", output_dir = tempdir())
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Create funnel and forest plots
# ma_obj <- plot_funnel(ma_obj = ma_obj)
# ma_obj <- plot_forest(ma_obj = ma_obj, ma_facetname = "MA")
#
# # Extract plots for viewing
# out_plots <- get_plots(ma_obj)
# out_plots$funnel$`analysis id: 1`$barebones
# out_plots$forest$`analysis id: 1`$moderated$barebones
#
# # The sensitivity_cumulative() and sensitivity_leave1out() functions also produce plots.
# out_plots$leave1out$`analysis id: 1`$barebones$plots
# out_plots$cumulative$`analysis id: 1`$barebones$plots
## ---- eval=TRUE, echo=FALSE, results = "hide", fig.keep="high"------------------------------------------------------------------------------------------------
ma_obj_print <- ma_r(ma_method = "ic", sample_id = sample_id,
rxyi = rxyi, n = n,
construct_x = x_name,
construct_y = y_name,
rxx = rxxi,
ryy = ryyi,
moderators = moderator,
clean_artifacts = FALSE,
impute_artifacts = FALSE,
data = data_r_meas_multi)
ma_obj_print <- plot_funnel(ma_obj = ma_obj_print)
ma_obj_print$funnel$`analysis id: 1`$barebones
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- ma_r(ma_method = "ic",
# rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# rxx = rxxi,
# ryy = ryyi,
# moderators = moderator,
# clean_artifacts = FALSE,
# impute_artifacts = FALSE,
# citekey = citekey,
# data = data_r_meas_multi)
#
# generate_bib(ma_obj,
#
# # The location of your .bib file (normally should be in the same directory
# # as the analysis script).
# bib = system.file("templates/sample_bibliography.bib", package="psychmeta"),
#
# # Your citation style. Must be the style ID for a style hosted at
# # http://zotero.org/styles/
# style = "apa",
#
# # What format to write output as (options are: "word", "html", "pdf", "text",
# # "Rmd", "biblatex", "citekeys")?
# output_format = "word",
#
# # What filename to output to (use "console" to print to the R console).
# file = "sources.docx"
# )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# generate_bib(ma_obj,
#
# bib = system.file("templates/sample_bibliography.bib",
# package="psychmeta"),
#
# analyses = list(construct_pair = list(c("X", "Y")))
#
# )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Create artifact-distribution objects for X, Y, and Z.
# ad_x <- create_ad(mean_qxi = 0.8927818, var_qxi = 0.0008095520, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")
# ad_y <- create_ad(mean_qxi = 0.8927818, var_qxi = 0.0008095520, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")
# ad_z <- create_ad(mean_qxi = 0.8962108, var_qxi = 0.0007840593, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")
#
# # Compute a meta-analysis of X and Y.
# ma_bb_xy <- ma_r_bb(r = rxyi, n = n,
# data = filter(data_r_meas_multi, x_name == "X", y_name == "Y"))
#
# # Correct the meta-analysis for measurement error.
# ma_r_ad(ma_obj = ma_bb_xy, ad_obj_x = ad_x, ad_obj_y = ad_y,
# correct_rr_x = FALSE, correct_rr_y = FALSE)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(ma_method = "ad", rxyi = rxyi, n = n,
# correct_rr_x = FALSE, correct_rr_y = FALSE,
# construct_x = x_name, construct_y = y_name, sample_id = sample_id,
# clean_artifacts = FALSE, impute_artifacts = FALSE,
# moderators = moderator, data = data_r_meas_multi,
# # The "supplemental_ads" argument can take a list of artifact-distribution objects.
# supplemental_ads =
# list(X = ad_x,
# Y = ad_y,
# Z = ad_z))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(ma_method = "ad", rxyi = rxyi, n = n,
# correct_rr_x = FALSE, correct_rr_y = FALSE,
# construct_x = x_name, construct_y = y_name, sample_id = sample_id,
# clean_artifacts = FALSE, impute_artifacts = FALSE,
# moderators = moderator, data = data_r_meas_multi,
# supplemental_ads =
# # The "supplemental_ads" argument can also take raw artifact values.
# # (These values are unrounded so our examples all produce identical results.)
# list(X = list(mean_qxi = 0.8927818, var_qxi = 0.0008095520, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha"),
# Y = list(mean_qxi = 0.8941266, var_qxi = 0.0009367234, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha"),
# Z = list(mean_qxi = 0.8962108, var_qxi = 0.0007840593, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")))
#
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ad_list <- create_ad_list(n = n, rxx = rxxi, ryy = ryyi,
# construct_x = x_name, construct_y = y_name,
# sample_id = sample_id,
# data = data_r_meas_multi)
#
# ma_r(ma_method = "ad", rxyi = rxyi, n = n,
# correct_rr_x = FALSE, correct_rr_y = FALSE,
# construct_x = x_name, construct_y = y_name, sample_id = sample_id,
# clean_artifacts = FALSE, impute_artifacts = FALSE,
# moderators = moderator, data = data_r_meas_multi,
# # The "supplemental_ads" argument can take the output of create_ad_list().
# supplemental_ads = ad_list)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # For purposes of illustration, delete all reliability values not associated with "Z":
