Predict shapes based on linear models calculated from PCscores
Predict shapes based on linear models calculated from PCscores.
predictShape.lm(fit, datamod, PC, mshape)
fit |
model of class |
datamod |
a one-sided "model" formula, of the form |
PC |
Matrix/vector containing Principal components (rotation matrix)
corresponding to PC-scores used in |
mshape |
matrix of the meanshape's landmarks by which the data was centered before rotation in covariance eigenspace. |
This function predicts the landmarks based on models calculated from PCscores.
predicted |
array or matrix containing predicted landmark coordinates |
predictedPC |
matrix containing predicted PC-scores |
Make sure that the levels of the variables used in
datamod correspond exactly to those used in fit. Otherwise
model matrix will be calculated erroneous.
data(boneData)
proc <- procSym(boneLM)
pop <- name2factor(boneLM,which=3)
##easy model with only one factor based on the first four PCs
fit <- lm(proc$PCscores[,1:4] ~ pop)
## get shape for Europeans only
datamod <- ~as.factor(levels(pop))[2]
Eu <- predictShape.lm(fit,datamod, proc$PCs[,1:4],proc$mshape)
## get shape for Europeans and Chinese
datamod <- ~as.factor(levels(pop))
pred <- predictShape.lm(fit,datamod, proc$PCs[,1:4],proc$mshape)
## Not run:
deformGrid3d(pred$predicted[,,1], pred$predicted[,,2], ngrid = 0)
## End(Not run)
## more complicated model
sex <- name2factor(boneLM,which=4)
fit <- lm(proc$PCscores[,1:4] ~ pop*sex)
## predict female for chinese and European
datamod <- ~(as.factor(levels(pop))*rep(as.factor(levels(sex))[1],2))
pred <- predictShape.lm(fit,datamod, proc$PCs[,1:4],proc$mshape)
## predict female and malefor chinese and European
popmod <- factor(c(rep("eu",2),rep("ch",2)))
sexmod <- rep(as.factor(levels(sex)),2)
datamod <- ~(popmod*sexmod)
pred <- predictShape.lm(fit,datamod, proc$PCs[,1:4],proc$mshape)
## add some (randomly generated) numeric covariate
somevalue <- rnorm(80,sd=10)
fit <- lm(proc$PCscores[,1:4] ~ pop+somevalue)
probs <- quantile(somevalue, probs=c(0.05, 0.95))
## make model for European at 5% and 95% quantile
popmod <- rep(factor(levels(pop))[2],2)
datamod <- ~(popmod+probs)
pred <- predictShape.lm(fit,datamod, proc$PCs[,1:4],proc$mshape)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.