Nothing
make.dmmobj <-
function(p=NULL,components=c("VarG(Ia)"),...){
# make.dmmobj() - construct an object of class 'dmm' from user-supplied
# variance/covariance matrices
# make component tables
ctable <- make.ctable()
# check components in ctable
if(any(is.na(match(components,ctable$all)))){
print(components)
stop("Component(s) not recognized:\n")
}
if(is.null(p)) {
stop("make.dmmobj(): phenotypic covariance matrix must be specified\n")
}
# p <- nearPD(p)$mat
p <- as.matrix(nearPD(p,ensureSymmetry=T)$mat)
traits <- dimnames(p)[[1]]
traitpairs <- permpaste(traits)
l <- length(traits)
comp <- list(...)
vcomp <- length(comp)
v <- length(components)
siga <- matrix(0,v,l*l)
for(i in 1:v){
siga[i, ] <- as.vector(comp[[i]])
}
dimnames(siga) <- list(components,traitpairs)
# kludge an am object for siga.posdef
am <- list(v=v,l=l)
# check siga positive definite
siga <- siga.posdef(siga,am,ctable)
varcom <- matrix(0,v+1,l*l)
varcom[1:v, ] <- siga[ , ]
varcom[v+1, ] <- as.vector(p)
dimnames(varcom) <- list(c(components,"VarP(I)"),traitpairs)
# kludge a b matrix
b <- matrix(0,1,l)
dimnames(b) <- list("dummy",traits)
retobj <- list(aov=NULL,mdf=NULL,fixform=NULL,b=b,seb=NULL,vara=NULL,totn=NULL,
degf=NULL,dme.wmat=NULL,dme.correl=NULL,dme.fit=NULL,dmeopt=NULL,
siga=siga, sesiga=NULL,vard=NULL,degfd=NULL,component=components,
correlation=NULL,correlation.variance=NULL,correlation.se=NULL,
fraction=NULL,fraction.variance=NULL,fraction.se=NULL,
variance.components=varcom,variance.components.se=NULL,
phenotypic.variance=p,phenotypic.variance.se=NULL,
observed.variance=NULL,call=NULL)
class(retobj) <- "dmm"
return(retobj)
}
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