# dat_zarts <- data_r_meas_multi
# dat_zarts[,"rxxi"] <- dat_zarts[dat_zarts$y_name != "Z","ryyi"] <- NA
#
# # Compute a meta-analysis using three different types of artifact formats:
# ma_r(ma_method = "ad", rxyi = rxyi, n = n,
# # Observed artifacts can be provided along with the "supplemental_ads" argument.
# ryy = ryyi,
# correct_rr_x = FALSE, correct_rr_y = FALSE,
# construct_x = x_name, construct_y = y_name, sample_id = sample_id,
# clean_artifacts = FALSE, impute_artifacts = FALSE,
# moderators = moderator, data = dat_zarts,
# supplemental_ads =
# list(X = ad_x,
# Y = list(mean_qxi = 0.8941266, var_qxi = 0.0009367234, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# sim_dat <- simulate_psych(n = 1000,
# rho_mat = reshape_vec2mat(.5),
# sigma_vec = c(1, 1),
# sr_vec = c(1, .5),
# rel_vec = c(.8, .8), var_names = c("Y", "X"))
## ---- eval=FALSE, echo=FALSE----------------------------------------------------------------------------------------------------------------------------------
# sim_dat
# cor(sim_dat$observed[,1:2])
# cor(sim_dat$true[,1:2])
# cor(sim_dat$error[,1:2])
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# simulate_r_sample(n = 1000,
# # Correlation parameter matrix
# rho_mat = reshape_vec2mat(.5, order = 5),
# # Reliability parameter vector
# rel_vec = rep(.8, 5),
# # Selection ratio vector
# sr_vec = c(1, 1, 1, 1, .5),
# # Number of items in each scale
# k_items_vec = 1:5,
# # Matrix of weights to use in creating composites
# wt_mat = cbind(c(0, 0, 0, .3, 1),
# c(1, .3, 0, 0, 0)),
# # Selection ratios for composites
# sr_composites = c(.7, .5))
#
# simulate_r_sample(n = Inf,
# rho_mat = reshape_vec2mat(.5, order = 5),
# rel_vec = rep(.8, 5),
# sr_vec = c(1, 1, 1, 1, .5),
# k_items_vec = 1:5,
# wt_mat = cbind(c(0, 0, 0, .3, 1),
# c(1, .3, 0, 0, 0)),
# sr_composites = c(.7, .5))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Note the varying methods for defining parameters:
# simulate_r_database(k = 10,
#
# # Sample-size parameters
# # Parameters can be defined as functions.
# n_params = function(n) rgamma(n, shape = 100),
#
# # Correlation parameters (one parameter distribution per correlation)
# # Parameters can also be defined as vectors...
# rho_params = list(c(.1, .3, .5),
# # ...as a mean + a standard deviation...
# c(mean = .3, sd = .05),
# # ...or as a matrix of values and weights.
# rbind(value = c(.1, .3, .5),
# weight = c(1, 2, 1))),
#
# # Reliability parameters
# rel_params = list(c(.7, .8, .9),
# c(mean = .8, sd = .05),
# rbind(value = c(.7, .8, .9),
# weight = c(1, 2, 1))),
#
# # Selection-ratio parameters
# sr_params = list(1, 1, c(.5, .7)),
#
# # Measure-length parameters
# k_items_params = list(5, 8, 10),
#
# # Composite weight parameters
# wt_params = list(list(1, 1, 0)),
#
# # Selection-ratio parameters for composites
# sr_composite_params = list(1),
#
# # Variable names
# var_names = c("X", "Y", "Z"))
## ----simulate_d_sample stats, eval=FALSE----------------------------------------------------------------------------------------------------------------------
# ## Simulate statistics by providing integers as "n_vec":
# simulate_d_sample(n_vec = c(200, 100),
#
# # List of rho matrices - one per group
# rho_mat_list = list(reshape_vec2mat(.5),
# reshape_vec2mat(.4)),
#
# # Matrix of group means (groups on rows, variables on columns)
# mu_mat = rbind(c(1, .5),
# c(0, 0)),
#
# # Matrix of group SDs (groups on rows, variables on columns)
# sigma_mat = rbind(c(1, 1),
# c(1, 1)),
#
# # Matrix of group reliabilities (groups on rows, variables on columns)
# rel_mat = rbind(c(.8, .7),
# c(.7, .7)),
#
# # Vector of selection ratios
# sr_vec = c(1, .5),
#
# # Number of items in each scale
# k_items_vec = c(5, 10),
#
# # Group names
# group_names = c("A", "B"))
## ----simulate_d_sample params, eval=FALSE---------------------------------------------------------------------------------------------------------------------
# simulate_d_sample(n_vec = c(2/3, 1/3),
# rho_mat_list = list(reshape_vec2mat(.5),
# reshape_vec2mat(.4)),
# mu_mat = rbind(c(1, .5),
# c(0, 0)),
# sigma_mat = rbind(c(1, 1),
# c(1, 1)),
# rel_mat = rbind(c(.8, .7),
# c(.7, .7)),
# sr_vec = c(1, .5),
# k_items_vec = c(5, 10),
# group_names = c("A", "B"))
## ----simulate_d_database, eval=FALSE--------------------------------------------------------------------------------------------------------------------------
