similar to: Poor family objects error messages

Just a quick point which may be easy to correct. Whilst typing the wrong thing into R 2.2.1, I noticed the following error messages, which seem to have some stray quotation marks and commas in the list of available families. Perhaps they have been corrected in the latest version (sorry, I don't want to upgrade yet, but it should be easy to check)? > glm(1 ~ 2,

What is the current state of the R-art for ordered probit models, and more esoterically is there any available R strategy for ordered cauchit models, i.e. ordered multinomial alternatives with a cauchy link function. MCMC is an option, obviously, but for a univariate latent variable model this seems to be overkill... standard mle methods should be preferable. (??) Googling reveals that spss

Full_Name: Henric Nilsson Version: 2.3.0 Patched (2006-05-09 r38014) OS: Windows 2000 SP4 Submission from: (NULL) (83.253.9.137) When supplying an unavailable link to `quasibinomial', the error message looks strange. E.g. > quasibinomial("x") Error in quasibinomial("x") : 'x' link not available for quasibinomial family, available links are "logit",

this may be a better question for r-devel, but ... Is there a particular reason (and if so, what is it) that the inverse link is not in the list of allowable link functions for the binomial family? I initially thought this might have something to do with the properties of canonical vs non-canonical link functions, but since other link functions (probit, cloglog, cauchit, log) are allowed, I

Dear all, A am trying to fit a generalized linear mixed effects model with a binomial link function, my response data is binary, using the lme4 R package, for the glmer model but with the cauchit link function (CDF of Cauchy distribution), under the package this has not yet been coded and was wondering if anyone knew a way in which I could incorporate this link function into the code. Thankyou

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and this generates an error for me: >

Hi, I would like apply different link functions from Aranda-Ordaz (1981) family to large binary dataset (n = 2000). The existing links in glm for binomial data (logit, probit, cloglog) are not adequate for my data, and I need to test some other transformations. Is it possible to do this in R? And how? Thank you for your help, /Sharon

Thank you for such a quick reply, here are some points that I think might have been missed: > I would state the question the other way : why are NAs integer indices allowed? > In my experience, they are sometimes useful but they often delay the detection of bugs. However, due to backward compatibility, this feature cannot be removed. Adding this feature to character indices would worsen the

Dear all, I am working on modifying the R working matrix to commodate some other correlations that not included in the package. I am having problem to locate where the R matrix are defined for regular matrices, i.e. independence, exchangeable, AR and unstructure. it might have something within .C("Cgee",but don't understand it well enough to know. Can you anyone help? /*gee source

Hi Folks, I am interested in extending the repertoire of link functions in glm(Y~X, family=binomial(link=...)) to include a "tan" link: eta = (4/pi)*tan(mu) i.e. this link bears the same relation to the Cauchy distribution as the probit link bears to the Gaussian. I'm interested in sage advice about this from people who know their way aroung glm. >From the surface, it looks

Hello, I'm not a Stata user so I'm trying to reproduce Stata results that are given to me in R. I would like to use a GLM with a complementary log-log function. The stata code I have is: glm c IndA fia, family(binomial s) link(cloglog) offset(offset) The R code is: glmt <- glm(data=dataset, c ~ IndA + fia, offset = offset, family = binomial(link = cloglog)) Which yields

This is to announce the new R-package ?ordinal? that implements cumulative link (mixed) models for ordinal (ordered categorical) data (http://www.cran.r-project.org/package=ordinal/). The main features are: - scale (multiplicative) as well as location (additive) effects - nominal effects for a subset of the predictors (denoted partial proportional odds when the link is the logistic) - structured

This is to announce the new R-package ?ordinal? that implements cumulative link (mixed) models for ordinal (ordered categorical) data (http://www.cran.r-project.org/package=ordinal/). The main features are: - scale (multiplicative) as well as location (additive) effects - nominal effects for a subset of the predictors (denoted partial proportional odds when the link is the logistic) - structured

Karolis wrote: > Hello, > I have stumbled upon a few cases where the behaviour of naming and subsetting in matrices seems unintuitive. > All those look related so wanted to put everything in one message. > 1. Why row/col selection by names with NAs is not allowed? > x <- setNames(1:10, letters[1:10]) > X <- matrix(x, nrow=2, dimnames = list(letters[1:2],

I am trying to figure out how to apply a loglog link to a binomial model (dichotomous response variable with far more zeros than ones). I am aware that there are several relevant posts on this list, but I am afraid I need a little more help. The two suggested approaches seem to be: 1) modify the make.link function in GLM, or 2) use the loglog or cloglog functions in the VGAM package.

Help needed with lm function: Dear R's, Could anyone tell me how to replace the link function (probit logit, loglog etc.) in lm with an abitrary user-defined function? The task is to perform ML Estimation of betas for a dichotome target variable. Maybe there is already a package for this (I did not find one). Any hints or a code excerpt would be welcome! Thank you -Jeff jeff.pr2 (at)

Hi, I'm getting an error message using polr(): Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : initial value in 'vmmin' is not finite The outcome variable is ordinal and factored, and the independant variable is continuous. I've checked the source code for both polr() and optim() and can't find any variable called

I'm having trouble getting fun="cloglog" to work with plot on a survfit object. Here are the data I used for the commands that follow. days status 2 0 2 0 5 1 9 0 14 1 16 0 16 0 17 0 29 1 30 0 37 1 37 0 39 1 44 0 44 0 58 0 60 1 67 1 68 1 82 1 82 1 86 0 86 0 89 1 93 0 97 1 100 0 100 0 100 0 > library(survival) Loading required package: splines > eg1.km <-

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n <- 10 N <- 1000 DF <- data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit <- GLMM(yield~1, random=~1|nest, family=binomial, data=DF, weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect

R versions 2.1.0 to present. Examples shown were computed under Windows R-devel, current SVN, but ix86 Linux shows similar behaviour (sometimes NaN or -Inf rather than Inf, depending on the compiler and optimization level used). The replacement pgamma algorithm used from R 2.1.0 has an inadequate design and no supporting documentation whatsoever. There is no reference given to support the