dimest: Estimate dimensionality of dissimilarity matrix

In markallenthornton/dimest: Dissimilarity Matrix Dimensionality Estimation

Description

Wrapper function for dimensionality estimation methods

Usage

dimest(dmat,epsratio=NULL,mds.control=list(type="ordinal",itmax=1000))

Arguments

dmat	vectorized dissimilarity matrix, or m x n matrix of n vectorized dissimilarity matrices epsratio convergence criterion. Leave default = NULL for method specific defaults: dimest.cv = .001, dimest.R2 = .1 mds.control list of smacofSym parameters: type, defaults to "ordinal"; itmax, defaults to 1000

Details

Wrapper function for dimest.cv and dimest.R2. Detects which method to use based on whether dmat argument is a matrix, for dimest.cv, or a vector, for dimest.R2. The epsratio is set automatically to the default of whichever method is chosen, but can be manually overridden. The smacof multidimensional scaling type defaults to ordinal for dealing with ordinal human ratings or neural pattern dissimilarity matrices with global components.

Value

ndim estimated number of dimensions cv.cor for dimest.cv, vector of mean crossvalidated correlations between mds configurations and left out data r2 for dimest.R2, R-squared of configuration with ndim dimensions

See Also

smacofSym dimest.cv dimest.R2

Examples

set.seed(1)
dat <- matrix(rnorm(200),100,2)
dmat <- as.vector(dist(dat))
dmat2 <- replicate(2,dmat+rnorm(4950))
dimR2fit <- dimest(dmat)
dimcvfit <- dimest(dmat2)

markallenthornton/dimest documentation built on May 21, 2019, 11:48 a.m.