similar to: Function hatTrace in package lme4

Dear R developers, The trace of the hat matrix H~(n,n) is computed as follows: tr(H) = tr(BS^-1B') = tr(S^-1B'B) := tr(X) = sum(diag(X)) with B~(n,p), S~(p,p). Since p is of the order 10^3 but S is sparse I would like to employ Taucs linear solver ( http://www.tau.ac.il/~stoledo/taucs/ ) on SX = B'B. (Further improvement by implying a looping over i=1,...,p, calling

Hi all, This was posted originally on r-sig-mixed-models, but I thought I would post here as well as it might be of more general interest. With a colleague, I have been trying to implement the Conditional AIC described by Vaida and Blanchard 2005 Biometrika, "Conditional Akaike information for mixed-effects models". This quantity is derived in a way analogous to the AIC, but is

Hi, I'm trying to install CAIC directly into the newest version of R using the code on the R-Forge CAIC website and I get an error message: install.packages("CAIC", repos="http://R-Forge.R-project.org") Warning message: In getDependencies(pkgs, dependencies, available, lib) : package ?CAIC? is not available (for R version 2.13.1) This has worked when I've tried in

Hello, I estimated a model using SEM package in R, which was fit to a raw moment matrix, and includes an intercept term. The only goodness of fit statistics that are output are Model Chisquare, AIC, AICc, BIC, CAIC, and normalized residuals. How can I get the other goodness of fit statistics, like adjusted goodness of fit, RMSEA, and R-squared? And how can I get the final value of the

Dear R users, Would it be difficult to change the code of stepAIC (from the MASS library) to use AICc instead of AIC? It would be great to know of someone has tried this already. Best wishes Christoph.

Please help me in writing the R code for this problem. I've been solving this for 4 days. It was hard for me to solve it. It's a simulation problem in R. The problem is My true model is a normal mixture which is given as 0.5 N(-0.8,1) + 0.5 N(0.8,1). This model has two components. I will get a random sample of size 100 from this model. I will do this 300 times. That means, I will have

Dear R users: I'm generating the following survival data: set.seed(123) n=200 #sample size x=rbinom(n,size=1,prob=.5) #binomial treatment v=rgamma(n,shape=1,scale=1) #gamma frailty w=rweibull(n,shape=1,scale=1) #Weibull deviates b=-log(2) #treatment's slope t=exp( -x*b -log(v) + log(w) ) #failure times c=rep(1,n) #uncensored indicator id=seq(1:n) #individual frailty indicator

Dear all! I'm quite new to R and at this moment a little bit lost in trying to make a function work with a for loop that has two variables (i and j) and multiple if statements.. I hope that someone of you could help me. I would very appreciate it! What do I want to do? I am analysing the data of an 'eye tracker'. The eye tracker registers the coordinates to which participants

Hello, I have conducted an SEM in which the resultant standardized path coefficients are much higher than would be expected from the raw correlation matrix. To explore further, I stripped the model down to a simple bivariate relationship between two variables (NDVI, and species richness), where it's my understanding that the SEM's standardized path coefficient should equal the correlation

I wonder whether any of you know of an efficient way to calculate the approximate degrees of freedom of a gamm() fit. Calculating the smoother/projection matrix S: y -> \hat y and then its trace by sum(eigen(S))$values is what I've been doing so far- but I was hoping there might be a more efficient way than doing the spectral decomposition of an NxN-matrix. The degrees of freedom

Hi everyone, I have the following problem: I have three variables, 'group', 'city' and 'pressure' There is an interaction effect between group and city and I'd like to show this in an interaction plot: interaction.plot(group, city, pressure, type="b", col= c(1:2), leg.bty="o", leg.bg="blue", lwd=1,

Dear all, I wanted to create the mean using a algebra matrix. so I tried this one: > meanAnimals <- new3%*%factorial (Calculates the matrix multiplication of the new3 * factorial). But I get the following error message: Error in new3 %*% factorial : non-conformable arguments These are my matrices: > new3 [,1] [,2] [1,] 1.350 8.1 [2,] 465.000 423.0

Dear lmer-ers, My thanks for all of you who are sharing your trials and tribulations publicly. I was hoping to elicit some feedback on my thoughts on denominator degrees of freedom for F ratios in mixed models. These thoughts and practices result from my reading of previous postings by Doug Bates and others. - I start by assuming that the appropriate denominator degrees lies between n