# bmds: Bayesian Multidimensional Scaling by Oh and Raftery (2001) In kyoustat/DAS: Distance-bAsed Statistical methods

## Description

Bayesian Multidimensional Scaling by Oh and Raftery (2001)

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```bmds( x, ndim = 2, par.a = 5, par.alpha = 0.5, par.step = 1, mc.iter = 8128, verbose = FALSE ) ```

Kisung You

## References

\insertRef

oh_bayesian_2001DAS

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```## use simple example of iris dataset with perturbation data(iris) dmat = as.matrix(stats::dist(iris[,1:4])) ## run Bayesian MDS # let's run 49 iterations (CRAN) quietly iris.cmds = cmds(dmat, ndim=2) iris.bmds = bmds(dmat, ndim=2, mc.iter=49, par.step=(2.38^2)) ## extract coordinates and class information cx = iris.cmds\$embed # embedded coordinates of CMDS bx = iris.bmds\$embed # BMDS icol = iris[,5] # class information ## visualize par(mfrow=c(1,2),pty="s") mc = paste("CMDS with STRESS=",round(iris.cmds\$stress,4),sep="") mb = paste("BMDS with STRESS=",round(iris.bmds\$stress,4),sep="") plot(cx, col=icol,pch=19,main=mc) plot(bx, col=icol,pch=19,main=mb) ```

kyoustat/DAS documentation built on Jan. 6, 2020, 7:10 a.m.