# simulate_d_database(k = 5,
# # "Group1" and "Group2" labels are not required for parameter arguments:
# # They are shown only for enhanced interpretability.
#
# # Sample-size parameter distributions
# n_params = list(Group1 = c(mean = 200, sd = 20),
# Group2 = c(mean = 100, sd = 20)),
#
# # Correlation parameter distributions
# rho_params = list(Group1 = list(c(.3, .4, .5)),
# Group2 = list(c(.3, .4, .5))),
#
# # Mean parameter distributions
# mu_params = list(Group1 = list(c(mean = .5, sd = .5),
# c(-.5, 0, .5)),
# Group2 = list(c(mean = 0, sd = .5),
# c(-.2, 0, .2))),
#
# # Reliability parameter distributions
# rel_params = list(Group1 = list(.8, .8),
# Group2 = list(c(.7, .8, .8),
# c(.7, .8, .8))),
#
# # Group names
# group_names = c("Group1", "Group2"),
#
# # Variable names
# var_names = c("Y", "Z"))
## ----correct mvrr, eval=FALSE---------------------------------------------------------------------------------------------------------------------------------
# # Create example matrices with different variances and covariances:
# Sigma_i <- reshape_vec2mat(cov = .2, var = .8, order = 4)
# Sigma_xx_a <- reshape_vec2mat(cov = .5, order = 2)
#
# # Correct "Sigma_i" for range restriction in variables 1 & 2:
# correct_matrix_mvrr(Sigma_i = Sigma_i,
# Sigma_xx_a = Sigma_xx_a,
# x_col = 1:2)
#
# # Correct the means of variables 1 & 2 for range restriction:
# correct_means_mvrr(Sigma = Sigma_i,
# means_i = c(2, 2, 1, 1),
# means_x_a = c(1, 1),
# x_col = 1:2)
## ----spearman-brown, eval=FALSE-------------------------------------------------------------------------------------------------------------------------------
# estimate_rel_sb(rel_initial = .5, k = 4)
#
# estimate_length_sb(rel_initial = .5, rel_desired = .8)
## ----single composite r, eval=FALSE---------------------------------------------------------------------------------------------------------------------------
# # Correlation between a variable and a composite
# composite_r_matrix(r_mat = reshape_vec2mat(.4, order = 5), x_col = 2:5, y_col = 1)
#
# composite_r_scalar(mean_rxy = .4, k_vars_x = 4, mean_intercor_x = .4)
#
#
# # Correlation between two composites
# composite_r_matrix(r_mat = reshape_vec2mat(.3, order = 5), x_col = 1:3, y_col = 4:5)
#
# composite_r_scalar(mean_rxy = .3,
# k_vars_x = 3, mean_intercor_x = .3, k_vars_y = 2, mean_intercor_y = .3)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# composite_d_matrix(d_vec = c(1, 1),
# r_mat = reshape_vec2mat(.7),
# wt_vec = c(1, 1),
# p = .5)
#
# composite_d_scalar(mean_d = 1, mean_intercor = .7, k_vars = 2, p = .5)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# composite_rel_matrix(rel_vec = c(.8, .8), r_mat = reshape_vec2mat(.4), sd_vec = c(1, 1))
#
# composite_rel_scalar(mean_rel = .8, mean_intercor = .4, k_vars = 2)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # For purposes of demonstration, generate a dataset from the following matrix:
# S <- reshape_vec2mat(cov = c(.3 * 2 * 3,
# .4 * 2 * 4,
# .5 * 3 * 4),
# var = c(2, 3, 4)^2,
# var_names = c("X", "Y", "Z"))
# mean_vec <- setNames(c(1, 2, 3), colnames(S))
# dat <- data.frame(MASS::mvrnorm(n = 100, mu = mean_vec, Sigma = S, empirical = TRUE))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Compute regression models using one predictor:
# lm_out1 <- lm(formula = Y ~ X, data = dat)
# lm_mat_out1 <- lm_mat(formula = Y ~ X, cov_mat = S, mean_vec = mean_vec, n = nrow(dat))
#
# # Compute regression models using two predictors:
# lm_out2 <- lm(formula = Y ~ X + Z, data = dat)
# lm_mat_out2 <- lm_mat(formula = Y ~ X + Z, cov_mat = S, mean_vec = mean_vec, n = nrow(dat))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# summary(lm_out1)
# summary(lm_mat_out1)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# anova(lm_out1, lm_out2)
# anova(lm_mat_out1, lm_mat_out2)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# mat_array <- get_matrix(ma_obj = ma_obj)
# R <- mat_array$individual_correction$`moderator_comb: 1`$true_score$mean_rho
# n <- mat_array$individual_correction$`moderator_comb: 1`$true_score$N[1,3]
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# lm_x <- lm_mat(formula = Y ~ X, cov_mat = R, n = n)
