similar to: logistic regression or not?

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Hi, I was wondering why anova() and drop1() give different tail probabilities for F tests. I guess overdispersion is calculated differently in the following example, but why? Thanks for any advice, Tom For example: > x<-c(2,3,4,5,6) > y<-c(0,1,0,0,1) > b1<-glm(y~x,binomial) > b2<-glm(y~1,binomial) > drop1(b1,test="F") Single term deletions Model: y ~

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

Hello R-Users,   I have one binary dependent variable and a set of independent variables (glm(formula,…,family=”binomial”) ) and I am using the function stepAIC (“MASS”) for choosing an optimal model. However I am not sure if stepAIC considers significance properties like Likelihood ratio test and Wald test (see example below).     > y <- rbinom(30,1,0.4) > x1 <- rnorm(30) > x2

Hi, I have a dichotomous variable (Q1) whose answers are Yes or No. Also I have 2 categorical explanatory variables (V1 and V2) with two levels each. I used logistic regression to determine whether there is an effect of V1, V2 or an interaction between them. I used the R and SAS, just for the conference. It happens that there is disagreement about the effect of the explanatory variables

I have a question about some strange results I get when using lmer to build a logistic mixed effects model. I have a data set of about 30k points, and I'm trying to do backwards selection to reduce the number of fixed effects in my model. I've got 3 crossed random effects and about 20 or so fixed effects. At a certain point, I get a model (m17) where the fixed effects are like this

Hi, I have a dataset that has 2 groups of samples. For each sample, then response measured is the number of success (no.success) obatined with the number of trials (no.trials). So a porportion of success (prpop.success) can be computed as no.success/no.trials. Now the objective is to test if there is a statistical significant difference in the proportion of success between the 2 groups of

Dear all, I am comparing logistic regression models to evaluate if one predictor explains additional variance that is not yet explained by another predictor. As far as I understand Baron and Li describe how to do this, but my question is now: how do I report this in an article? Can anyone recommend a particular article that shows a concrete example of how the results from te following simple

Hi, I am performing glm with binomial family and my data show slight overdispersion (HF<1.5). Nevertheless, in order to take into account for this heterogeneity though weak, I use F-test rather than Chi-square (Krackow & Tkadlec, 2001). But surprisingly, outputs of this two tests are exactly similar. What is the reason and how can I scale the output by overdispersion ?? Thank you,

I have a data with binary response variable, repcnd (pregnant or not) and one predictor continuous variable, svl (body size) as shown below. I did Hosmer-Lemeshow test as a goodness of fit (as suggested by a kind “R-helper” previously). To test whether the predictor (svl, or body size) has significant effect on predicting whether or not a female snake is pregnant, I used the differences between

Hi, I have a data set with 999 observations, for each of them I have data on four variables: site, colony, gender (quite a few NA values), and cohort. This is how the data set looks like: > str(dispersal) 'data.frame': 999 obs. of 4 variables: $ site : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 2 2 ... $ gender: Factor w/ 2 levels "0","1":

Dear R-help, I am trying to make logistic regression analysis using the R function "glm", with the parameter family set to binomial, in order to use a logistic regression model. I have 70 samples. The dependent variables has two levels (0 and 1) and one of the independent variables has too two levels (0 and 1). The variables associate in the way shown in the table:

Dear R-list members, I have been a user of SPSS for a few years and quite new to R. I read the documentation and tried samples but I have some problems to obtain results for a logistic regression under R. The following SPSS script LOGISTIC REGRESSION vir /METHOD = FSTEP(LR) d007 d008 d009 d010 d011 d012 d013 d014 d015 d016 d017 d018 d069 d072 d073 /SAVE = PRED COOK SRESID

Hi I am trying to do a logistic regression on a small data file yet when i get up to plotting the first set of graphs instead of 4 I only get one graph, and some error messages. Yet it still looks like the program is doing something due to the "blue wheel" of the mouse. Below is the script copied from R and where I get up to before the message occurs. The data set is based on how

Hi, I'm using glm function to do logistic regression and now I want to know if it exists a kind of R-squared with this function in order to check the model. Thank you.

Dear list, I want to perform a logistic regression analysis with multiple categorical predictors (i.e., a logit) on some data where there is a very definite relationship between one predicator and the response/independent variable. The problem I have is that in such a case the p value goes very high (while I as a naive newbie would expect it to crash towards 0). I'll illustrate my problem

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)) >

I have a data set to be analyzed using to binary logistic regression. The data set is iin grouped form. My question is: how I can compute Hosmer-Lemeshow test and measures like sensitivity and specificity? Any suggestion will be greatly appreciated. Thank you Endy [[alternative HTML version deleted]]

Hi, I tried to do a nested Anova with the attached Data. My response variable is "survivors" and I would like to know the effect of (insect-egg clutch) "size", "position" (of clutch on twig) and "clone" (/plant genotype) on the survival of eggs (due to predation). Each plant was provided with three different sizes of clutches (45,15,5) and had

Dear R-help-list, I have a problem in which the explanatory variables are categorical, the response variable is a proportion, and experiment contains technical replicates (pseudoreplicates) as well as biological replicated. I am new to both generalized linear models and mixed- effects models and would greatly appreciate the advice of experienced analysts in this matter. I analyzed the