Nothing
## ------------------------------------------------------------------------
library(metaplotr)
## ---- eval = FALSE-------------------------------------------------------
# help('metaplotr')
## ---- eval = FALSE-------------------------------------------------------
# help('crosshairs')
## ------------------------------------------------------------------------
rm(list = ls())
## ------------------------------------------------------------------------
attach(FergusonBrannick2012)
## ------------------------------------------------------------------------
crosshairs(pub_z, dis_z, pub_z_se, dis_z_se)
## ------------------------------------------------------------------------
# confint option can control whiskers length.
crosshairs(pub_z, dis_z, pub_z_se, dis_z_se, confint = .7)
# You can see results of different whisker lengths.
crosshairs(pub_z, dis_z, pub_z_se, dis_z_se, confint = .95)
crosshairs(pub_z, dis_z, pub_z_se, dis_z_se, confint = .3)
## ------------------------------------------------------------------------
# use whis_on argument.
crosshairs(pub_z, dis_z, pub_z_se, dis_z_se, whis_on = FALSE)
## ------------------------------------------------------------------------
# Main and axes labels can be changed.
crosshairs(pub_z, dis_z, pub_z_se, dis_z_se,
main_lab = 'Published vs. Dissertation Effect Sizes',
x_lab = 'Published Studies',
y_lab = 'Dissertations')
## ------------------------------------------------------------------------
# We will use another data set by attaching it.
attach(Sweeney2015)
# You can find information regarding this data set as usual.
# help('Sweeney2015')
# Add descriptive statistics to graph.
crosshairs(inten_d, beh_d, inten_se, beh_se,
main_lab = 'Sweeney (2015) Data',
x_lab = 'Intentions',
y_lab = 'Behaviors',
annotate = TRUE) # use annotate argument
## ------------------------------------------------------------------------
# Boxplots will be hidden.
crosshairs(inten_d, beh_d, inten_se, beh_se,
main_lab = 'Sweeney (2015) Data',
x_lab = 'Intentions',
y_lab = 'Behaviors',
annotate = TRUE,
bxplts = FALSE)
## ------------------------------------------------------------------------
# Add moderator and label.
attach(GenderDiff02) # attach dataframe to working environment
# help('GenderDiff02')
crosshairs(men_z, women_z, men_se, women_se,
main_lab = 'Ali et al. Psychopathology and Parental Acceptance',
x_lab = 'Men',
y_lab = 'Women',
mdrtr = region,
mdrtr_lab = 'Region',
mdrtr_lab_pos = c(.1, .5))
## ------------------------------------------------------------------------
# McLeod2007 data frame is used in this graph.
# help('McLeod2007')
attach(McLeod2007) # attach dataframe to working environment
# metafor package is needed for this graph. If you do not have this package use
# install.packages('metafor')
# and load metafor.
library(metafor)
# using rma() function of metafor package.
res1 <- rma(yi = z, vi = var, method = 'DL', data = McLeod2007)
# Estimates best linear unbiased predictors.
res2 <- blup(res1)
# You can see the resuling data frame.
head(res2, 15)
# Assign data to x, standard error of x, y, standard error of y,
# variable name of a moderator (if any) here. Note how the names
# and values of the x variables come from the McLeod2007 dataset.
# The names and values of the shrunken estimates came from
# the output of the metafor program.
x1 <- McLeod2007$z
se.x1 <- sqrt(McLeod2007$var)
y1 <- res2$pred
se.y1 <- res2$se
# Create the plot.
crosshairs(x1, y1, se.x1, se.y1,
main_lab = 'Effects of Empirical Bayes Estimation',
x_lab = 'Parenting and Depression Correlations',
y_lab = 'Shrunken Estimates',
annotate = TRUE,
whis_on = FALSE)
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