# lm_xz <- lm_mat(formula = Y ~ X + Z, cov_mat = R, n = n)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# summary(lm_x)
# summary(lm_xz)
# anova(lm_x, lm_xz)
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## ----setup, include=FALSE-------------------------------------------------------------------------------------------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
library(psychmeta)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# install.packages("psychmeta")
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# devtools::install_github("psychmeta/psychmeta")
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# library(psychmeta)
## ---- echo=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# First 10 observations from the the database of Gonzalez-Mulé, Mount, and Oh (2014; JAP)
head(data_r_gonzalezmule_2014[,c("Study", "n", "rxyi", "Rating source",
"rxxi", "ryyi", "ux")])
## ---- include=FALSE-------------------------------------------------------------------------------------------------------------------------------------------
dat_matrix <- data.frame(var_names = c("X", "Y", "Z"),
n = c(100, 100, 100),
mean = c(4, 5, 3),
sd = c(2.4, 2.6, 2),
rel = c(.8, .7, .85),
reshape_vec2mat(cov = c(.3, .4, .5),
var_names = c("X", "Y", "Z")))
rownames(dat_matrix) <- NULL
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
dat_matrix
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
reshape_mat2dat(
var_names = "var_names", # Column of variable names
cor_data = c("X", "Y", "Z"), # Names of correlation columns
common_data = "n", # Names of columns shared among relationships
unique_data = c("mean", "sd", "rel"), # Names of columns unique to relationships
data = dat_matrix)
## ---- include=FALSE-------------------------------------------------------------------------------------------------------------------------------------------
dat_wide <- data.frame(sample_id = c(1, 2, 3),
ni = c(66, 74, 93),
rxyi_X_Y = c(-.29, -.16, -.34),
rxyi_X_Z = c(.18, .18, .02),
rxyi_Y_Z = c(.15, .00, .00),
rel_X = c(.95, .93, .97),
rel_Y = c(.85, .86, .82),
rel_Z = c(.91, .90, .89))
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
dat_wide
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
common_vars <- c("sample_id") # Column names for variables common to all
# relationships
var_names <- c("X", "Y", "Z")
es_design = matrix(NA, 3, 3) # Matrix containing the column names
es_design[lower.tri(es_design)] <- # for the intercorrelations among variables
c("rxyi_X_Y", "rxyi_X_Z", "rxyi_Y_Z") # in the lower triangle of the matrix
rownames(es_design) <-
colnames(es_design) <-
var_names
n_design <- "ni" # Sample size column name or es_design-like
# matrix
other_design <- # Matrix with variable names as row names,
cbind(rel = c("rel_X", # names of long-format variables as column names,
"rel_Y", # and column names of dat_wide as elements
"rel_Z"))
rownames(other_design) <- var_names
reshape_wide2long(common_vars = common_vars,
es_design = es_design,
n_design = n_design,
other_design = other_design,
es_name = "rxyi", # Type of effect size in dat_wide
data = dat_wide)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# convert_es(es = 1, input_es = "d", output_es = "r", n1 = 50, n2 = 50)
# convert_es(es = -1.3, input_es = "t", output_es = "r", n1 = 100, n2 = 140)
# convert_es(es = 10.3, input_es = "F", output_es = "r", n1 = 100, n2 = 150)
# convert_es(es = 1.3, input_es = "chisq", output_es = "r", n1 = 100, n2 = 100)
# convert_es(es = .021, input_es = "p.chisq", output_es = "r", n1 = 100, n2 = 100)
# convert_es(es = 4.37, input_es = "or", output_es = "r", n1 = 100, n2 = 100)
# convert_es(es = 1.47, input_es = "lor", output_es = "r", n1 = 100, n2 = 100)
#
# convert_es(es = .2, input_es = "r", output_es = "d", n1 = 50, n2 = 50)
# convert_es(es = -1.3, input_es = "t", output_es = "d", n1 = 100, n2 = 140)
# convert_es(es = 10.3, input_es = "F", output_es = "d", n1 = 100, n2 = 150)
# convert_es(es = 1.3, input_es = "chisq", output_es = "d", n1 = 100, n2 = 100)
# convert_es(es = .021, input_es = "p.chisq", output_es = "d", n1 = 100, n2 = 100)
# convert_es(es = 4.37, input_es = "or", output_es = "d", n1 = 100, n2 = 100)
# convert_es(es = 1.47, input_es = "lor", output_es = "d", n1 = 100, n2 = 100)
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
convert_es(es = c(.4, .3, .25),
input_es = "r", output_es = "d",
n1 = c(50, 110, 65), n2 = c(50, 70, 65)
)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# correct_r_dich(r = c(.3, .5), px = .5, py = .5, n = 100)
