| PLOT_NO.PING.PONG {NO.PING.PONG} | R Documentation |
Plots of output from the NO.PING.PONG function
Description
Provides plots of the output from the NO.PING.PONG function for a sequence of studies
Usage
PLOT_NO.PING.PONG(nopingpongOutput,
plot_this = c('NHST','CUM_META','BAYES_SR'),
plot_save = FALSE, plot_save_type = 'png',
plot_title=NULL, Xrange=NULL)Arguments
nopingpongOutput |
Output from the NO.PING.PONG function |
plot_this |
The kind of output data to be plotted. The options are one or any combination of 'NHST', 'CUM_META', 'BAYES_GEN', 'BAYES_RAW', and 'BAYES_SR'. 'CUM_META' is for meta-analysis results; 'BAYES_GEN' is for results based on Bayesian generated data analyses; 'BAYES_RAW' is for results based on Bayesian raw data analyses; 'BAYES_SR' is for results based on the Schmidt-Raju (2007) Bayesian method. The default is plot_this = c('NHST', 'CUM_META', 'BAYES_SR'). |
plot_save |
Should a plot be saved to disk? TRUE or FALSE (the default). |
plot_save_type |
The output format if plot_save = TRUE. The options are 'bitmap', 'tiff', 'png' (the default), 'jpeg', and 'bmp'. |
plot_title |
optional. A title for the plot that will appear in the saved file name. |
Xrange |
optional. A range for the x axis in the plots. |
Details
This function provides plots of the output from the NO.PING.PONG function for a sequence of studies, with options for specifying the kind of results to be plotted (via the plot_this argument), whether to save the plot to disc, the file type of the saved plot (via the plot_save_type argument), the plot title, and the x axis range for the plot.
Value
A plot is produced, but there are no returned values.
Author(s)
Brian P. O'Connor
References
O'Connor, B. P., & Ermacora, D. (2021). Unnecessary ping-pong: Illustrations of
why previous findings should be taken into account when evaluating new datasets.
Canadian Journal of Behavioural Science, 53(3), 328-341. https://doi.org/10.1037/cbs0000259
O'Connor, B. P., & Khattar, N. (2022). Controversies regarding
null hypothesis testing. In W. O'Donohue, A. Masuda, & S. O. Lilienfeld (Eds.).
Avoiding Questionable Research Practices in Applied Psychology (pp. 147-174).
Cham, Switzerland: Springer Nature Switzerland.
Examples
# data from SchmidtRaju (2007, p. 303)
data_Schmidt_Raju <- '
1 60 .44
2 75 .20
3 85 .60
4 110 .32
5 50 .41
6 90 .25
7 100 .12
8 65 .35
9 80 .35
10 65 .19 '
data_Schmidt_Raju <- data.frame(read.table(text=data_Schmidt_Raju, fill=TRUE))
colnames(data_Schmidt_Raju) <- c('Study','N','r')
data_Schmidt_Raju <- data_Schmidt_Raju[,2:3]
nppOutput <- NO.PING.PONG(data_Schmidt_Raju, ES_type_IN='r', ES_type_OUT='r',
ma_method='REML',
Bayes_type = c('Schmidt_Raju', 'generated'),
prior_type='META', CI_level_in = 95,
ES = 'r', N = 'N', ES_var = NULL,
nitt=13000, burnin=3000, thin=10, verbose=TRUE)
PLOT_NO.PING.PONG(nppOutput, plot_this = c('NHST','CUM_META','BAYES_SR','BAYES_GEN'))
# Cannabis Psychosis data
nppOutput <- NO.PING.PONG(data_NPP$Cannabis_Psychosis, ES_type_IN='d', ma_method='REML',
Bayes_type = c('Schmidt_Raju', 'generated'), prior_type='META',
ES = 'Std_diff_in_mean', N = 'N', ES_var = NULL, #ES_var = 'Variance',
nitt=13000, burnin=3000, thin=10, verbose=TRUE)
PLOT_NO.PING.PONG(nppOutput, plot_this = c('NHST','CUM_META'))
PLOT_NO.PING.PONG(nppOutput, plot_this = c('NHST','CUM_META','BAYES_SR','BAYES_GEN'))
PLOT_NO.PING.PONG(nppOutput, plot_this = c('NHST','CUM_META','BAYES_GEN','BAYES_RAW'))
# PopulationR.20 data (has raw data)
nppOutput <- NO.PING.PONG(data_NPP$PopulationR.20, ES_type_OUT='r',
rawdata_type = 'for_correl',
ma_method='REML',
Bayes_type = c('generated', 'Schmidt_Raju'),
prior_type='META', CI_level_in = 95,
ES = 'r', N = 'N', ES_var = NULL,
nitt=13000, burnin=3000, thin=10, verbose=TRUE)
PLOT_NO.PING.PONG(nppOutput, plot_this = c('NHST','CUM_META','BAYES_GEN','BAYES_RAW'))
# raw data for paired samples
nppOutput <-
NO.PING.PONG(donnes=data_NPP$Paired_Samples,
rawdata_type = 'paired_samples',
ES_type_OUT = 'd',
paired_samples_ES_type = 'SMCRH')
PLOT_NO.PING.PONG(nppOutput, plot_this = c('NHST','CUM_META'))
PLOT_NO.PING.PONG(nppOutput, plot_this = c('NHST','CUM_META','BAYES_GEN'))
PLOT_NO.PING.PONG(nppOutput, plot_this = c('NHST','CUM_META','BAYES_GEN','BAYES_RAW'))