similar to: How to perform a Likelihood-ratio test?

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 tried the following: F<-function(x,y,b1,b2,b3) b1/(1+b2*(x+b3*y)); t<-data.frame(ExM=c(T$ExM,F(T$S1,T$S2,0.2,0.2,51.28205)),S1=rep(T$S1,2),S2=rep(T$S2,2),gr=c(rep(1,length(T$ExM)),rep(2,length(T$ExM)))); library(lattice); wireframe(ExM ~ S1 * S2, data = t, groups=gr); where T is da dataframe like T<-data.frame(S1=...,S2=...,ExM=...) But the resulting response

Hi guRus, My discipline (experimental psychology) is gradually moving away from Null Hypothesis Testing and towards measures of evidence. One measure of evidence that has been popular of late is the likelihood ratio. Glover & Dixon (2005) demonstrate the calculation of the likelihood ratio from ANOVA tables, but I'm also interested in non-parametric statistics and wonder if anyone has any

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 am analysing some binary data with a mixed effects model using glmmPQL. I am aware that I cannot use the AIC values to help me find the minimum adequate model so how do I perform likelihood ratio tests? I need to fix on the minimum adequate model but I'm not sure of the proper way to do this. Thank you very much, Elizabeth Boakes Elizabeth Boakes PhD Student Institute of Zoology

Hi, I want to do a global likelihood ratio test for the proportional odds logistic regression model and am unsure how to go about it. I am using the polr() function in library(MASS). 1. Is the p-value from the likelihood ratio test obtained by anova(fit1,fit2), where fit1 is the polr model with only the intercept and fit2 is the full polr model (refer to example below)? So in the case of the

Hello, I would like to get the likelihood ratio and score tests for specific variables in a multivariate coxph model. The default is Wald, so the tests for each separate variable is based on Wald's test. I have the other tests for the full model but I don't know how to get them for each variable. Any idea? David Biau. [[alternative HTML version deleted]]

I'm applying logistic regression to a moderate sized data set for which I believe Wald based confidence intervals on B coefficients are too conservative. Some of the literature recommends using confidence intervals based on the likelihood ratio in such cases, but I'm having difficulty locating a package that can do these. Any help would be immensely appreciated. Best, Jeff Hanna --

Hello, I am using the following model model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair), data=MateChoice, REML=F) 1. After reading around through the R help, I have learned that the above code is the right way to analyze a mixed model with the MatingPair as the fixed effect, DrugPair as the random effect and the interaction between these two as the random effect as well.

Dear R-help, Can anybody tell me which R package has Lo-Mendell Rubin LR test and Bootstrap LR test to compare the model fit between k class and k+1 class model for Latent class analysis? Thanks in advance,  warn regards,Ms.Karunambigai M PhD Scholar Dept. of Biostatistics NIMHANS Bangalore India [[alternative HTML version deleted]]

It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected? In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.

Hi All I have to run multiple stimations and to compute Likelihhod ratio. If I compute ls function with coef and summary I can extract "outputs" that I need. I am not able to find something similar to log liklihood.... Can you pease tell me running a ls function x on y how to extract if posible LR statitic or Likelihood or Log likelihood. Many thanks in advance. If you send me

Hello, everybody, There is the first time for me to post a question, because I really cannot find answer from books, websites or my colleagues. Thank you in advance for your help! I am running likelihood ratio test to find if the simpler model is not significant from more complicated model. However, when I run LRT to compare them, the test did not return F value and p-value for me. What's the

Dear Peter, I am running your g.test() with the william's correction but I have a question about the input numbers. These are my data: "Our data are consistent with those obtained using microarray comparative genome hybridization in that we found significantly fewer variants per Mb on the X compared to the autosomal chromosomes (152 versus 336 respectively, G = 93.4, P < 2e-16, df =

Dear R list, From the lrm() binary logistic model we derived the G2 value or the likelihood-ratio chi-squared statistic given as L.R. model, in the output of the lrm(). How can this value be penalized for non-linearity (we used splines in the lrm function)? lrm.iRVI <- lrm(arson ~ rcs(iRVI,5), penalty=list(simple=10,nonlinear=100,nonlinear.interaction=4)) This didn’t work

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,

Hi. I have the following two ecdf plots in one graph: plot( ecdf(....), do.points=FALSE, verticals=TRUE, main=paste("Ecdf of distances ",DIM,sep=""), col="red" ); lines( ecdf(....), do.points=FALSE, verticals=TRUE ); How do I change the color of the resulting graph? Adding col="red" to either plot or lines results in an error

Hey! Could you please take a quick look at what I have done? Somehow I get wrong results using the anova(lm()) combination compared to doing a two way ANOVA by hand. Running: Data<-read.table("Data.txt"); g<-lm(ExM~S1*S2,Data); anova(g); Gives: Analysis of Variance Table Response: ExM Df Sum Sq Mean Sq F value Pr(>F) S1 1 4.3679

I'd like to be able to test linear hypotheses after setting up and running a model using optim or perhaps nlm. One hypothesis I need to test are that the average of several coefficients is less than zero, so I don't believe I can use the likelihood ratio test. I can't seem to find a provision anywhere for testing linear combinations of coefficients after max. likelihood. Cheers

I would like to try a likelihood ratio test in place of waldtest. Ideally I'd like to provide two glm models, the second a submodel of the first, in the style of lrt (http://www.pik-potsdam.de/~hrust/tools/farismahelp/lrt.html). [lrt takes farimsa objects] Does anyone know of such a likelihood ratio test? Chris Elsaesser, PhD Principal Scientist, Machine Learning SPADAC Inc. 7921