# correct_r_split(r = c(.3, .5), pi = .2, n = 100)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(
# #### Commonly used arguments: ####
# # Which data set would you like to use?
# data = NULL,
#
# # Specify essential effect-size information.
# rxyi, n, n_adj = NULL,
#
# # Differentiate sources of data.
# sample_id = NULL, citekey = NULL,
#
# # Specify methodological parameters.
# ma_method = "bb", ad_type = "tsa",
# correction_method = "auto",
#
# # Specify constructs and measures of constructs.
# construct_x = NULL, construct_y = NULL,
# measure_x = NULL, measure_y = NULL,
#
# # Weighting method
# wt_type = "sample_size",
#
# # Correct for small-sample bias?
# correct_bias = TRUE,
# # Correct for measurement error?
# correct_rxx = TRUE, correct_ryy = TRUE,
# # Correct for range restriction?
# correct_rr_x = TRUE, correct_rr_y = TRUE,
# # Is the range restriction indirect (vs. direct) in nature?
# indirect_rr_x = TRUE, indirect_rr_y = TRUE,
#
# # What are your reliability coefficients?
# rxx = NULL, ryy = NULL,
# # Are your reliability coefficients range restricted?
# rxx_restricted = TRUE, ryy_restricted = TRUE,
# # What types of reliability estimates are you using?
# rxx_type = "alpha", ryy_type = "alpha",
#
# # What are your range-restriction u (SD) ratios?
# ux = NULL, uy = NULL,
# # Are your u ratios computed from observed (vs. true-score) SDs?
# ux_observed = TRUE, uy_observed = TRUE,
#
# # What are your moderators and which ones are categorical?
# moderators = NULL, cat_moderators = TRUE,
#
# # What type of moderator analysis do you want (simple vs. hierarchical)?
# moderator_type = "simple",
#
# # If correcting for bivariate indirect RR, how do constructs correlate
# # with selection mechanisms? (only need to specify the +1 or -1 sign of the relationships)
# sign_rxz = 1, sign_ryz = 1,
#
# # Do you have any artifact distributions to include that are not in your database?
# supplemental_ads = NULL,
#
# #### Other arguments to know about: ####
# # Specify the order in which constructs should be displayed in output.
# construct_order = NULL,
# # If analyzing multiple relationships, how should each constructs's measurement
# # error be handled?
# correct_rel = NULL,
# # If analyzing multiple relationships, how should each construct's
# # range restriction be handled?
# correct_rr = NULL,
# # If analyzing multiple relationships, which constructs are affected by indirect
# # range restriction?
# indirect_rr = NULL,
# # If analyzing multiple relationships, how does each construct correlate with
# # the selection mechanism?
# sign_rz = NULL,
#
# # Additional methological parameters can be modified using the "control" argument.
# control = control_psychmeta()
# )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# control_psychmeta(
# # Should the mean or the sample-specific effect sizes be used to estimate error variance?
# error_type = c("mean", "sample"),
# # What proportion of the distribution of means should be included in confidence intervals?
# conf_level = .95,
# # What proportion of the residual distribution of observations should be included in credibility intervals?
# cred_level = .8,
# # How should confidence and credibility intervals be computed? With the t or normal distribution?
# conf_method = c("t", "norm"),
# cred_method = c("t", "norm"),
# # Should weighted variance estimates be computed as unbiased (i.e., multiplied by k / [k-1])?
# var_unbiased = TRUE,
# # Should overall artifaction distributions be computed for each construct (pairwise_ads == FALSE) or should
# # artifact distributions be computed separately for each construct pair (pairwise_ads == TRUE)?
# pairwise_ads = FALSE,
# # Should artifact distributions be computed by collapsing across moderator
# # levels (moderated_ads == FALSE) or should artifact distributions be computed
# # separately for each moderator combination (moderated_ads == TRUE)?
# moderated_ads = FALSE,
# # Should artifact-distribution corrections be computed using artifact distributions
# # that have had sampling error removed (residual_ads == TRUE) or should the observed
# # distributions be used (residual_ads == TRUE)?
# residual_ads = TRUE,
# # Should dependent observations (i.e., multiple observations of the same relationship in a single sample) be
# # consolidated so that there is just one independent observation per sample?
# check_dependence = TRUE,
# # If check_dependence is TRUE, how should dependency be resolved? Options are to compute a composite
# # effect size (default), compute the average of the effect sizes, or to stop with an error message.
# collapse_method = c("composite", "average", "stop"),
# # The intercor argument uses the control_intercor() function to control how the intercorrelations
# # among variables are handled with collapse_method == "composite"
# intercor = control_intercor(),
# # Should artifact information be cleaned to resolve discrepancies among values recorded for multiple
# # relationsips involving a given construct in a given sample?
# clean_artifacts = TRUE,
# # Should missing artifact information be imputed? (For use with individual-correction method only)
# impute_artifacts = TRUE,
# # If impute_artifacts is TRUE, how should imputation be performed? See the documentation for the
# # control_psychmeta() function for descriptions of the available options.
# impute_method = c("bootstrap_mod", "bootstrap_full", "simulate_mod", "simulate_full",
# "wt_mean_mod", "wt_mean_full", "unwt_mean_mod", "unwt_mean_full",
# "replace_unity", "stop"),
# # What seed value should be set for the imputation process? (This makes the imputation reproducible)
# seed = 42,
# # Should artifact information from observations not included in meta-analyses be harvested from "data"
# # and included in artifact distributions?
# use_all_arts = TRUE,
# # For meta-analyses of d values, should the proportionality of membership in the unrestricted sample
# # be estimated from the range-restricted proportions and the range-restriction correction factor?
# estimate_pa = FALSE,
# # To what number of decimal places should interactive artifact distributions be rounded prior to use?
# # Rounding reduces the computational burden of creating multi-dimensional arrays of artifact information.
# decimals = 2,
# # Should the "Hunter-Schmidt" override settings be used? When TRUE, this will override settings for:
# # - wt_type will set to "sample_size"
# # - error_type will set to "mean"
# # - correct_bias will set to TRUE
# # - conf_method will set to "norm"
# # - cred_method will set to "norm"
# # - var_unbiased will set to FALSE
# # - residual_ads will be set to FALSE
# # - use_all_arts will set to FALSE
# hs_override = FALSE
# )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = data_r_meas_multi$rxyi,
# n = data_r_meas_multi$n)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = "rxyi", n = "n",
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# moderators = moderator,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# moderators = c("Rating source", "Published", "Complexity"),
