NO.PING.PONG (version 0.1.8.7)

PLOT_NO.PING.PONG: 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)

Value

A plot is produced, but there are no returned values.

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.

Author

Brian P. O'Connor

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.

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'))


# \donttest{ 
# 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'))

# }