similar to: Which function for this?

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

I've just been running my first ever regression and I'm using R for an lmer. I've created two models, a null one (part ~ 1 + (1 | id) + (1 | word)) and another with a predictor I want to test (part ~ 1 + conf + (1 | id) + (1 | word)). I've compared them both and the model with a predictor is significantly better, but I can't see from the data which direction the prediction goes

I am trying to test goodness of fit for my legalistic regression using several options as shown below.  Hosmer-Lemeshow test (whose function I borrowed from a previous post), Hosmer–le Cessie omnibus lack of fit test (also borrowed from a previous post), Pearson chi-square test, and deviance test.  All the tests, except the deviance tests, produced p-values well above 0.05.  Would anyone please

Dear all, I try to consider overdispersion in a lmer model. But using family=quasibinomial rather than family=binomial seems to change the fit but not the result of an anova test. In addition if we specify test="F" as it is recomanded for glm using quasibinomial, the test remains a Chisq test. Are all tests scaled for dispersion, or none? Why is there a difference between glm and lmer

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 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

I am working on a two stage threshold quantile regression model in R, and my aim is to estimate the threshold of the reduced-form equation (call it rhohat), and the threshold of the structural equation (call it qhat), in two stages. On the first stage, i estimate rhohat by quantile regression and obtain the fitted values. I use these fitted values to estimate qhat on the second stage. The code is

Dear Prof. Thomas Yee I$B!G(Bm very interested in your R program VGAM. I tried below your data: # Nonparametric proportional odds model data(pneumo)pneumo = transform(pneumo, let=log(exposure.time))vgam(cbind(normal,mild,severe) ~ s(let), cumulative(par=TRUE), pneumo) However, the results by Version of VGAM are different; ----------The result by Version 0.7-7

I am running lrm() with a single factor. I then run anova() on the fitted model to obtain a p-value associated with having that factor in the model. I am noticing that the "Model L.R." in the lrm results is almost the same as the "Chi-Square" in the anova results, but not quite; the latter value is always slightly smaller. anova() calculates the p-value based on

Dear R-help users, I am trying to analyze some visual transect data of organisms to generate a habitat distribution model. Once organisms are sighted, they are followed as point data is collected at a given time interval. Because of the autocorrelation among these "follows," I wish to utilize a GAM-GEE approach similar to that of Pirotta et al. 2011, using packages 'yags' and

Hi, I am trying to carry out a multinomial regression using the cumlogit link function. I have tried using the VGAM package, and have gotten some results... fit1 <- vgam(Y ~ X1 + X2 + X3 + X4, cumulative(link=logit,intercept.apply=FALSE,parallel=TRUE), data = data1 ) The problem arrises when I try to get the information out of the fitted object. I can

I have a great dataset like this: name ? ? ?colour ? ... ... ... jerry ? ? ? ? red pippo ? ? ? ?red tom ? ? ? ? ?red tom ? ? ? ?yellow tom ? ? ? ?green jessie ? ? orange jessie ? ? ?red bill ? ? ? ? ?yellow kate ? ? ? red henry ? ? ?green .. .. I want to find out, in this great dataset, if (the name of) a person is repeated (and which colour are associated).? My output has to be

Hi i would like to use some graphs or tables to explore the data and make some sensible guesses of what to expect to see in a glm model to assess if toxin concentration and sex have a relationship with the kill rate of rats. But i cant seem to work it out as i have two predictor variables~help?Thanks.:) Here's my data. >

Dear R_help list members!   I’m studding forest carnivores and I have data on resting site selection and use!   I’m trying to model the probability that a forest carnivore might have in be located in a tree hollow (1) (the main resting site selected) rather than elsewhere (0) (other resting site types, dens, nests, etc.). The model should be related with several variables such as for instance:  

I am fitting a logistic model to binary data. The response variable is a factor (0 or 1) and all predictors are continuous variables. The main predictor is LT (I expect a logistic relation between LT and the probability of being mature) and the other are variables I expect to modify this relation. I want to test if all predictors contribute significantly for the fit or not I fit the full

Greetings, I have stumbled across some unexpected behavior (potential a bug) in, what I suspect to be R's (2.6.2 on Ubuntu Linux) t.test function; then again the problem may exist in my code. I have shutdown R and started it back up, re-run the code and re-experienced the error. I have searched on Google for the abnormal termination error message "(stderr < 10 * .Machine$double.eps *

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

Hi I have 1800 response variables to regress on two factors (latitude and age), what is the script to run all response variables at once instead of writing 1800 models? Thanks R. ________________________________________ L?hett?j?: r-help-bounces at r-project.org [r-help-bounces at r-project.org] k&#228;ytt&#228;j&#228;n Dennis Murphy [djmuser at gmail.com] puolesta L?hetetty: 25.

Dear all, I've got some questions, probably due to misunderstandings on my behalf, related to fitting overdispersed binomial data using glm(). 1. I can't seem to get the correct p-values from anova.glm() for the F-tests when supplying the dispersion argument and having fitted the model using family=quasibinomial. Actually the p-values for the F-tests seems identical to the p-values for

Dear list, I hope the topic is of sufficient interest, because it is not R-related. I have N=100 yes/no-responses from a psychophysics paradigm (say Y Yes and 100-Y No-Responses). I want to see whether these yes-no-responses are in line with a model predicting a certain amount p of yes-responses. Standard procedure would be a one-sample binomial test for the observed proportion, chi?(1 df) =