search for: caic

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 availab...

Dear all, For a project I need to calculate the conditional AIC of a mixed effects model. Luckily, I found a reference in the R help forum for a function to be used: CAIC <- function(model) { sigma <- attr(VarCorr(model), 'sc') observed <- attr(model, 'y') predicted <- fitted(model) cond.loglik <- sum(dnorm(observed, predicted, sigma, log=TRUE)) rho <- hatTrace(model) p...

...etrika, "Conditional Akaike information for mixed-effects models". This quantity is derived in a way analogous to the AIC, but is appropriate for scenarios where one is interested in the particular coefficient estimates for individual random effects. The formula for the asymptotic CAIC is given as -2*log(likelihood of observed values, conditional on ML estimates of fixed effects and empirical Bayes estimates of random effects) + 2*K where K = rho + 1, and rho = "effective degrees of freedom" = trace of the hat matrix mapping predicted values onto observed values....

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

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 log-likelihood of the model? Thanks, Maya [[alternative HTML version deleted]]

...x models with frailty: fit1=coxph(Surv(t,c)~x+frailty (id,dist='gamma',sparse=TRUE,method='em')) fit2=coxph(Surv(t,c)~x+frailty (group,dist='gamma',sparse=TRUE,method='em')) fit3=coxph(Surv(t,c)~x+frailty (id,dist='gamma',sparse=TRUE,method='aic',caic=TRUE)) fit4=coxph(Surv(t,c)~x+frailty (group,dist='gamma',sparse=TRUE,method='aic',caic=TRUE)) In all cases, and after several replications, I am getting estimates of the variance of the random effect that are almost zero, whereas I thought that they should be around 1 (the var...

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