Description Details Author(s) References Examples
An alternative to t tests, producing posterior estimates for groups means and standard deviations and their differences and effect sizes. Bayesian estimation provides a much richer picture of the data, and can be summarized as point estimates and credible intervals.
The core function, BESTmcmc
, generates posterior distributions to compare the means of two groups, or to compare the mean of one group with a standard, taking into account the standard deviation(s). It is thus similar to a t test. However, our Bayesian approach results in probability statements about the values of interest, rather than p-values and significance levels.
In addition, the procedure accounts for departures from normality by using a t-distribution to model the variable of interest and estimating a measure of normality.
Functions to summarize and to visualize the output are provided.
The function BESTpower
allows simulation-based estimates of power, either retrospective power directly with BESTmcmc
output or prospective power analysis with makeData
.
Original code by John K. Kruschke, packaged by Mike Meredith.
Kruschke, J. K. 2013. Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General 142(2):573-603. doi: 10.1037/a0029146
Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, especially Chapter 18.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ## Comparison of two groups:
## =========================
y1 <- c(5.77, 5.33, 4.59, 4.33, 3.66, 4.48)
y2 <- c(3.88, 3.55, 3.29, 2.59, 2.33, 3.59)
# Run an analysis, takes up to 1 min.
BESTout <- BESTmcmc(y1, y2, parallel=FALSE)
# Look at the result:
BESTout
summary(BESTout)
plot(BESTout)
plot(BESTout, "sd")
plotPostPred(BESTout)
plotAll(BESTout, credMass=0.8, ROPEm=c(-0.1,0.1),
ROPEeff=c(-0.2,0.2), compValm=0.5)
plotAll(BESTout, credMass=0.8, ROPEm=c(-0.1,0.1),
ROPEeff=c(-0.2,0.2), compValm=0.5, showCurve=TRUE)
summary(BESTout, credMass=0.8, ROPEm=c(-0.1,0.1), ROPEsd=c(-0.15,0.15),
ROPEeff=c(-0.2,0.2))
pairs(BESTout)
head(BESTout$mu1)
muDiff <- BESTout$mu1 - BESTout$mu2
mean(muDiff > 1.5)
mean(BESTout$sigma1 - BESTout$sigma2)
hist(BESTout$nu)
# Retrospective power analysis
# ----------------------------
# This takes time, so we do 2 simulations here; a real analysis needs several hundred
powerRet <- BESTpower(BESTout, N1=length(y1), N2=length(y2),
ROPEm=c(-0.1,0.1), maxHDIWm=2.0, nRep=2, parallel=FALSE)
powerRet
# We only set criteria for the mean, so results for sd and effect size are all NA.
## Analysis with a single group:
## =============================
y0 <- c(1.89, 1.78, 1.30, 1.74, 1.33, 0.89)
# Run an analysis, takes up to 40 secs.
BESTout1 <- BESTmcmc(y0, parallel=FALSE)
BESTout1
summary(BESTout1)
plot(BESTout1)
head(BESTout1$mu)
mean(BESTout1$sigma)
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