Wolfgang Viechtbauer wviechtb

## rcode_rma_mv_example.r
############################################################################

library(metafor)
library(ape)

############################################################################

# read the documentation for this dataset
help(dat.moura2021)

## install_packages.r
############################################################################

# During the course, we will make use of a number of add-on packages for R.
# Ideally, you should install these packages before the course (then you don't
# have to bother with installing packages during the course). The code below
# can be used to install each of the packages one by one. So, just run each
# command below (e.g., by copy-pasting it into R) line by line.

# If you are asked to create a 'personal library', choose 'yes'. If you are
# asked to choose a 'CRAN mirror', just select the default one (which is

## diamond_plot.r
# inspired by: https://github.com/mjskay/uncertainty-examples/blob/master/diamond_plot.md
# to see if we can abuse metafor::forest() and metafor::addpoly() to create such a plot

library(palmerpenguins)
library(metafor)

set.seed(1234)

dat <- penguins
dat <- dat[!is.na(dat$sex),]

## backcalculate_sd.r
# means, SDs, and group sizes
m1 <- 52
m2 <- 40
sd1 <- 6
sd2 <- 4
n1 <- 20
n2 <- 40

# pooled SD
sdp <- ((n1-1)*sd1^2 + (n2-1)*sd2^2) / (n1 + n2 - 2)

## bivariate_normal_wallpaper.r
rm(list=ls())

setwd("/home/wviechtb/misc/wallpapers/bivariate_normal")

library(mvtnorm)
library(ellipse)

### specify the correlation for the bivariate normal distribution
rho <- 0.4

## boot_rma_mv_parallel.r
############################################################################

library(metafor)
library(boot)

dat <- dat.crede2010
dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat, subset=criterion=="grade")

############################################################################

## journal_name_generator.r
genjournal <- function() {
   x1 <- sample(c("American Journal of", "Austin Journal of", "Annals of", "Archives of", "International Journal of", "Journal of", "Open Journal of", "World Journal of", "Insights Journal of", "Short Reports of", ""), 1)
   x2 <- sample(c("Clinical", "Child & Adolescent", "Family", "Current", "Preventive", "Emergency", "Novel", ""), 1)
   x3 <- sample(c("Medicine", "Health Care", "Biomedical Science", "Case Reports", "Nursing Studies", "Surgery", "Medical Case Reports", "Obstetrics", "Gynecology", "Family Medicine", "Primary Care", "Nursing", "Psychiatry", "Pulmonology", "Images", "Biology", "Behavioral Science", "Public Health", "Nursing Practice", "Respiratory Care", "Cancer", "Neurology", "Musculoskeletal Disorders", "Epidemiology", "Cardiology", "Vascular Medicine", "Hematology", "Imaging", "Microbiology", "Infectious Diseases", "Oncology", "Neuroinflammation", "Neurodegenerative Diseases", "Gerontology", "Addiction", "Rehabilitation Medicine", "Translational Medicine", "Images

## Multivariate meta-analysis
Kalaian, H. A., & Raudenbush, S. W. (1996). A multivariate mixed linear model for meta-analysis. Psychological Methods, 1(3), 227-235.

Berkey, C. S., Hoaglin, D. C., Antczak-Bouckoms, A., Mosteller, F., & Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine, 17(22), 2537-2550.

van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. Statistics in Medicine, 21(4), 589-624.

Nam, I.-S., Mengersen, K., & Garthwaite, P. (2003). Multivariate meta-analysis. Statistics in Medicine, 22(14), 2309-2333.

Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39-65.

## output.txt
> ############################################################################
>
> # A comparison of for-loops versus apply() and sapply() for 1) computing the
> # row means in a matrix and 2) for computing the means of all elements in a
> # list. For task 1), we can also examine the performance of rowMeans() as a
> # specialized / vectorized function and for task 2), we can also compare
> # sapply() with vapply() (note: vapply() was added in version R-2.12.0). Also,
> # for the for-loop, we can examine what the impact is of pre-allocating the
> # vector in which to store the results versus 'growing' the vector in each
> # iteration.

