similar to: How can I fit a fixed-effect linear model or generalized linear model with method="ml"?

When I ran a Gamma regression in SAS, the algorithm converged. When I ran it in R, it keeps uncoverged even if I used 10000 iterations. What was wrong? I used the following code in R: glm(y ~ x1 x2 x3, control=glm.control(maxit=10000), data, family=Gamma(link="log")) [[alternative HTML version deleted]]

Thanks to those who responded to my inquiry about generalized linear mixed models on R and S-plus. Before I summarize the software, I note that there are several ways of doing statistical inference for generalized linear mixed models: (1)Standard maximum likelihood estimation, computationally intensive due to intractable likelihood function (2) Penalized quasi likelihood or similar

Hello, I'm working on the multiple categorical data (5-points scale) using linear mixed effect model and wondering if anyone knows about or works on the linear mixed effect model with ordered logit or probit link. I found that the "lmer" function in R is very flexible and supports various models, but not ordered logit/probit models. I may conduct my analysis by turning my DVs

Dear All, I wonder if there is a way to fit a generalized linear mixed models (for repeated binomial data) via a direct Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey), the "glmmPQL" in the "MASS" package (Ripley) and "glmmGIBBS" (Myle and Calyton) are not using the full maximum likelihood as I understand. The

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

Hi, I need to run a PCoA (PCO) for a data set wich has both positive and negative values for variables. I  could not find any distancecoefficient other than euclidean distace running for the data set. Are there any other coefficient works with negtive values.Also I cannot get summary out put (the eigen values) for PCO as for PCA.   Thanks. Dilshan [[alternative HTML version deleted]]

Hi, In glmmPQL in the MASS library, the function uses repeated calls to the function lme(), using ML. Does anyone know how you can change this to REML? I know that in lme(), the default is actually set to REML and you can also specify this as 'method=REML' or 'method'ML' but this isn't applicable to glmmPQL(). I'd appreciate any help or advice! Thanks, Emma

Dear all, I am planning to use nlme library for analysis of experiments in semiconductor industry. Currently I am using "lm" but plan to move to "lme" to handle within wafer / wafer-to-wafer and lot-to-lot variation correctly. So far everything is working well, but I have a fundamentel question: NLME offers "maximum likelihood" and "restricted maximum

Hi, All,   My data collection is from 4 regions (a, b, c, d). Within each region, it has 2 or 3 units. Within each unit, it has measurement from about 25 sample site. I was trying to use lme function to discribe relationship between y and a few covariates. Both y and covariates were measured at the sample site level. My question is when I use exactlly the same model but choose different estimation

Hi there, How may I smooth spline two vectors with the smoothing parameter selected by generalized maximum likelihood (GML) .? Thanks a lot. -- View this message in context: http://r.789695.n4.nabble.com/Smoothing-spline-with-smoothing-parameters-selected-by-generalized-maximum-likelihood-tp4210679p4210679.html Sent from the R help mailing list archive at Nabble.com.

windows XP R 2.10 As pointed out by Prof. Venables and Ripley (MASS 4th edition, p275), the results obtained from lme using method="ML" and method="REML" are often different, especially for small datasets. Is there any way to determine which method is preferable for a given set of data? Thanks, john John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics

Hello, currently I am estimating an ordered probit model with the function polr (MASS package). Is there a simple way to obtain values for the prediction of the index function ($X*\hat{\beta}$)? (E..g. in the GLM function there is the linear.prediction value for this purpose). If not, is there another function / package where this feature is implemented? Thank you very much for

Dear R Team, First, thank you for incredibly useful software! Now the bad news: The attached script (or the original version with real data) will reliably crash R on my machine. I am using: R version: either 2.2.1 or 2.3.1 Windows 2000 Professional, Service Pack 4 512 MB of RAM On my machine the script will crash R on line 42 [ probits21 <- lapply(... ]. In both this script and the

Hi I am struggling to fit a non-linear trend using the likfit function in geoR. Specifically I want a sigmoidal function, something like SSfpl in the nls package to fit the trend. But it seems trend.spatial in geoR only works with lm or glm type models. Any ideas how I can specify the model to calculate the kriging parameters using REML, including the parameters of a sigmoidal trend function

Dear R-users, I am currently trying to switch from SAS to R, and am not very familiar with R yet, so forgive me if this question is irrelevant. If I try to find the significance of the fixed factor "spikes" in a generalized linear mixed model, with "site" nested within "zone" as a random factor, I compare following two models with the anova function:

Dear friends, I searched the R site and found a lot of results on general linear model and generalized linear model , and i was confused by them. Here, I only want to get some concise answers on the following questions and i'll study it by your hints: 1. Which function(package) could be used to fit the general linear model ? 2. Which function(package) could be used to fit the generalized

Hi, I have used the heckit function in micEcon. Now I would like to evaluate the fit of the probit part of the model but when I enter AIC(sk$probit) I get this error Error in logLik(object) : no applicable method for "logLik" How can I then get the AIC for this model? Side question: If you know - from the top of your head - some link to readings dealing with evaluating the

Dear all, I have a very simple set of data and I would like to analyze them with probit analysis. dose event trial 0.0 3 15 1.1 4 15 1.3 4 15 2.0 3 15 2.2 5 15 2.8 4 15 3.7 5 15 3.9 9 15 4.4 8 15 4.8 11 15 5.9 12 15 6.8 13 15 The dose should be transformed with log10(). I use glm(y ~ log10(dose), family=binomial(link=probit)) to do probit analysis, however, I have to exclude the

Hi, I'm trying to make a new S4 object with a slot for a "link-glm" object. R doesn't like me have a slot of class "link-glm" > class(make.link("probit")) [1] "link-glm" > setClass("a",representation(item="link-glm")) [1] "a" Warning message: undefined slot classes in definition of "a": item(class