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2011 Jan 05
0
Nnet and AIC: selection of a parsimonious parameterisation
...}
if (optimFlag) {
return(RSS)
}else{
prn=nnWtsPrunning(nn,data,alpha,ii)
list(choice=prn$choice,nparam=prn$nparam,nparaminit=length(nn$wts),AIC=prn$AIC,nn1=prn$nn)
}
}
maxSize=maxSize+1; j=1;
choice <- numeric(maxSize); nparam <- numeric(maxSize); lambdaj <-
numeric(maxSize)
AIC <- numeric(maxSize); nparamInit <- numeric(maxSize)
ri <- sample(nifold, nrow(data), replace = TRUE)
size=seq(maxSize-1,0); minAIC=1000000
for(j in 1:maxSize) {
tlambda=optimize(CVnn1,decayRange,tol = 0.0001,formula, data, nreps=nreps,...
2006 Oct 02
1
multilevel factor model in lmer
...model is
straightforward. A two-factor model requires a set of factor loadings
multiplying a single random effect. For example, a logit model for the ith
subject responding correctly to the jth item (j=1,..,J) is
logit[p(ij)] = a1*item1(i) + ... + aJ * itemJ(i) +
lambda1*item1(i)*u(i) + ... + lambdaJ*itemJ(i)*u(i)
where the lambdas are factor loadings, with lambda1 fixed to 1.0 and
item1-itemJ are dummy variables for the items.
Thanks,
Dan
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Daniel A. Powers, Ph.D.
Department of Sociology
University of Texas at Austin
1 University Station A1700
Aus...