similar to: What kind of test in summary(glm)?

I have a very simple problem. When using glm to fit binary logistic regression model, sometimes I receive the following warning: Warning messages: 1: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start,

As suggested in my earlier message, I have a large population of independent variables and a binary dependent outcome. It is expected that only a few of the independent variables actually contribute to the outcome, and I'd like to find those. If it wasn't already obvious, I am *not* a statistician. Not even close. :-) Statistician colleagues have suggested that I use logistic

I just ran example(glm) and happened to notice that models based on the Gamma distribution gives a t test, while the Poisson models give a z test. Why? Both are b/s.e., aren't they? I can't find documentation supporting the claim that the distribution is more like t in one case than another, except in the Gaussian case (where it really is t). Aren't all of the others approximations

Hello all, I've come across an online posting http://www.biostat.wustl.edu/archives/html/s-news/2001-10/msg00119.html that described how to get confidence intervals for predicted values from predict.glm. These instructions were meant for S-Plus. Yet, it generally seems to work with R too, but I am encountering some problems. I am explaining my procedure in the following and would be most

I have tightened the tolerances in glm.control in R-devel (aka 1.8.0 Under Development) from epsilon = 1e-4 to 1e-8, and increases maxit from 10 to 25. Normally the effect is to do one more iteration and get more accurate results. However, in cases of partial separation several more iterations will be done and it will be clearer from the results which parameters are converging and which are

Dear all, Is there any function for calculating confidence limits for coefficients in an lm() object? I know of the confint() function in the MASS library working very well on my binomial GLMs and I have tried it (using glm () , family=gaussian) but it gives NAs according to below. Does the confint() function not accept gaussian GLMs? Could there be convergence problems in the GLM? Note the

Hi, I am puzzled by the relationship between the p-values asociated with the coefficients of a univariate logistic regression involving categorical variables and the p-value I get from Fisher's exact test of the associated 2 x 2 contingency table. (1) The 2-sided p-value for the table is ~ 0.0015, whereas the p-value for the independent is 0.101 and the p-value for the intercept is

Hallo all, I have the following glm model: f1 <- as.formula(paste("factor(y.fondi)~", "flgsess + segmeta2 + udm + zona.geo + ultimo.prod.", "+flg.a2 + flg.d.na2 + flg.v2 + flg.cc2", " +(flg.a1 + flg.d.na1 + flg.v1 + flg.cc1)^2", " + flg.a2:flg.d.na2 + flg.a2:flg.v2 +

Dear members, here is my trouble: My data consists of counts of trapped insects in different attractive traps. I usually use GLMs with a poisson error distribution to find out the differences between my traitments (and to look at other factor effects). But for some dataset where one traitment contains only zeros, GLM with poisson family fail to find any difference between this particular traitment

Dear Masters, first I'd like to wish u all a great 2011 and happy holydays by now, second (here it come the boring stuff) I have a question to which I hope u would answer: I run a logistic regression by glm(), on the following data type (y1=1,x1=x1); (y2=0,x2=x2);......(yn=0,xn=xn), where the response (y) is abinary outcome on 0,1 amd x is any explanatory variable (continuous or not)

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 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

With ggplot2, I can plot the glm stat_smooth for binomial data when the response is binary or a two-level factor as follows: data("Donner", package="vcdExtra") ggplot(Donner, aes(age, survived)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2) But how can I

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in

In the example below, from http://www.ling.upenn.edu/~joseff/rstudy/summer2010_ggplot2_intro.html I'd like to make (a) the fitted line thicker and (b) change the background fill color for the confidence envelope around each fitted line to a low-alpha transparent version of the same color used for the separate fitted lines for GENDER, rather than grey for both. How can I do this?

Something for the wish list (not really a bug): I was reminded of what I see as a problem with summary.glm last week when some of my students fell into a trap in a homework exercise, defining a logit model in which coefficients were aliased. When this happens in lm, summary.lm prints a message ["Coefficients: (1 not defined because of singularities)"], but summary.glm is silent. In

If my aim is to select a good subset of parameters for my final logit model built using glm(). What is the best way to cross-validate the results so that they are reliable? Let's say that I have a large dataset of 1000's of observations. I split this data into two groups, one that I use for training and another for validation. First I use the training set to build a model, and the the

Dear list members, I was reminded of what I see as a problem with summary.glm this week when some of my students fell into a trap in a homework exercise, defining a logit model in which coefficients were aliased. When this happens in lm, summary.lm prints a message ["Coefficients: (1 not defined because of singularities)"], but summary.glm is silent. In both instances, the print

Hello all, I found a weird result of the GLM function that seems to be a bug. The code: a=c(rep(1,8),rep(2,8)) b=c(rep(0,8),rep(3,8)) cbind(a,b) model=glm(b~a, family=poisson) summary(model) generates a dataset with two groups. One group consists entirely of zeros, the other of 3's (as happened in a dataset I’m analyzing right now). Since they are count data, one should apply a

Dear R-list. I have done a logistic glm using Age as explanatory variable for some allergic event. #the model model2d<-glm(formula=AEorSAEInfecBac~Age,family=binomial("logit"),data=emrisk) #predictions for age 30 and 60 preds<-predict(model2d,data.frame(Age=c(30,60)),se.fit=TRUE) # prediction interval