search for: biometics

Hello, I was searching online to find more info about Biometics and I came across your information. Can you tell me, are you still involved with Biometics? If you are, how are things going for you? Please let me know. Sincerely, Will Hammack

http://biomet.oxfordjournals.org/cgi/reprint/86/3/677 biometrika1999 http://biomet.oxfordjournals.org/cgi/reprint/94/4/1006 biometrika2000 Hi All: I just want to try some luck. I am currenly working on my project,one part of my project is to reanalysis the kenward cattle data by using the method in Mohsen's paper,but I found I really can get the same or close output as he did,so,any

R Users, The d/p/q/r functions for the bridge distribution are now available in bridgedist. When a random intercept follows the bridge distribution, as detailed in Wang and Louis (2003) <doi:10.1093/biomet/90.4.765 <http://dx.doi.org/10.1093/biomet/90.4.765>>, a marginalized random-intercept logistic regression will still be a logistic regression with marginal coefficients that are

Dear All I recently proposed a simple modification to Wedderburn's 1974 estimate of overdispersion for count and binomial data, which is used in glm for the quasipoisson and quasibinomial families (see the reference below). Although my motivation for the modification arose from considering sparse data, it will be almost identical to Wedderburn's estimate when the data are not sparse.

Dear R contributors, I suggest adding a new method to `p.adjust` ("Adjust P-values for Multiple Comparisons", https://stat.ethz.ch/R-manual/R-devel/library/stats/html/p.adjust.html). This new method is published in Benjamini, Krieger, Yekutieli 2016 Adaptive linear step-up procedures that control the false discovery rate (Biometrika). https://doi.org/10.1093/biomet/93.3.491 This paper

UseRs, I have a dataframe with 2547 rows and several hundred columns in R 3.1.3. I am trying to run a small logistic regression with a subset of the data. know_fin ~ comp_grp2+age+gender+education+employment+income+ideol+home_lot+home+county > str(knowf3) 'data.frame': 2033 obs. of 18 variables: $ userid : Factor w/ 2542 levels

Michael, Thank you for the suggestion. I will take your advice and look more critically at the covariates. John On 7/27/2017 8:08 AM, Michael Friendly wrote: > Rather than go to a penalized GLM, you might be better off > investigating the sources of quasi-perfect separation and simplifying > the model to avoid or reduce it. In your data set you have several > factors with large

Rather than go to a penalized GLM, you might be better off investigating the sources of quasi-perfect separation and simplifying the model to avoid or reduce it. In your data set you have several factors with large number of levels, making the data sparse for all their combinations. Like multicolinearity, near perfect separation is a data problem, and is often better solved by careful

Hi, Late to the thread here, but I noted that your dependent variable 'know_fin' has 3 levels in the str() output below. Since you did not provide a full c&p of your glm() call, we can only presume that you did specify 'family = binomial' in the call. Is the dataset 'knowf3' the result of a subsetting operation, such that there are only two of the three levels of

Marc, Sorry for the lack of info on my part. Yes, I did use 'family = binomial' and I did drop the 3rd level before running the model. I think the str(<subset>) that I wrote into my original email might not have been my final step before using glm. Thank you for reminding of the potential problem. I think Michael Friendly's idea is probably the solution I need to consider.

Ben Bolker writes: > This looks really useful. Base R is very conservative; despite the > fact that it would be much more easily adopted in base R, I think it > is much more likely to find a home in an add-on package such as aods3 > or glm2 than in base R ... Thanks for these suggestions Ben - Simon Wood has also been in touch, and plans to put it into mgcv David Fletcher Original

If you mean this paper by Fahrmeir: http://biomet.oxfordjournals.org/cgi/content/abstract/81/2/317 I would recommend BayesX: http://www.stat.uni-muenchen.de/~bayesx/. BayesX interfaces with R and estimates discrete (and continuous) time survival data with penalized regression methods. If you are looking for a bona fide Bayesian survival analysis method and do not wish to spend a lot of time

Good morning I am learning about NLME and LME4, using Pinheiro and Bates and other materials from Douglas Bates, but I have not seen anything on how to do variable selection sensibly in this type of model. In OLS regression, I frequently use the lasso, but googling did not reveal a method for lasso with mixed models. Most of the material I've seen on these packages is about models with very