similar to: Tolerances in glm.control

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 R Users, when I use summary(glm) for a logistic regression model with logit as link function I get one column "z value". What kind of test does R use? (I would have expected a t-test). Thanks, Anne -- GMX - Die Kommunikationsplattform im Internet. http://www.gmx.net -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

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 +

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,

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

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

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

Dear R HELP, ABOUT glmmPQL and the anova command. Here is an example of a repeated-measures ANOVA focussing on the way starling masses vary according to (i) roost situation and (ii) time (two time points only). library(nlme);library(MASS)

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

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

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 On 06/16/2011 01:47 PM, Rob James wrote: > Ben, > > Thanks for this. Very helpful and clearly others have tripped over the > same problem > I would have supposed that the solution was to ask lrm (or glm) to use > LR rather than Wald, but I don't see syntax to achieve this. Typically drop1 or dropterm (MASS package) will drop

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:

Hi, I am trying to fit the following model: sr.reg.s4.nore <- survreg(Surv(age_sym4,sym4), as.factor(lifedxm), data=bip.surv) Where age_sym4 is the age that a subject develops clinical thought problems; sym4 is whether they develop clinical thoughts problems (0 or 1); and lifedxm is mother's diagnosis: BIPOLAR, MAJOR DEPRESSION, or CONTROL. I am interested in whether or not

Dear list, I am from an engineering background, accustomed to work with tolerances. For example, I have measured Q = 0.15 +- 0.01 m^3/s H = 10 +- 0.1 m and now I want to calculate P = 5 * Q * H and get a value with a tolerance +- What is the elegant way of doing this in R? Thank you, Jan

Full_Name: Daniel R Jeske Version: 1.8.1 OS: Windows 2000 Submission from: (NULL) (138.23.228.79) We have just noticed that when you use glm() it seems the logical output 'converged' is always TRUE. The same data set that shows FALSE in version 1.7.1 shows TRUE in 1.8.1. And I know that FALSE is the correct answer...so it seems like we cannot trust the 'converged' flag for

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

Dear Colleagues I have been fitting some multinomial logistic regression models using R (version 1.6.1 on a linux box) and Stata 7. Although the vast majority of the parameter estimates and standard errors I get from R are the same as those from Stata (given rounding errors and so on), there are a few estimates for the same model which are quite different. I would be most grateful if

all/any coerce their arguments to logical (if possible). I've added a warning in R-devel if coercion is from something other than integer. This arose because it is easy to make a slip and write all(X) > 0 rather than all(X > 0): thanks to Bill Dunlap for bringing that to my attention. However, it has been useful in detecting quite a few other things: - indices which had been made