::Free Statistics and Forecasting Software::

All rights reserved. The non-commercial (academic) use of this software is free of charge. The only thing that is asked in return is to cite this software when results are used in publications.

This R module performs the Bayesian t Test for two independent samples based on the Gibbs-Sampling algorithm (a type of Markov Chain Monte Carlo simulation).

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

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Source code of R module

library(BEST) par1 <- as.numeric(par1) #column number of first sample par2 <- as.numeric(par2) #column number of second sample par3 <- as.numeric(par3) par4 <- as.numeric(par4) if(par6=="yes") { par7 <- as.numeric(par7) par8 <- as.numeric(par8) par9 <- as.numeric(par9) par10 <- as.numeric(par10) par11 <- as.numeric(par11) par12 <- as.numeric(par12) par13 <- as.numeric(par13) par14 <- as.numeric(par14) par15 <- as.numeric(par15) par16 <- as.numeric(par16) } z <- t(y) if (par1 == par2) stop("Please, select two different column numbers") if (par1 < 1) stop("Please, select a column number greater than zero for the first sample") if (par2 < 1) stop("Please, select a column number greater than zero for the second sample") if (par1 > length(z[1,])) stop("The column number for the first sample should be smaller") if (par2 > length(z[1,])) stop("The column number for the second sample should be smaller") if(par6=="no") { if(par5=="unpaired") { (r <- BESTmcmc(z[,par1],z[,par2], parallel=F)) } if(par5=="paired") { (r <- BESTmcmc(z[,par1]-z[,par2], parallel=F)) } } else { yy <- cbind(z[1,],z[2,]) if(par5=="unpaired") { (r <- BESTmcmc(z[,par1],z[,par2], priors=list(muM = c(par7,par8), muSD = c(par9,par10), sigmaMode = c(par11,par12), sigmaSD = c(par13,par14), nuMean = par15, nuSD = par16), parallel=F)) } if(par5=="paired") { (r <- BESTmcmc(z[,par1]-z[,par2], priors=list(muM = c(par7,par8), muSD = c(par9,par10), sigmaMode = c(par11,par12), sigmaSD = c(par13,par14), nuMean = par15, nuSD = par16), parallel=F)) } } bitmap(file="test2.png") plot(r, credMass=par4, compVal=par3) dev.off() load(file="createtable") a<-table.start() a<-table.row.start(a) a<-table.element(a,"Bayesian Two Sample Test",1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('summary(r, credMass=par4, compValeff=par3)'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file="mytable.tab")

Cite this software as:

Wessa, P. (2016). Bayesian Two Sample Test (v1.0.7) in Free Statistics Software (v1.2.1), Office for Research Development and Education, URL http://www.wessa.net/rwasp_Bayesian_Two_Sample_Test.wasp

The R code is based on :

Kruschke, J. K. (2012). Bayesian estimation supersedes the t test, Journal of Experimental Psychology: General Version of May 31, 2012

To cite Wessa.net in publications use:
Wessa, P. (2024), Free Statistics Software, Office for Research Development and Education,
version 1.2.1, URL https://www.wessa.net/

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Software Version : 1.2.1
Algorithms & Software : Patrick Wessa, PhD
Server : www.wessa.net

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