Morpho (version 2.12)

RegScore: calulate regression scores for linear model

Description

calulate regression scores for linear model as specified in Drake & Klingenberg(2008)

Usage

RegScore(model, x = NULL)

Value

returns a n x m matrix containing the regression scores for each specimen.

Arguments

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.

Warning

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.

Details

the data are orthogonally projected onto the regression lines associated with each factor.

References

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.

Examples

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])
if (FALSE) {
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)]])
}