## output.txt
############################################################################

R version 2.5.0 (2007-04-23)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
	############################################################################

	library(metafor)
	library(ape)

	############################################################################

	# read the documentation for this dataset
	help(dat.moura2021)
	############################################################################

	# During the course, we will make use of a number of add-on packages for R.
	# Ideally, you should install these packages before the course (then you don't
	# have to bother with installing packages during the course). The code below
	# can be used to install each of the packages one by one. So, just run each
	# command below (e.g., by copy-pasting it into R) line by line.

	# If you are asked to create a 'personal library', choose 'yes'. If you are
	# asked to choose a 'CRAN mirror', just select the default one (which is
	# inspired by: https://github.com/mjskay/uncertainty-examples/blob/master/diamond_plot.md
	# to see if we can abuse metafor::forest() and metafor::addpoly() to create such a plot

	library(palmerpenguins)
	library(metafor)

	set.seed(1234)

	dat <- penguins
	dat <- dat[!is.na(dat$sex),]
	# means, SDs, and group sizes
	m1 <- 52
	m2 <- 40
	sd1 <- 6
	sd2 <- 4
	n1 <- 20
	n2 <- 40

	# pooled SD
	sdp <- ((n1-1)sd1^2 + (n2-1)sd2^2) / (n1 + n2 - 2)
	rm(list=ls())

	setwd("/home/wviechtb/misc/wallpapers/bivariate_normal")

	library(mvtnorm)
	library(ellipse)

	### specify the correlation for the bivariate normal distribution
	rho <- 0.4
	############################################################################

	library(metafor)
	library(boot)

	dat <- dat.crede2010
	dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat, subset=criterion=="grade")

	############################################################################
	genjournal <- function() {
	x1 <- sample(c("American Journal of", "Austin Journal of", "Annals of", "Archives of", "International Journal of", "Journal of", "Open Journal of", "World Journal of", "Insights Journal of", "Short Reports of", ""), 1)
	x2 <- sample(c("Clinical", "Child & Adolescent", "Family", "Current", "Preventive", "Emergency", "Novel", ""), 1)
	x3 <- sample(c("Medicine", "Health Care", "Biomedical Science", "Case Reports", "Nursing Studies", "Surgery", "Medical Case Reports", "Obstetrics", "Gynecology", "Family Medicine", "Primary Care", "Nursing", "Psychiatry", "Pulmonology", "Images", "Biology", "Behavioral Science", "Public Health", "Nursing Practice", "Respiratory Care", "Cancer", "Neurology", "Musculoskeletal Disorders", "Epidemiology", "Cardiology", "Vascular Medicine", "Hematology", "Imaging", "Microbiology", "Infectious Diseases", "Oncology", "Neuroinflammation", "Neurodegenerative Diseases", "Gerontology", "Addiction", "Rehabilitation Medicine", "Translational Medicine", "Images
	Kalaian, H. A., & Raudenbush, S. W. (1996). A multivariate mixed linear model for meta-analysis. Psychological Methods, 1(3), 227-235.

	Berkey, C. S., Hoaglin, D. C., Antczak-Bouckoms, A., Mosteller, F., & Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine, 17(22), 2537-2550.

	van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. Statistics in Medicine, 21(4), 589-624.

	Nam, I.-S., Mengersen, K., & Garthwaite, P. (2003). Multivariate meta-analysis. Statistics in Medicine, 22(14), 2309-2333.

	Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39-65.
	> ############################################################################
	>
	> # A comparison of for-loops versus apply() and sapply() for 1) computing the
	> # row means in a matrix and 2) for computing the means of all elements in a
	> # list. For task 1), we can also examine the performance of rowMeans() as a
	> # specialized / vectorized function and for task 2), we can also compare
	> # sapply() with vapply() (note: vapply() was added in version R-2.12.0). Also,
	> # for the for-loop, we can examine what the impact is of pre-allocating the
	> # vector in which to store the results versus 'growing' the vector in each
	> # iteration.
	############################################################################

	R version 2.5.0 (2007-04-23)
	Copyright (C) 2007 The R Foundation for Statistical Computing
	ISBN 3-900051-07-0

	R is free software and comes with ABSOLUTELY NO WARRANTY.
	You are welcome to redistribute it under certain conditions.
	Type 'license()' or 'licence()' for distribution details.