Nothing
"rescomp" <-
function (theta=vector(), d=vector(), currModel=currModel, currTheta=vector())
{
#browser()
if(currModel@modelspec[[1]]@lscalpar) {
thetascal <- currModel@modellist[[1]]@thetascal
theta = theta * thetascal
}
if(length(currTheta) == 0)
currTheta <- getThetaCl(theta, currModel)
groups <- currModel@groups
m <- currModel@modellist
resid <- clpindepX <-list()
nexp <- length(m)
for(i in 1:nexp) {
clpindepX[[i]] <- if(!m[[i]]@clpdep || m[[i]]@getX)
getClpindepX(model = m[[i]], theta =
currTheta[[i]], multimodel = currModel,
returnX = FALSE, rawtheta= theta, dind=0)
else matrix()
}
for(i in 1:length(groups)) {
resid[[i]] <- residPart(model = m[[1]],
group = groups[[i]], multimodel = currModel,
thetalist = currTheta, clpindepX = clpindepX,
finished = currModel@finished,
returnX = FALSE, rawtheta = theta)
if(currModel@finished){
currModel <- fillResult(group = groups[[i]],
multimodel = currModel, thetalist = currTheta,
clpindepX = clpindepX, rlist = resid[[i]],
rawtheta = theta)
}
}
if(currModel@finished) {
currModel@fit@nlsres$onls$nclp <- currModel@nclp
if(currModel@optlist[[1]]@sumnls) {
if(class(currModel@fit@nlsres$onls) == "nls")
class(currModel@fit@nlsres$onls) <- "timp.nls"
else if(class(currModel@fit@nlsres$onls) == "nls.lm")
class(currModel@fit@nlsres$onls) <- "timp.nls.lm"
else
class(currModel@fit@nlsres$onls) <- "timp.optim"
currModel@fit@nlsres$sumonls <- summary(currModel@fit@nlsres$onls,
currModel=currModel,
currTheta=currTheta)
}
if(currModel@stderrclp) {
for(i in 1:length(groups)) {
currModel <- getStdErrClp(group = groups[[i]],
multimodel = currModel, thetalist = currTheta,
clpindepX = clpindepX, rlist = resid[[i]],
rawtheta = theta)
}
}
}
## if using a trilinear type model, we have cp=AE; so separate A out.
if(currModel@finished && currModel@trilinear){
trires <- triResolve(currModel, currTheta)
currModel <- trires$currModel
currTheta <- trires$currTheta
}
if(currModel@finished && m[[1]]@mod_type == "kin") {
if (m[[1]]@fullk) {
for(i in 1:nexp) {
nocolsums <- length(m[[1]]@lightregimespec) > 0 # lightdiff (see compModel.R)
eig <- fullKF(currTheta[[i]]@kinpar, currTheta[[i]]@kinscal, m[[1]]@kmat, currTheta[[i]]@jvec, m[[1]]@fixedkmat, m[[1]]@kinscalspecial,
m[[1]]@kinscalspecialspec, nocolsums)
currTheta[[i]]@eigenvaluesK <- eig$values
}
}
}
if(currModel@finished) {
return(list(currModel=currModel,currTheta=currTheta))
}
if(currModel@algorithm == "optim") ## minimize this sum
retval <- sum(unlist(resid))
else
retval <- unlist(resid) ## nls and nls.lm want the residuals,
## to minimize the sum of their squares
retval
}
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