similar to: Running a likelihood ratio test for a logit model

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,

I am currently running a generalized linear mixed effect model using glmer and I want to estimate how much of the variance is explained by my random factor. summary(glmer(cbind(female,male)~date+(1|dam),family=binomial,data= liz3")) Generalized linear mixed model fit by the Laplace approximation Formula: cbind(female, male) ~ date + (1 | dam) Data: liz3 AIC BIC logLik deviance 241.3

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

Hello everyone I appologize for my lack of experience in statistical methods. I am an R user begginer and I am running a logit model using "zelig" and "pcse" packages. I will go to the point and is that Im having problems with interpreting the results of my models.. It is really simple (I guess for the most advanced scholars) however I really dont understand how to interpret

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 statistician experts, Sorry if this is a trivial question, or the old same question (i don't know what is the efficient key word for this issue). In order to understand the calculation of parameter of logistic regression, I did an exercise through spreadsheet following the procedural example from a literature, or the available spreadsheet (with calculation formula). I ended up with

Hi, I just ran the following logit regression. But can anyone tell me how to calculate how much more likely males (Male=1) could show such symptom than females(Male=0)? I know it must be simple to get once I have the coefficients, but I just don't recall. Thank you very much! Call: glm(formula = Symptoms ~ 1 + Male, family = binomial(link = logit), data = HA) Deviance Residuals:

System: R 2.3.1 on Windows XP machine. I am building a logistic regression model for a sample of 100 cases in dataframe "d", in which there are 3 binary covariates: x1, x2 and x3. ---------------- > summary(d) y x1 x2 x3 0:54 0:50 0:64 0:78 1:46 1:50 1:36 1:22 > fit <- glm(y ~ x1 + x2 + x3, data=d, family=binomial(link=logit)) >

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 R-list, *When comparing two logistic regression models with the anova CHi test, I obtain the following error: (there are no NA's in the time series). How can this be solved such that I can compare two models on the same dataset were different explanatory variables are used? l.KBDI <- glm(zna.arson2 ~ zna.KBDI,family = binomial) l.NDWI <- glm(zna.arson2 ~ zna.NDWI,family

Hello, after calculating a multinomial logit regression on my data, I compared the output to an output retrieved with SPSS 18 (Mac). The coefficients appear to be the same, but the logLik (and therefore fit) values differ widely. Why? The regression in R: set.seed(1234) df <- data.frame( "y"=factor(sample(LETTERS[1:3], 143, repl=T, prob=c(4, 1, 10))), "a"=sample(1:5,

R gurus, I'm working on data analysis for a small project. My response variable is total vines per tree (median = 0, mean = 1.65, min = 0, max = 24). My predictors are two categorical variables (four sites and four species) and one continuous (tree diameter at breast height (DBH)). The main question I'm attempting to answer is whether or not the species identity of a tree has

Dear R help gurus, I have the following problem and I would be delighted if you could help me. >From a large (1500) cohort of patients we have been taking some measurements (ECG measurements, but its not important). The measurements are ordinal in 4 grades (Grade I-IV, grade IV being the most severe form). Every patients has been measured several times (usually once per year). The

Dear all, Last autumn there was some discussion on the list of the warning Warning message: fitted probabilities numerically 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, when fitting binomial GLMs with many 0 and few 1. Parts of replies: "You should be able to tell which coefficients are infinite -- the coefficients and their standard errors will

Dear all, I am facing some problem with how to fit a "Baseline category Logit model" with R. Basically I am considering famous "Alligator" data as discussed by Agresti. This data can also be found here: https://onlinecourses.science.psu.edu/stat504/node/174 (there is also an accompanying R file, however the underlying R code could not load the data properly!!!) Below are

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

This is a follow up to the message I posted 3 days ago about how to estimate mixed ordinal logit models. I hope you don't mind that I am just pasting in the code and comments from an R file for your feedback. Actual estimates are at the end of the post. ### Subject: mixed ordinal logit via "augmented" data setup. ### I've been interested in estimating an ordinal logit model

Hello dear r-gurus! I have a question about the logit-model. I think I have misunderstood something and I'm trying to find a bug from my code or even better from my head. Any help is appreciated. The question is shortly: why I'm not having same coefficients from the logit-regression when using a link-function and an explicite transformation of the dependent. Below some details. I'm

Hi Peter and others, If it helps, I wrote a small function glm.scoretest() for the statmod package on CRAN to compute score tests from glm fits. The score test for adding a covariate, or any set of covariates, can be extracted very neatly from the standard glm output, although you probably already know that. Regards Gordon --------------------------------------------- Professor Gordon K

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" and deviance residuals are not used in the text. For now I'll just provide the students with a simple function to use, but I