# data = data_r_gonzalezmule_2014)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# moderators = moderator,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- ma_r(ma_method = "ic",
# rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# rxx = rxxi,
# ryy = ryyi,
# moderators = moderator,
# clean_artifacts = FALSE,
# impute_artifacts = FALSE,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(ma_method = "ad",
# rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# rxx = rxxi,
# ryy = ryyi,
# correct_rr_x = FALSE,
# correct_rr_y = FALSE,
# moderators = moderator,
# data = data_r_meas_multi)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r_ad(ma_obj, correct_rr_x = FALSE, correct_rr_y = FALSE)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(rxyi = rxyi, n = n, sample_id = sample_id,
# collapse_method = "composite",
# data = data_r_meas_multi)
#
# ma_r(rxyi = rxyi, n = n, sample_id = sample_id,
# collapse_method = "average",
# data = data_r_meas_multi)
## ---- eval=TRUE, echo=TRUE------------------------------------------------------------------------------------------------------------------------------------
(gonzalezmule <- ma_r(ma_method = "ic", rxyi = rxyi, n = n,
construct_x = "GMA",
construct_y = "OCB",
rxx = rxxi, ryy = ryyi, ux = ux, indirect_rr_x = TRUE,
moderators = c("Rating source", "Type", "Published"),
moderator_type = "hierarchical",
control = control_psychmeta(hs_override = TRUE),
data = data_r_gonzalezmule_2014))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# summary(gonzalezmule)
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
metatab_list <- get_metatab(gonzalezmule)
metatab_list$individual_correction$true_score
## ---- eval=TRUE-----------------------------------------------------------------------------------------------------------------------------------------------
gonzalezmule$meta_tables$`analysis_id: 1`$individual_correction$true_score
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- sensitivity(ma_obj,
# leave1out = TRUE,
# bootstrap = TRUE,
# cumulative = TRUE,
#
# sort_method = "weight",
#
# boot_iter = 100,
# boot_conf_level = 0.95,
# boot_ci_type = "norm")
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj_gm <- ma_r(ma_method = "ic",
# rxyi = rxyi, n = n,
# rxx = rxxi,
# ryy = ryyi,
# moderators = c("Rating source", "Complexity"),
# data = data_r_gonzalezmule_2014)
#
# ma_obj_gm <- metareg(ma_obj_gm)
# get_metareg(ma_obj = ma_obj_gm)[[1]]$individual_correction$true_score
#
# ma_obj_mc <- ma_r(ma_method = "ic",
# rxyi = r, n = N,
# moderators = c("Parent", "Age"),
# cat_moderators = c(TRUE, FALSE),
# data = data_r_mcleod_2007)
#
# ma_obj_mc <- metareg(ma_obj_mc, formula_list = list("Parent*Age" = yi ~ Parent*Age))
# get_metareg(ma_obj_mc)[[1]]$individual_correction$true_score
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj_gm <- ma_r(ma_method = "ic",
# rxyi = rxyi, n = n,
# rxx = rxxi,
# ryy = ryyi,
# moderators = c("Rating source", "Complexity"),
# moderator_type = "hierarchical",
# data = data_r_gonzalezmule_2014)
#
# ma_obj_gm
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- heterogeneity(ma_obj)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- ma_r(ma_method = "ic", sample_id = sample_id,
# rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# rxx = rxxi,
# ryy = ryyi,
# moderators = moderator,
# clean_artifacts = FALSE,
# impute_artifacts = FALSE,
# data = data_r_meas_multi)
#
# ma_obj
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# filter_ma(ma_obj, analyses = list(construct = "Y") )
# filter_ma(ma_obj, analyses = list(construct_pair = list(c("X", "Z"))) )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Heterogeneity statistics
# ma_obj <- heterogeneity(ma_obj)
# out_heterogeneity <- get_heterogeneity(ma_obj)
# out_heterogeneity$`analysis id: 1`$barebones
#
# # Sensitivity analyses
# ma_obj <- sensitivity(ma_obj, bootstrap = FALSE)
# out_leave1out <- get_leave1out(ma_obj)
# out_leave1out$`analysis id: 1`$barebones
#
# out_cumulative <- get_cumulative(ma_obj)
# out_cumulative$`analysis id: 1`$barebones
#
# # Meta-regression analyses
# ma_obj <- metareg(ma_obj)
# out_metareg <- get_metareg(ma_obj)
# out_metareg$`analysis id: 1`$barebones
#
# # A summary of artifact distributions
# get_ad(ma_obj, ma_method = "ic")
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Write meta-analysis tables to a Word file called "Meta-analysis table.docx"
# metabulate(ma_obj, file = "Meta-analysis table.docx",
# output_format = "word", output_dir = tempdir())
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Create funnel and forest plots
# ma_obj <- plot_funnel(ma_obj = ma_obj)
# ma_obj <- plot_forest(ma_obj = ma_obj, ma_facetname = "MA")
#
# # Extract plots for viewing
# out_plots <- get_plots(ma_obj)
# out_plots$funnel$`analysis id: 1`$barebones
# out_plots$forest$`analysis id: 1`$moderated$barebones
#
# # The sensitivity_cumulative() and sensitivity_leave1out() functions also produce plots.
# out_plots$leave1out$`analysis id: 1`$barebones$plots
# out_plots$cumulative$`analysis id: 1`$barebones$plots
## ---- eval=TRUE, echo=FALSE, results = "hide", fig.keep="high"------------------------------------------------------------------------------------------------
ma_obj_print <- ma_r(ma_method = "ic", sample_id = sample_id,
rxyi = rxyi, n = n,
construct_x = x_name,
construct_y = y_name,
rxx = rxxi,
ryy = ryyi,
moderators = moderator,
clean_artifacts = FALSE,
impute_artifacts = FALSE,
data = data_r_meas_multi)
ma_obj_print <- plot_funnel(ma_obj = ma_obj_print)
ma_obj_print$funnel$`analysis id: 1`$barebones
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_obj <- ma_r(ma_method = "ic",
# rxyi = rxyi, n = n,
# construct_x = x_name,
# construct_y = y_name,
# rxx = rxxi,
# ryy = ryyi,
# moderators = moderator,
# clean_artifacts = FALSE,
# impute_artifacts = FALSE,
# citekey = citekey,
# data = data_r_meas_multi)
#
# generate_bib(ma_obj,
#
# # The location of your .bib file (normally should be in the same directory
# # as the analysis script).
# bib = system.file("templates/sample_bibliography.bib", package="psychmeta"),
#
# # Your citation style. Must be the style ID for a style hosted at
# # http://zotero.org/styles/
# style = "apa",
#
# # What format to write output as (options are: "word", "html", "pdf", "text",
# # "Rmd", "biblatex", "citekeys")?
# output_format = "word",
#
# # What filename to output to (use "console" to print to the R console).
# file = "sources.docx"
# )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# generate_bib(ma_obj,
#
# bib = system.file("templates/sample_bibliography.bib",
# package="psychmeta"),
#
# analyses = list(construct_pair = list(c("X", "Y")))
#
# )
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Create artifact-distribution objects for X, Y, and Z.
# ad_x <- create_ad(mean_qxi = 0.8927818, var_qxi = 0.0008095520, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")
# ad_y <- create_ad(mean_qxi = 0.8927818, var_qxi = 0.0008095520, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")
# ad_z <- create_ad(mean_qxi = 0.8962108, var_qxi = 0.0007840593, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")
#
# # Compute a meta-analysis of X and Y.
# ma_bb_xy <- ma_r_bb(r = rxyi, n = n,
# data = filter(data_r_meas_multi, x_name == "X", y_name == "Y"))
#
# # Correct the meta-analysis for measurement error.
# ma_r_ad(ma_obj = ma_bb_xy, ad_obj_x = ad_x, ad_obj_y = ad_y,
# correct_rr_x = FALSE, correct_rr_y = FALSE)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(ma_method = "ad", rxyi = rxyi, n = n,
