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Dear R Users, I am trying to obtain a best-fit analytic distribution for a dataset with 11535459 entries. The data range in value from 1 to 300000000. I use: fitdistr(data, "gamma") to obtain mle's for the parameters. I get the following error: Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : non-finite finite-difference value [1] And the following warnings:

...R code ######################################## gamma.param1 <- shape.mom gamma.param2 <- scale.mom log.gamma.param1 <- log(gamma.param1) log.gamma.param2 <- log(gamma.param2) gammaLoglik <- function(params, negative=TRUE){ lglk <- sum(dgamma(data, shape=exp(params[1]), scale=exp(params[2]), log=TRUE)) if(negative) return(-lglk) else return(lglk) } optim.list <- optim(c(log.gamma.param1, log.gamma.param2), gammaLoglik) gamma.param1 <- exp(optim.list$par[1]) gamma.param2 <- exp(optim.list$par[...

Hello, Is there a way to compare the relative relevance of fixed and random effects in mixed models? I have in mind measures of effect size in ANOVAs, and would like to obtain similar information with mixed models. Are there information criteria that allow to compare the relevance of each of the effects in a mixed model to the overall fit? Thank you, Bruno

Hello, all: I have a dataset with 2 groups. I want to estimate 2 means and 2 standard deviations. I naively think I should be able to use lme to do that, e.g., lme(y~gp, random=y~1|gp, method='ML'). I think I should get the same answer as from lm(y~1, ...) within each level of group. I can get the same means, but I don't know how to extract the within-gp standard

Hi, everyone I am trying to estimate 3 parameters for my survival function. It's very complicated. The negative loglikelihood function is: l<- function(m1,m2,b) -sum( d*( log(m1) + log(m2) + log(1- exp(-(b + m2)*t)) ) + (m1/b - d)*log(m2 + b*exp(-(b + m2)*t) ) + m1*t - m1/b*log(b+m2) ) here d and t are given, "sum" means sum over these two vairables. the parameters

I use model comparison with glms without mixed effects with anova(modelA,modelB), with mixed effects glm (glmmPQL), this doesn't work. Is there a way to compare model fits with glmmPQL's? Paula M. den Hartog Behavioural Biology Institute of Biology Leiden Leiden University [[alternative HTML version deleted]]