similar to: logLik calculations

Hi. Thanks for your help with my previous question (comparing two lm() models with a maximum likelihood ratio test) I had a look at lrtest from the package lmtest as it has been suggested to me, but I am not 100% sure if that is the right thing to do ... lrtest uses the same log likelihoods as you can extract by hand from lm() with logLik - are this the maximum log likelihoods? How does R

Hi, I'm trying to compare models, one of which has all parameters fixed using offsets. The log-likelihoods seem reasonble in all cases except the model in which there are no free parameters (model3 in the toy example below). Any help would be appreciated. Cheers, Jarrod x<-rnorm(100) y<-rnorm(100, 1+x) model1<-lm(y~x) logLik(model1) sum(dnorm(y, predict(model1),

Hello all. I was wondering if the logLike.nls() and logLike.nlme() functions are still being used. Neither function seems to be available in the most recent release of R (1.9.1). The following is contained in the help file for logLik(): "classes which already have methods for this function include: 'glm', 'lm', 'nls' and 'gls', 'lme' and others in

hello dear list! since we want to do a model analysis and some people would like to see pseudo-R^2 values for different types of glm of a logistic regression, i've decided to write a function that computes either nagelkerkes normed pseudo-R or cox & snells pseudo-R. however, i am not clear as in the decisive step, i need to calculate the log of (maximum likelihood estimates of model

I would like to figure out the equations for calculating "AIC" in both "step() function" and "AIC () function". They are different. Then I just type "step" in the R console, and found the "AIC" used in "step() function" is "extractAIC". I went to the R help, and found: "The criterion used is AIC = - 2*log L + k *

Dear R users, I'm attempting to fit a GLM with random effects using the tweedie family for the error structure. I'm getting the error: iteration 1 Error in logLik.reStruct(object, conLin) : NA/NaN/Inf in foreign function call (arg 3) I'm running V2.1.0 I notice from searching the lists that the same error was reported in May 2004 by Spencer Graves, but no-one was able to

Dear R-experts: I am using survreg() to estimate the parameters of a Weibull density having right-censored observations. Some observations are weighted. To do that I regress the weighed observations against a column of ones. When I enter the data as 37 weighted observations, the parameter estimates are exactly the same as when I enter the data as the corresponding 70 unweighted observations.

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

I have a dataset that I am running non-linear regression on via the following code: Hill <- function(E0,Em,C50,g,C){ # # Hill is the hill interaction function. # # E0 Represents the minimum interaction Effect # # Em Represents the Maximum Interaction Effect # # C50 represents the concentration at which 50% of the effect occurs. # # gamma represents the cooperativity of the

Hi all, I am running the following model: > glm89.nb <- glm.nb(AvGUD ~ Year*Trt*Micro) where Year has 3 levels, Trt has 2 levels and Micro has 3 levels. However when I run it has a zero inflated negative binomial (as I have lots of zeros) I get the below error message: > Zinb <- zeroinfl(AvGUD ~ Year*Trt*Micro |1, data = AvGUD89, dist = "negbin") Error in optim(fn =

Dear R users, I am estimating Poisson mixed models using glmmPQL (MASS) and lmer (lme4). We know that glmmPQL do not provide the correct loglikelihood for such models (it gives the loglike of a 'pseudo' or working linear mixed model). I would like to know how the loglike is calculated by lmer. A minor question is: why do glmmPQL and lmer give different degrees-of-freedom for the same

How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare "log-linear model fits to

Hi Gregg, Below I try to address 1) The sum constraint would apply for each set ?? and ?? i.e. sum(??) = sum(??) = 1.60 2) Just like 1) the lower and upper bounds will be applied for individual set i.e. individual elements of ?? are subject to lower = c(1, -1, 0) and upper = c(2, 1, 1) and individual elements of ?? are subject to lower = c(1, -1, 0) and upper = c(2, 1, 1) I hope that I am

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

I'm getting some odd results computing log-likelihoods with gls using splines with increasing degrees of freedom -- the deviance *increases* substantially with increasing df. (Since spline models with increasing df aren't nested, it need not decline monotonically but I would expect it to have a decreasing trend!) I may just be confused, but I *think* the issue is somewhere within the

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

R-Users, I'm in the process of developing some notes on optimizing likelihoods for a class I'm teaching next term. Can anyone offer examples showing the optimization of a complex likelihood function that they wouldn't mind sharing using optim(). I've adapted some of the S-plus likelihood examplesfrom the ``Guide to Statistics" and the Zip example from ``Statistical Models in

I have a dataset like this: p aa index x y z sdx sdy sdz delta as ms cur sc 1 821p MET 1 -5.09688 32.8830 -5.857620 1.478200 1.73998 0.825778 13.7883 126.91 92.37 -0.1320180 111.0990 2 821p THR 2 -4.07357 28.6881 -4.838430 0.597674 1.37860 1.165780 13.7207 64.09 50.72 -0.0977129 98.5319 3 821p GLU 3 -5.86733 30.4759

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

I want to get the logLik to calculate McFadden.R2 ,ML.R2 and Cragg.Uhler.R2, but the value from multinom does not have logLik.So my quetion is : is logLik meaningful to multinomial logistic model from multinom?If it does, how can I get it? Thank you! ps: I konw VGAM has function to get the multinomial logistic model with logLik, but I prefer use the function from "official" R