similar to: glm gives t test sometimes, z test others. Why?

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 guys im having a problem getting R to numerically integrate for some function, say f(bhat)*optimize(G(bhat)), over bhat. Where id like to integrate this over some finite range, so that here as we integrate over bhat optimize would return a different optimum. For instance consider this simple example for which I cannot get R to return the desired result: f <- function(bhat) exp(bhat) g

In glm, is there a way to specify the family to be exponential, or Gamma but with alpha=1? This is required by Dobson Exercise 4.3d, page 48. But without specifying alpha=1, the asnwer is the same as in the book. If reply to the list, please cc me a copy. Thanks. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hello Everyone, I'm currently learning about quantile regressions. I've been using an optimizer to compare with the rq() command for quantile regression. When I run the code, the results show that my coefficients are consistent with rq(), but the intercept term can vary by a lot. I don't think my optimizer code is wrong and suspects it has something to do with the starting

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:

Hi! I'm trying to install package Bhat on a Win machine, had a problem and figured out a fix, but would like to report the problem and make sure the fix is correct. This is what I do: 1. Download Bhat_0.9-07.tar.gz 2. Uncompress it and compress it back to Bhat_0.9-07.zip 3. Install from the R windows gui (that apparentely requires the zip compression and cannot deal with the tar.gz) But

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 +

xen_play_dead calls cpu_bringup() which looks weird, because xen_play_dead() is invoked in the cpu down path, whereas cpu_bringup() (as the name suggests) is useful in the cpu bringup path. Getting rid of xen_play_dead()'s dependency on cpu_bringup() helps in hooking on to the generic SMP booting framework. Also remove the extra call to preempt_enable() added by commit 41bd956 (xen/smp: Fix

xen_play_dead calls cpu_bringup() which looks weird, because xen_play_dead() is invoked in the cpu down path, whereas cpu_bringup() (as the name suggests) is useful in the cpu bringup path. Getting rid of xen_play_dead()'s dependency on cpu_bringup() helps in hooking on to the generic SMP booting framework. Also remove the extra call to preempt_enable() added by commit 41bd956 (xen/smp: Fix

I see. Is there a pre-existing way to do this in LLVM? Cheers, ~Siddharth. On Thu, 17 Aug 2017 at 02:12 Craig Topper <craig.topper at gmail.com> wrote: > ConstantExprs are immutable, they can't be changed once they are created. > And a ConstantExpr can reference other ConstantExprs. So replacing all uses > of a Value in a ConstantExpr would require creating a new immutable

Dear list, I'm trying to learn how to use nlme to be able to fit ad analyse mixed-model logistic regressions. In order to keep things simple, I started by the simplest possible model : a one (fixed-effect ...) continuous variable. This problem is, of course, solved by glm, but I wanted to look at a "hand-made" nls fit, in order to be able to "generalize" to nlme

Hi Folks, I'm seeking confirmation of something which is probably true but which I have not managed to find in the documentation. I have a binary response y={0.1} and a variable x and have fitted a probit response to the data with f <- glm( y~x, family=binomial(link=probit) ) and then, with a specified set of x-value X I have used the predict.glm function as p <- predict( f, X,

Where can I download Bhat package? I need to use plkhci function in this package, which estimates confidence interval using likelihood ratio. Thanks. Hongsheng (Hank) Liao, PhD. Lab Manager Center for Quantitative Fisheries Ecology Old Dominion University 757-683-4571 [[alternative HTML version deleted]]

Whoops, sorry, I meant "value->replaceAllUsesWith". Should I create a new post with an updated title? Thanks Siddharth On Thu 17 Aug, 2017, 01:05 Tim Northover <t.p.northover at gmail.com> wrote: > On 16 August 2017 at 15:39, (IIIT) Siddharth Bhat via llvm-dev > <llvm-dev at lists.llvm.org> wrote: > > From what I have observed, using

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

Hi userRs! I am trying to fit some GLM-poisson and neg.binomial. The neg. Binomial model is to account for over-dispersion. When I fit the poisson model i get: (Dispersion parameter for poisson family taken to be 1) However, if I estimate the dispersion coefficient by means of: sum(residuals(fit,type="pearson")^2)/fit$df.res I obtained 2.4. This is theory means over-dispersion since

Dear all, I've tried to solve the Es. 12, cap 4 of "Introduction to GLM" by Annette Dobson. It's about the relationship between survival time of leukemia patients and blood cell count. I tried to fit a model with exponential distribution, first by glm (family gamma and then dispersion parameter fixed to 1) and then with survreg. They gave me the same point estimates but the

I'm confused as to the trustworthiness of the dispersion parameters reported by glm. Any help or advice would be greatly appreciated. Context: I'm interested in using a fitted GLM to make some predictions. Along with the predicted values, I'd also like to have estimates of variance for each of those predictions. For a Gamma-family model, I believe this can be done as Var[y] =

I have been implementing a proposal of Jim Lindsey for glm(.) to return AIC values, and print.glm(.) and print.summary.glm(.) printing them.... however: >>>>> "Jim" == Jim Lindsey <jlindsey@luc.ac.be> writes: Jim> The problem still remains of getting the correct AIC when the user Jim> wants the scale parameter to be fixed. (The calculation should

Dear list, This is a question about overdispersion and the ML estimates of the parameters returned by the glm.poisson.disp (L. Scrucca) and glm.nb (Venables and Ripley) functions. Both appear to assume a negative binomial distribution for the response variable. Paul and Banerjee (1998) developed C(alpha) tests for "interaction and main effects, in an unbalanced two-way layout of counts