similar to: How long to wait for process?

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.

I am desperately trying to impute missing data using 'imp.mix' but always run into this yucky error message to which I cannot find the solution. It's the first time I am using mix and I'm trying really hard to understand, but there's just this one step I don't get...perhaps someone knows the answer? Thanks! Jens My code runs:

I recently couldn't connect to a windowXP domain share from a winXP client.(Samba is the PDC) When I attempted to mount the share from the samba server this is the error I got. colin@Alfalfa:~/oakwood$ smbmount //buckwheat/share buckwheat/ Password: 6586: tree connect failed: ERRDOS - ERRnomem (Insufficient server memory to perform the requested function.) colin@Alfalfa:~/oakwood$ smbmount

I have the following problem with outputting multilingual data to a file. I get (except for Korean) what I expect as result in the RGui, but when I use sink() to output to a text file loose the characters in the foreign languages. I post a small example below. Since I am not sure how well my email system as the list copes with all the different characters I have additionally created a pdf

Dear R-list, Is there any way I can extract part or whole column name from two variables and combine them? Not values, but column names. For example, I have data set with 70 variables. The column names are "Alfalfa", "Corn","Soybean",..."Sunflower". After I combine the two variable (say Alfalfa and Corn), I want to rename it as "AlfCor" or

The above came with V1.2.3, but don't seem to be included with V1.3.0. When I upgraded R, I assumed they would also come with the new version, so didn't save them elsewhere. Could someone please email them to me, or tell me where I could get hold of them? Many thanks in advance. ****************************************************************************************** * Dr.

Hello R folk I have an xpt file which I have been trying to open into R in R studio On the net I found guidance which says that I need packages Hmisc and SASxport which I have successfully loaded. I had also found some code which says that this would allow me to read the xpt file into R: library(SASxport) data(Alfalfa) lookup.xport("test.xpt")

Hi all, Note: lm(Yield ~ Block + C(Variety, base = 2), Alfalfa) equals i <- 2; lm(Yield ~ Block + C(Variety, base = i), Alfalfa) However, lme(Yield ~ C(Variety, base = 2), Alfalfa, random=~1|Block) which is fine, does not equal i <- 2; lme(Yield ~ C(Variety, base = i), Alfalfa, random=~1|Block) after which I get the message "Error in model.frame.default(formula = ~Yield + Variety +

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

> On Apr 13, 2018, at 10:01 AM, WRAY NICHOLAS via R-help <r-help at r-project.org> wrote: > > Hello R folk > > I have an xpt file which I have been trying to open into R in R studio > > On the net I found guidance which says that I need packages Hmisc and SASxport which I have successfully loaded. > > I had also found some code which says that this would allow

I''m trying to sort an object by its property names. As I understand it, iterating over an object using for...in or using Hash.each() (or Hash.sortBy()) does not guarantee the order of the properties. Is there an easy and low-overhead way to guarantee order? Or would I need to have some type of collection object that uses arrays instead of the property-value configuration that Hash

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

Hello, I'm attempting to use samba winbind for ssh authentication for users in Active Directory. Things are mostly working, but I'm looking for two things: 1.) I currently have to authenticate as ADLOCAL+elijah at concordia.uchicago.edu . I would like to authenticate as elijah at concordia.uchicago.edu 2.) I would like the uid of the elijah user to be pulled from an existing

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

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