# correct_rr_x = FALSE, correct_rr_y = FALSE,
# construct_x = x_name, construct_y = y_name, sample_id = sample_id,
# clean_artifacts = FALSE, impute_artifacts = FALSE,
# moderators = moderator, data = data_r_meas_multi,
# # The "supplemental_ads" argument can take a list of artifact-distribution objects.
# supplemental_ads =
# list(X = ad_x,
# Y = ad_y,
# Z = ad_z))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ma_r(ma_method = "ad", rxyi = rxyi, n = n,
# correct_rr_x = FALSE, correct_rr_y = FALSE,
# construct_x = x_name, construct_y = y_name, sample_id = sample_id,
# clean_artifacts = FALSE, impute_artifacts = FALSE,
# moderators = moderator, data = data_r_meas_multi,
# supplemental_ads =
# # The "supplemental_ads" argument can also take raw artifact values.
# # (These values are unrounded so our examples all produce identical results.)
# list(X = list(mean_qxi = 0.8927818, var_qxi = 0.0008095520, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha"),
# Y = list(mean_qxi = 0.8941266, var_qxi = 0.0009367234, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha"),
# Z = list(mean_qxi = 0.8962108, var_qxi = 0.0007840593, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")))
#
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# ad_list <- create_ad_list(n = n, rxx = rxxi, ryy = ryyi,
# construct_x = x_name, construct_y = y_name,
# sample_id = sample_id,
# data = data_r_meas_multi)
#
# ma_r(ma_method = "ad", rxyi = rxyi, n = n,
# correct_rr_x = FALSE, correct_rr_y = FALSE,
# construct_x = x_name, construct_y = y_name, sample_id = sample_id,
# clean_artifacts = FALSE, impute_artifacts = FALSE,
# moderators = moderator, data = data_r_meas_multi,
# # The "supplemental_ads" argument can take the output of create_ad_list().
# supplemental_ads = ad_list)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # For purposes of illustration, delete all reliability values not associated with "Z":
# dat_zarts <- data_r_meas_multi
# dat_zarts[,"rxxi"] <- dat_zarts[dat_zarts$y_name != "Z","ryyi"] <- NA
#
# # Compute a meta-analysis using three different types of artifact formats:
# ma_r(ma_method = "ad", rxyi = rxyi, n = n,
# # Observed artifacts can be provided along with the "supplemental_ads" argument.
# ryy = ryyi,
# correct_rr_x = FALSE, correct_rr_y = FALSE,
# construct_x = x_name, construct_y = y_name, sample_id = sample_id,
# clean_artifacts = FALSE, impute_artifacts = FALSE,
# moderators = moderator, data = dat_zarts,
# supplemental_ads =
# list(X = ad_x,
# Y = list(mean_qxi = 0.8941266, var_qxi = 0.0009367234, k_qxi = 40,
# mean_n_qxi = 11927 / 40, qxi_dist_type = "alpha")))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# sim_dat <- simulate_psych(n = 1000,
# rho_mat = reshape_vec2mat(.5),
# sigma_vec = c(1, 1),
# sr_vec = c(1, .5),
# rel_vec = c(.8, .8), var_names = c("Y", "X"))
## ---- eval=FALSE, echo=FALSE----------------------------------------------------------------------------------------------------------------------------------
# sim_dat
# cor(sim_dat$observed[,1:2])
# cor(sim_dat$true[,1:2])
# cor(sim_dat$error[,1:2])
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# simulate_r_sample(n = 1000,
# # Correlation parameter matrix
# rho_mat = reshape_vec2mat(.5, order = 5),
# # Reliability parameter vector
# rel_vec = rep(.8, 5),
# # Selection ratio vector
# sr_vec = c(1, 1, 1, 1, .5),
# # Number of items in each scale
# k_items_vec = 1:5,
# # Matrix of weights to use in creating composites
# wt_mat = cbind(c(0, 0, 0, .3, 1),
# c(1, .3, 0, 0, 0)),
# # Selection ratios for composites
# sr_composites = c(.7, .5))
#
# simulate_r_sample(n = Inf,
# rho_mat = reshape_vec2mat(.5, order = 5),
# rel_vec = rep(.8, 5),
# sr_vec = c(1, 1, 1, 1, .5),
# k_items_vec = 1:5,
# wt_mat = cbind(c(0, 0, 0, .3, 1),
# c(1, .3, 0, 0, 0)),
# sr_composites = c(.7, .5))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Note the varying methods for defining parameters:
# simulate_r_database(k = 10,
#
# # Sample-size parameters
# # Parameters can be defined as functions.
# n_params = function(n) rgamma(n, shape = 100),
#
# # Correlation parameters (one parameter distribution per correlation)
# # Parameters can also be defined as vectors...
# rho_params = list(c(.1, .3, .5),
# # ...as a mean + a standard deviation...
# c(mean = .3, sd = .05),
# # ...or as a matrix of values and weights.
# rbind(value = c(.1, .3, .5),
# weight = c(1, 2, 1))),
#
# # Reliability parameters
# rel_params = list(c(.7, .8, .9),
# c(mean = .8, sd = .05),
# rbind(value = c(.7, .8, .9),
# weight = c(1, 2, 1))),
#
# # Selection-ratio parameters
# sr_params = list(1, 1, c(.5, .7)),
#
# # Measure-length parameters
# k_items_params = list(5, 8, 10),
#
# # Composite weight parameters
# wt_params = list(list(1, 1, 0)),
#
# # Selection-ratio parameters for composites
# sr_composite_params = list(1),
#
# # Variable names
# var_names = c("X", "Y", "Z"))
## ----simulate_d_sample stats, eval=FALSE----------------------------------------------------------------------------------------------------------------------
# ## Simulate statistics by providing integers as "n_vec":
# simulate_d_sample(n_vec = c(200, 100),
#
# # List of rho matrices - one per group
# rho_mat_list = list(reshape_vec2mat(.5),
# reshape_vec2mat(.4)),
#
# # Matrix of group means (groups on rows, variables on columns)
# mu_mat = rbind(c(1, .5),
# c(0, 0)),
#
# # Matrix of group SDs (groups on rows, variables on columns)
# sigma_mat = rbind(c(1, 1),
# c(1, 1)),
#
# # Matrix of group reliabilities (groups on rows, variables on columns)
# rel_mat = rbind(c(.8, .7),
# c(.7, .7)),
#
# # Vector of selection ratios
# sr_vec = c(1, .5),
#
# # Number of items in each scale
# k_items_vec = c(5, 10),
#
# # Group names
# group_names = c("A", "B"))
## ----simulate_d_sample params, eval=FALSE---------------------------------------------------------------------------------------------------------------------
# simulate_d_sample(n_vec = c(2/3, 1/3),
# rho_mat_list = list(reshape_vec2mat(.5),
# reshape_vec2mat(.4)),
# mu_mat = rbind(c(1, .5),
# c(0, 0)),
# sigma_mat = rbind(c(1, 1),
# c(1, 1)),
# rel_mat = rbind(c(.8, .7),
# c(.7, .7)),
# sr_vec = c(1, .5),
# k_items_vec = c(5, 10),
# group_names = c("A", "B"))
## ----simulate_d_database, eval=FALSE--------------------------------------------------------------------------------------------------------------------------
