search for: region4

...") split.screen(c(2,2)) #Load maps once only w <- read.shape("c:\\data\\region1.shp", dbf.data = TRUE) x <- read.shape("c:\\data\\region2.shp", dbf.data = TRUE) y <- read.shape("c:\\data\\region3.shp", dbf.data = TRUE) z <- read.shape("c:\\data\\region4.shp", dbf.data = TRUE) #Loop through the clusters and produce maps of each region #The base maps stay the same with the exception of map1 #which has the title of Cluster # and the points which #reflect the current cluster. #Subset data on cluster for(i in 1:ClusCount){ mapit <- subset(da...

For example, PcaHubert, how to get the variance explained which are similar to those concepts in traditional PCA? In traditional PCA, you have a bunch of eigenvalue lambdas... and you sort the lambdas from the biggest to the smallest, the lambda_i / (sum of all lambdas) is the variance explained by that principal component... how to obtain the equivalent concepts in PcaHubert? Thanks a lot!