calulate regression scores for linear model
calulate regression scores for linear model as specified in Drake & Klingenberg(2008)
RegScore(model, x = NULL)
model |
linear model |
x |
optional: matrix containing fitted data to be projected onto the regression lines. If omitted the model's fitted values will be used. |
the data are orthogonally projected onto the regression lines associated with each factor.
returns a n x m matrix containing the regression scores for each specimen.
if model contains factors with more than 2 levels, R calculates one regression line per 2 factors. Check the colnames of the returned matrix to select the appropriate one. See examples for details.
Drake, AG. & Klingenberg, CP. The pace of morphological change: historical transformation of skull shape in St Bernard dogs. Proceedings of the Royal Society B: Biological Sciences, The Royal Society, 2008, 275, 71-76.
model <- lm(as.matrix(iris[,1:3]) ~ iris[,4]) rs <- RegScore(model) plot(rs,iris[,4]) ##now use a random subsample for model fitting rand <- sample(nrow(iris)) x <- iris[rand[1:100],4] newmod <- lm(as.matrix(iris[rand[1:100],1:3]) ~ x) ##predict the rest of data and get their regression scores rsPred <- RegScore(newmod,as.matrix(iris[rand[101:150],1:3])) plot(rsPred,iris[rand[101:150],4]) ## Not run: data(boneData) proc <- procSym(boneLM) pop.sex <- name2factor(boneLM,which=3:4) # generate a factor with 4 levels lm.ps.size <- lm(proc$PCscores ~ pop.sex+proc$size) rs <- RegScore(lm.ps.size) colnames(rs) # in this case, the last column contains the regression # scores associated with proc$size ## validate by using a subsample for fitting rand <- sample(dim(boneLM)[3]) lm.ps.size0 <- lm(proc$PCscores[rand[1:50],] ~ proc$size[rand[1:50]]) rs0 <- RegScore(lm.ps.size0,proc$PCscores[rand[-c(1:50)],] ) plot(rs0,proc$size[rand[-c(1:50)]]) ## End(Not run)
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