# R/dimest.R2.R In markallenthornton/dimest: Dissimilarity Matrix Dimensionality Estimation

#### Documented in dimest.R2

```#' Estimate dimensionality by R-squared
#'
#' @description Estimate dimensionality by change in multdimensional scaling R-squared
#' @usage dimest.R2(dmat,epsratio=.1,mds.control=list(type="ordinal",itmax=1000))
#' @param dmat vectorized dissimilarity matrix
#' epsratio convergence ratio, defaults to .1
#' mds.control list of smacofSym parameters: type, defaults to "ordinal"; itmax, defaults to 1000
#' @return ndim estimated number of dimensions
#' r2 R-squared of configuration with ndim dimensions
#' @details Defines identity matrix of dimensions n x n. Reproduces functionality of identically named MATLAB function.
#' @examples
#' set.seed(1)
#' dat <- matrix(rnorm(200),100,2)
#' dmat <- as.vector(dist(dat))
#' dimR2fit <- dimest.R2(dmat)
#' @export dimest.R2
dimest.R2 <- function(dmat,epsratio=.1,mds.control=list(type="ordinal",itmax=1000)){
ndim <- 1
converged = FALSE
r2last <- 0
while (!converged){
mdfit <- smacof::smacofSym(squareform(dmat),ndim,type=mds.control\$type,itmax=mds.control\$itmax)
r2 <- cor(mdfit\$confdist,dmat)^2
if (ndim == 1){
r2eps <- epsratio*r2
}
if (r2-r2last < r2eps){
converged=TRUE
} else {
ndim <- ndim + 1
r2last <- r2
}
}
result <- list(r2=r2,ndim=ndim-1)
return(result)
}
```
markallenthornton/dimest documentation built on June 17, 2018, 12:44 a.m.