# simulate_d_database(k = 5,
# # "Group1" and "Group2" labels are not required for parameter arguments:
# # They are shown only for enhanced interpretability.
#
# # Sample-size parameter distributions
# n_params = list(Group1 = c(mean = 200, sd = 20),
# Group2 = c(mean = 100, sd = 20)),
#
# # Correlation parameter distributions
# rho_params = list(Group1 = list(c(.3, .4, .5)),
# Group2 = list(c(.3, .4, .5))),
#
# # Mean parameter distributions
# mu_params = list(Group1 = list(c(mean = .5, sd = .5),
# c(-.5, 0, .5)),
# Group2 = list(c(mean = 0, sd = .5),
# c(-.2, 0, .2))),
#
# # Reliability parameter distributions
# rel_params = list(Group1 = list(.8, .8),
# Group2 = list(c(.7, .8, .8),
# c(.7, .8, .8))),
#
# # Group names
# group_names = c("Group1", "Group2"),
#
# # Variable names
# var_names = c("Y", "Z"))
## ----correct mvrr, eval=FALSE---------------------------------------------------------------------------------------------------------------------------------
# # Create example matrices with different variances and covariances:
# Sigma_i <- reshape_vec2mat(cov = .2, var = .8, order = 4)
# Sigma_xx_a <- reshape_vec2mat(cov = .5, order = 2)
#
# # Correct "Sigma_i" for range restriction in variables 1 & 2:
# correct_matrix_mvrr(Sigma_i = Sigma_i,
# Sigma_xx_a = Sigma_xx_a,
# x_col = 1:2)
#
# # Correct the means of variables 1 & 2 for range restriction:
# correct_means_mvrr(Sigma = Sigma_i,
# means_i = c(2, 2, 1, 1),
# means_x_a = c(1, 1),
# x_col = 1:2)
## ----spearman-brown, eval=FALSE-------------------------------------------------------------------------------------------------------------------------------
# estimate_rel_sb(rel_initial = .5, k = 4)
#
# estimate_length_sb(rel_initial = .5, rel_desired = .8)
## ----single composite r, eval=FALSE---------------------------------------------------------------------------------------------------------------------------
# # Correlation between a variable and a composite
# composite_r_matrix(r_mat = reshape_vec2mat(.4, order = 5), x_col = 2:5, y_col = 1)
#
# composite_r_scalar(mean_rxy = .4, k_vars_x = 4, mean_intercor_x = .4)
#
#
# # Correlation between two composites
# composite_r_matrix(r_mat = reshape_vec2mat(.3, order = 5), x_col = 1:3, y_col = 4:5)
#
# composite_r_scalar(mean_rxy = .3,
# k_vars_x = 3, mean_intercor_x = .3, k_vars_y = 2, mean_intercor_y = .3)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# composite_d_matrix(d_vec = c(1, 1),
# r_mat = reshape_vec2mat(.7),
# wt_vec = c(1, 1),
# p = .5)
#
# composite_d_scalar(mean_d = 1, mean_intercor = .7, k_vars = 2, p = .5)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# composite_rel_matrix(rel_vec = c(.8, .8), r_mat = reshape_vec2mat(.4), sd_vec = c(1, 1))
#
# composite_rel_scalar(mean_rel = .8, mean_intercor = .4, k_vars = 2)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # For purposes of demonstration, generate a dataset from the following matrix:
# S <- reshape_vec2mat(cov = c(.3 * 2 * 3,
# .4 * 2 * 4,
# .5 * 3 * 4),
# var = c(2, 3, 4)^2,
# var_names = c("X", "Y", "Z"))
# mean_vec <- setNames(c(1, 2, 3), colnames(S))
# dat <- data.frame(MASS::mvrnorm(n = 100, mu = mean_vec, Sigma = S, empirical = TRUE))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# # Compute regression models using one predictor:
# lm_out1 <- lm(formula = Y ~ X, data = dat)
# lm_mat_out1 <- lm_mat(formula = Y ~ X, cov_mat = S, mean_vec = mean_vec, n = nrow(dat))
#
# # Compute regression models using two predictors:
# lm_out2 <- lm(formula = Y ~ X + Z, data = dat)
# lm_mat_out2 <- lm_mat(formula = Y ~ X + Z, cov_mat = S, mean_vec = mean_vec, n = nrow(dat))
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# summary(lm_out1)
# summary(lm_mat_out1)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# anova(lm_out1, lm_out2)
# anova(lm_mat_out1, lm_mat_out2)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# mat_array <- get_matrix(ma_obj = ma_obj)
# R <- mat_array$individual_correction$`moderator_comb: 1`$true_score$mean_rho
# n <- mat_array$individual_correction$`moderator_comb: 1`$true_score$N[1,3]
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# lm_x <- lm_mat(formula = Y ~ X, cov_mat = R, n = n)
# lm_xz <- lm_mat(formula = Y ~ X + Z, cov_mat = R, n = n)
## ---- eval=FALSE----------------------------------------------------------------------------------------------------------------------------------------------
# summary(lm_x)
# summary(lm_xz)
# anova(lm_x, lm_xz)
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