similar to: logistic regression without intercept

Dear R-list members, I would like to pose a question about the use and results of the glm() function for logistic regression calculations. The question is based on an example provided on p. 229 in P. Dalgaard, Introductory Statistics with R, 2nd. edition, Springer, 2008. By means of this example, I was trying to practice the different ways of entering data in glm(). In his book, Dalgaard

Hello! When I am analyzing proportion data, I usually apply logistic regression using a glm model with binomial family. For example: m <- glm( cbind("not realized", "realized") ~ v1 + v2 , family="binomial") However, sometimes I don't have the number of cases (realized, not realized), but only the proportion and thus cannot compute the binomial model. I just

Greetings. Here is sample code, with some comments. It shows how I can simulate data and estimate glm with binomial family when there is no individual level random error, but when I add random error into the linear predictor, I have a difficult time getting reasonable estimates of the model parameters or the variance component. There are no clusters here, just individual level responses, so

Dear All, I am learning the ropes about logistic regression in R. I found some interesting examples http://bit.ly/Vq4GgX http://bit.ly/W9fUTg http://bit.ly/UfK73e but I am a bit lost. I have several questions. 1) For instance, what is the difference between glm.out = glm(response ~ poverty + gender, family=binomial(logit), data=mydata) and glm.out = glm(response ~ poverty * gender,

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

Hi all, Could anybody tell me what happened to my logistic regression in R? mylog=glm(mytraindata$V1 ~ ., data=mytraindata, family=binomial("logit")) It generated the following error message: Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : factor 'state1' has new level(s) AP Thank you!

I have two questions: 1. If I want to do a binomial logit, how to handle the categorical response variable? Data for the response variables are not numerical, but text. 2. What if I want to do a multinomial logit, still with categorical response variable? The variable has 5 non-numerical response levels, I have to do it with a multinomial logit. Any input is highly appreciated! Thanks! Ed

Dear friends, I'm doing a simulation on logistic regression model, but the programs can't work well,please help me to correct it and give some suggestions. My programs: data<-matrix(rnorm(400),ncol=8) #sample size is 50 data<-data.frame(data) names(data)<-c(paste("x",1:8,sep="")) #8 independent variables,x1-x8; #logistic regression model is

Hi all, In my continuous transition of GLIM to R I try to make a glm with binomial errors. The data file have 3 vectors: h -> the factor that is ajusted (have 3 levels) d -> number of animais alive (the response) n -> total number of animals To test proportion of alive, make d/n. In GLIM: $yvar d$ $error binomial n$ $fit +h$ scale deviance = 25.730 (change = -9.138) at cycle 4

You can fit a linear probability model with glm and a bit of arm twisting. First, make your own copy of the binomial function: > dump('binomial', file='mybinom.R') Edit it to change the function name to "mybinom" (or anything else you like), and to add 'identity' to the list of okLinks. Source the file back in, and use mybiom('identity') to fit

Hi everyone, I'm sorry if this turns out to be more a statistical question than one specifically about R - but would greatly appreciate your advice anyway. I've been using a logistic regression model to look at the relationship between a binary outcome (say, the odds of picking n white balls from a bag containing m balls in total) and a variety of other binary parameters:

Dear friends, I hope this email finds you all well. This is the dataset I am working with: dput(random_mod12_data2) structure(list(Index = c(1L, 5L, 11L, 3L, 2L, 8L, 9L, 4L), x = c(5, 13, 25, 9, 7, 19, 21, 11), n = c(500, 500, 500, 500, 500, 500, 500, 500), r = c(100, 211, 391, 147, 122, 310, 343, 176), ratio = c(0.2, 0.422, 0.782, 0.294, 0.244, 0.62, 0.686, 0.352)), row.names = c(NA, -8L),

Hi all I have another GLIM question. I have been using R as well as Genstat (version 6) in order to fit GLIM models to the data (displayed below). The same models are fitted but the answers supplied by the two packages are not the same. Why? Can anyone help? A discription of the data and the type of model/s fitted can be found below. Regards Allan The

Dear all, I have a question about the glm. When the events of an observation is 0, the logit function on it is Inf. I wonder how the glm solve it. An example: Treat Events Trials A 0 50 B 7 50 C 10 50 D 15 50 E 17 50 Program: treat <- factor(c("A", "B", "C", "D", "E")) events <- c(0, 7, 10, 15,

Hi all, Where f(x) is a logistic function, I have data that follow: g(x) = f(x)*.5 + .5 How would you suggest I modify the standard glm(..., family='binomial') function to fit this? Here's an example of a clearly ill-advised attempt to simply use the standard glm(..., family='binomial') approach: ######## # First generate some data ######## #define the scale and location of

Dear R developers, I have noticed a discrepancy between the coefficients returned by R's glm() for logistic regression and SAS's PROC LOGISTIC. I am using dist = binomial and link = logit for both R and SAS. I believe R uses IRLS whereas SAS uses Fisher's scoring, but the difference is something like 100 SE on the intercept. What accounts for such a huge difference? Thank you for

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.

Hello everyone, I tried to understand the relationship between temperature and the death of an organism by using logistic regression. glm(formula = Death ~ Temperature, family = binomial(link = "logit"), data = mydata) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -87.9161 7.7987 -11.27 <2e-16 *** Temperature

Hello, Is there an example out there that shows how to treat each of the predictor variable types when doing logistic regression in R? Something like this: glm(y~x1+x2+x3+x4, data=mydata, family=binomial(link="logit"), na.action=na.pass) I'm drawing mostly from: http://www.ats.ucla.edu/stat/r/dae/logit.htm ...but there are only two types of variable in the example given. I'm

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