similar to: GLM plotting estimated responses

In summary.glm I'm trying to get a better feel for the z output. The following lines can be found in the function 1 if (p > 0) { 2 p1 <- 1:p 3 Qr <- object$qr 4 coef.p <- object$coefficients[Qr$pivot[p1]] 5 covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) 6 dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))

I have a vector that has 1,974 elements and each element is one of the following (B, F, N, Y). How do I recreate that vector accept in the place of N put 0 and in the place of B, F or Y put a 1? Thanks, Jacob [[alternative HTML version deleted]]

Hi, I'm running gam (mgcv version 1.3-29) and glm (logit) (stats R 2.61) on the same models/data, and I got error messages for the gam() model and warnings for the glm() model. R-help suggested that the glm() warning messages are due to the model perfectly predicting binary output. Perhaps the model overfits the data? I inspected my data and it was not immediately obvious to me (though I

Hello, I was trying a glm fitting (as shown below) and I got a warning and a fitted residual deviance larger than the null deviance. Is this the expected behavor of glm? I would expect that even though the warning might be warranted I should not get worse fitting with an additional covariate in the model. Could anyone tell me what I'm missing? I get the same results in both R2.5.1 on windows

Dear All, I know this may be a trivial question. In the past I have used glm to make logistic regressions on data. The output creates an object with the results of the logistic regression. This object can then be used to make predictions. Great. I have a different problem. I need to make predictions from a logistic regression taken from a paper. Thus I need to (by hand) enter the reported odds

Hello, Are there any packages/functions available for testing the assumptions underlying assumptions for a good GLM model? Like linktest in STATA and smilar. If not, could somebody please describe their work process when they check the validity of a logit/probit model? Regards, Jay

Hi, my dependent variable is a proportion ("prob.bind"), and the independent variables are factors for group membership ("group") and a covariate ("capacity"). I am interested in the effects of group, capacity, and their interaction. Each subject is observed on all (4) levels of capacity (I use capacity as a covariate because the effect of this variable is normatively

Dear all, A question related to the following has been asked on R-help before, but I could not find any answer to it. Input will be much appreciated. I got an unexpected sign of the "slope" parameter associated with a covariate (diam) using zeroinfl(). It led me to compare the estimates given by zeroinfl() and hurdle(): The (significant) negative estimate here is surprising, given

Until a weeks ago I used stata for everything. Now I'm learning R and trying to move. But, in this stage I'm testing R trying to do the same things than I used to do in stata whit the same outputs. I have a problem with the logit, applying weights. in stata I have this output . svy: logit bach job2 mujer i.egp4 programa delay mdeo i.str evprivate (running logit on estimation sample)

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

glm(A~B+C+D+E+F,family = binomial(link = "logit"),data=tre,na.action=na.omit) Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels however, glm(A~B+C+D+E,family = binomial(link = "logit"),data=tre,na.action=na.omit) runs fine glm(A~B+C+D+F,family = binomial(link =

Hi I am trying to make glm() work to analyze a toy logit system. I have a dataframe with x and y independent variables. I have L=1+x-y (ie coefficients 1,1,-1) then if I have a logit relation with L=log(p/(1-p)), p=1/(1+exp(L)). If I interpret "p" as the probability of success in a Bernouilli trial, and I can observe the result (0 for "no", 1 for

Hi, I had a logit regression, but don't really know how to handle the "Warning message: non-integer #successes in a binomial glm! in: eval(expr, envir, enclos)" problem. I had the same logit regression without weights and it worked out without the warning, but I figured it makes more sense to add the weights. The weights sum up to one. Could anyone give me some hint? Thanks a lot!

Hi, I am using glm(). I'd like to know what the command means. For example, glm(family=binomial(link=logit)) means logit model. Then, glm(family=gaussian(link=logit)), does this mean? Thank you in advance. Kenji. A Analysis Manager SPI - Strategy, Productivity, Insight., Japan

Dear all, I am using glm function in order to estimate a logit model i.e. glm(Y ~ data[,2] + data[,3], family = binomial(link = "logit")). I also created a function that estimates logit model and I would like it to compare it with the glm function. So, does anyone know what optimizer or optimization method glm uses in order to derive the result? Thank you Dimitris -- View this

Hello everyone, I am currently teaching an intermediate stats. course at UCSD Extension using R. We are using Venables and Ripley as the primary text for the course, with Freund & Wilson's Statistical Methods as a secondary reference. I recently gave a homework assignment on logistic regression, and I had a question about glm. Let n be the number of trials, p be the estimated

Hello list, Is there any mean to fit models for ordinal response other than multinomial polytomous ("multinom" from nnet ) and cumulative logit ("polr" from MASS)? I am particularly interested in continuation-ratio model and adjacent-category logit model. It is for the sake of epidemiology in wild-living populations! Many thanks, Emmanuelle Fromont

I am learning R via a textbook that performs analysis with SPSS and SAS. In trying to reproduce the results for an ordinal logit model, I get very similar point estimates for my cut-off points, but the parameters for the covariate q60 do not match. The estimate for q51 also matches. Is this because I need to change a base case for the ordered covariate q60? Can this be done in or is it always the

Dear UseRs: I have simulated data from a zero-inflated Poisson model, and would like to use a package like zicounts to test my code of fitting the model. My question is: can I use zicounts directly with the following simulated data? Create a sample of n=1000 observations from a ZIP model with no intercept and a single covariate x_{i} which is N(0,1). The logit part is logit(p_{i})=x_{i}*beta

I am creating a model attempting to predict the probability someone will reoffend after being caught for a crime. There are seven total inputs and I planned on using a logistic regression. I started with a null deviance of 182.91 and ended up with a residual deviance of 83.40 after accounting for different interactions and such. However, I realized after that my code is different from that in