similar to: log likelihood and deviance

Dear all, I've replicated the cheese tasting example on p175 of GLM's by McCullagh and Nelder. This is a 4 treatment (rows) by 9 ordinal response (cols) table. Here's my simple code: #### cheese library(MASS) options(contrasts = c("contr.treatment", "contr.poly")) y = c(0,0, 1, 7, 8,8,19, 8,1, 6,9,12,11, 7,6, 1, 0,0, 1,1, 6, 8,23,7,

hello, I'd like tu superpose two graphics, I mean, be able to plot two functions on the same graphics .. Can someone help me ? Alvine Bissery -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body",

A maybe trivial and stupid question: In the case of a lm or glm fit, it is quite informative (to me) to have a look to the null deviance and the residual deviance of a model. This is generally provided in the print method or the summary, eg: Null Deviance: 658.8 Residual Deviance: 507.3 and (a bit simpled minded) I like to think that the proportion of deviance 'explained' by the

Hi all -- I have to calculate a likelihood ratio test for a logit model. I found logLik, but I need to calculate the log likelihood for the model without any predictors. How can I specify this in glm? If the full model is glm(y ~ x1), is the one without predictors (y ~ 0)? Or (y ~ 1)? Is there a more direct way of getting this? -- Chris

I have experimented with the cheese data example from McCullagh&Nelder, page 175. With a proportional odds model they obtain a residual deviance of 20.31. Estimating the same model with polr(MASS) gives a residual deviance of 762.11 !, while using ordglm(gnlm) gives a deviance of 523.94. Can anybody explain these differences? The data frame with the data are: > cheese Cheese N

It has always been my understanding that deviance for GLMs is defined by; D = -2(loglikelihood(model) - loglikelihood(saturated model)) and this can be calculated by (or at least usually is); D = -2(loglikelihood(model)) As is done so in the code for 'polr' by Brian Ripley (in the package 'MASS') where the -loglikehood is minimised using optim; res <-

Dear list, I am running some zero inflated models and would like to know what the deviance of the models. Unlike running a normal GLM where the deviance is displayed in the summary all that is displayed in a summary of the zero inflated model is the log likelihood. I hope this isn't a read the manual question, and if it is I apologize for wasting your time, but if you could still send me a

Greetings, everybody. Can I ask some glm questions? 1. How do you find out -2*lnL(saturated model)? In the output from glm, I find: Null deviance: which I think is -2[lnL(null) - lnL(saturated)] Residual deviance: -2[lnL(fitted) - lnL(saturated)] The Null model is the one that includes the constant only (plus offset if specified). Right? I can use the Null and Residual deviance to

I have fitted the faults.data to glm.nb and to the function negbin from the package aod. The output of both is the following: summary(glm.nb(n~ll, data=faults)) Call: glm.nb(formula = n ~ ll, data = faults, init.theta = 8.667407437, link = log) Deviance Residuals: Min 1Q Median 3Q Max -2.0470 -0.7815 -0.1723 0.4275 2.0896 Coefficients:

Two related questions. First, I am fitting a model with a single predictor, and then a null model with only the intercept. In theory, the fitted model should have a higher log-likelihood than the null model, but that does not happen. See the output below. My first question is, how can this happen? > m Call: glm(formula = school ~ sv_conform, family = binomial, data = dat, weights =

I've computed a loglinear model on a categorical dataset. I would like to test whether an interaction can be dropped by comparing the log-likelihoods from two models(the model with the interaction vs. the model without). Since R does not immediately print the log-likelihood when I use the "glm" function, I used SAS initially. After searching for an extracting function, I found one

Hi all, I'm working on a multinomial (or "polytomous") logistic regression using R and have made great progress using multinom() from the nnet library. My response variable has three categories, and there are two different possible predictors. I'd like to use the likelihoods of certain models (ie, saturated, fitteds, and null) to calculate Nagelkerke R-squared values for

Dear list, I am fitting a logistic multi-level regression model and need to test the difference between the ordinary logistic regression from a glm() fit and the mixed effects fit from glmer(), basically I want to do a likelihood ratio test between the two fits. The data are like this: My outcome is a (1,0) for health status, I have several (1,0) dummy variables RURAL, SMOKE, DRINK, EMPLOYED,

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of David Firth > Sent: Tuesday, March 16, 2004 1:12 PM > To: Paul Johnson > Cc: r-help at r-project.org > Subject: Re: [R] glm questions > > > Dear Paul > > Here are some attempts at your questions. I hope it's of some help.

Hello, The question looks like simple. It's probably even stupid. But I spent several hours searching Internet, downloaded tons of papers, where deviance is mentioned and... And haven't found an answer. Well, it is clear for me the using of entropy when I split some node of a classification tree. The sense is clear, because entropy is an old good measure of how uniform is distribution.

Running a binary logit model on the data df <- data.frame(y=sample(letters[1:3], 100, repl=T), x=rnorm(100)) reveals some residual deviance: summary(glm(y ~ ., data=df, family=binomial("logit"))) However, running a multinomial model on that data (multinom, nnet) reveals a residual deviance: summary(multinom(y ~ ., data=df)) On page 203, the MASS book says that "here the

Folks, I am not sure if it's a feature or a "bug". The same is observed in Splus. Suppose I have Poisson counts, and I would like to estimate the parameter using glm. I would assume I can feed it the individual counts, or I can feed it the distinctive counts with the frequency as the weights, and I would get the same results. I do, but the deviance df are returned differently.

Hello, I have a question about the add1() function and quasilikelihoods for GLMs. I am fitting quasi-Poisson models using glm(, family = quasipoisson). Technically, with the quasilikelihood approach the deviance does not have the interpretation as a likelihood-based measure of sample information. Functions such as stepAIC() cannot be used. The function add1() returns the change in the scaled

Dear list, I'm wondering if anyone can help me calculate the deviance of either a zeroinfl or hurdle model from package pscl? Even if someone could point me to the correct formula for calculating the deviance, I could do the rest on my own. I am trying to calculate a pseudo-R-squared measure based on the R^{2}_{DEV} of [1], so I need to be able to calculate the deviance of the full and null

Hello I have a problem when fitting a mixed generalised linear model with the lmer-function in the Matrix package, version 0.98-7. I have a respons variable (sfox) that is 1 or 0, whether a roe deer fawn is killed or not by red fox. This is expected to be related to e.g. the density of red fox (roefoxratio) or other variables. In addition, we account for family effects by adding the mother