similar to: make error in R devel

Hi there I have a post I would like to put on the "95% confidence intercal with glm" thread. Thank-you so much! I am wondering first of all if anyone knows how to calculate confidence intervals for a GLMM? I use the lme4 library. Also, I am wondering how to predict a model mean and confidence intervals for a particular independent variable? For example in the following example:

I can't get confint.glm to work from within a function. Consider the following (using R 1.9.1, Windows 2000): # FIRST: SOMETHING THAT WORKS FROM A COMMAND PROMPT DF <- data.frame(y=.1, N=100) (fit <- glm(y~1, family=binomial, data=DF, weights=DF[,"N"])) Call: glm(formula = y ~ 1, family = binomial, data = DF, weights = DF[, "N"]) Coefficients:

There may be a bug in the anova.glm function. deathstar[32] R R : Copyright 2004, The R Foundation for Statistical Computing Version 2.0.1 (2004-11-15), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project

Full_Name: Soren Feodor Nielsen Version: 2.5.0 OS: linux-gnu Submission from: (NULL) (130.225.103.21) The print-out of xtable in the following example is wrong; instead of yielding the correct ci's for the second model it repeats the ci's from the first model. require(xtable) require(MASS) data(cats) b1<-lm(Hwt~Sex,cats) b2<-lm(Hwt~Sex+Bwt,cats)

It seems that confint.default returns an empty data.frame for objects of class mlm. For example: ``` nobs <- 20 set.seed(1234) # some fake data datf <- data.frame(x1=rnorm(nobs),x2=runif(nobs),y1=rnorm(nobs),y2=rnorm(nobs)) fitm <- lm(cbind(y1,y2) ~ x1 + x2,data=datf) confint(fitm) # returns: 2.5 % 97.5 % ``` I have seen proposed workarounds on stackoverflow and elsewhere, but

Hi Community, I'm trying to reconcile Cook's Distances computed in glm. The following snippet of code shows that the Cook's Distances contours on the plot of Residuals v Leverage do not seem to be the same as the values produced by cooks.distance() or in the Cook's Distance against observation number plot. counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9)

Dear list, I've noticed that influence.measures and cooks.distance gives different results for non-gaussian GLMs. For example, using R-1.9.0 alpha (2003-03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome +

the function confint uses the profiling method of the function of the package MASS confint.glm even after the package has been detached! 1: might this be the intenden behavior? 2. How does the function remember its 'MASS' functionality after detaching the package? R: 1.8.0; Windows 2000 Here is a sample program > set.seed(7882) > x<-rep(c(0,1),c(20,20)) >

Dear R developers, I noticed a bug in the stats4 package, specifically in the confint method applied to ?mle? objects. In particular, when some ?fixed? parameters define the log likelihood, these parameters are stored within the mle object but they are not used by the ?confint" method, which retrieves their value from the global environment (whenever they still exist). Sample code: >

Dear R-helpers, I am using confint() within a function, and I want to turn off the message it prints: x <- rnorm(100) y <- x^1.1+rnorm(100) nlsfit <- nls(y ~ g0*x^g1, start=list(g0=1,g1=1)) > confint(nlsfit) Waiting for profiling to be done... 2.5% 97.5% g0 0.4484198 1.143761 g1 1.0380479 2.370057 I cannot find any way to turn off 'Waiting for. .." I tried

I have the following binary data set: Sex Response 0 1 0 159 162 1 4 37 My commands library(MASS) sib.glm=glm(sib~sex,family=binomial,data=sib.data) summary(sib.glm) The coefficients in the output are Estimate Std. Error z value Pr(>|z|) (Intercept) -3.6826 0.5062 -7.274 3.48e-13

Hi all! I'm getting a model fit from glm() (a binary logistic regression fit, but I don't think that's important) for a formula that contains powers of the explanatory variable up to fourth. So the fit looks something like this (typing into mail; the actual fit code is complicated because it involves step-down and so forth): x_sq <- x * x x_cb <- x * x * x x_qt <- x * x * x

I don't have a copy of Dobson (1990) from which the glm.D93 example is taken in example("glm"), but I'm strongly suspecting that these are made-up data rather than real data; the means of the responses within each treatment are _identical_ (equal to 16 2/3), so two of the parameters are estimated as being zero (within machine tolerance). (At this moment I don't understand

Full_Name: Christian Lajaunie Version: 2.5.1 OS: Fedora fc6 Submission from: (NULL) (193.251.63.39) confint() does not use the appropriate variance term when the design matrix contains a zero column (which of course should not happen). Example: A 10x2 matrix with trivial column 1: > junk <- data.frame(x=rep(0,10), u=factor(sample(c("Y", "N"), 10, replace=T))) The

Greetings all, I was in the process of creating a function to generate profile likelihood confidence intervals for a proportion using a binomial glm. This is a component of a larger function to generate and plot confidence intervals for proportions using the above, along with bootstrap (BCa), Wilson and Exact to visually demonstrate the variation across the methods to some folks. I had initially

Hi! The nobs() method for glm objects always returns the number of cases with non-null weights in the data, which does not correspond to the number of observations for Poisson regression/log-linear models, i.e. when family="poisson" or family="quasipoisson". This sounds dangerous since nobs() is, as the documentation states, primarily aimed at computing the Bayesian

Greetings, I am running glm models for species counts using a poisson link function. Normal summary functions for this provide summary statistics in the form of the deviance, AIC, and p-values for individual predictors. I would like to obtain the p-value for the overall model. So far, I have been using an analysis of deviance table to check a model against the null model with the intercept as

Hi, I have a glm gives summary as follows, Estimate Std. Error z value Pr(>|z|) (Intercept) -2.03693352 1.449574526 -1.405194 0.159963578 A 0.01093048 0.006446256 1.695633 0.089955471 N 0.41060119 0.224860819 1.826024 0.067846690 S -0.20651005 0.067698863 -3.050421 0.002285206 then I use confint(k.glm)

Hi, I am currently doing logistic regression analyses and I am trying to get confidence intervals for my partial logistic regression coefficients. Supposing I am right in assuming that the formula to estimate a 95% CI for a log odds coefficient is the following: log odds - 1.96*SE to log odds + 1.96*SE then I am not getting the right CI. For instance, this is a summary of my model:

Hello R users I like to extract z values for x1 and x2. I know how to extract coefficents using model$coef but I don't know how to extract z values for each of independent variable. I looked around using names(model) but I couldn't find how to extract z values. Any help would be appreciated. Thanks TM ######################################################### >summary(model) Call: