search for: brglm

I''m running brglm with binomial loguistic regression. The perhaps multicollinearity-related feature(s) are: (1) the k IVs are all binary categorical, coded as 0 or 1; (2) each row of the IVs contains exactly C (< k) 1''s; (3) k IVs, there are n * k unique rows; (4) when brglm is run, at least 1 IV is...

...() function, however I was having 2 warnings concerning glm.fit () : # 1: glm.fit: algorithm did not converge # 2: glm.fit: fitted probabilities numerically 0 or 1 occurred After some investigation I found out that the problem was most probably quasi complete separation and therefor decide to use brglm and/or logistf. * logistf : analysis does not run When running logistf() I get a error message saying : # error in chol.default(x) : # leading minor 39 is not positive definite I looked into logistf package manual, on Internet, in the theoretical and technical paper of Heinze and Ploner and canno...

Dear List, With regard to the question I previously raised, here is the result I obtained right now, brglm() does help, but there are two situations: 1) Classifiers with extremely high AIC (over 200), no perfect separation, coefficients converge. in this case, using brglm() does help! It stabilize the AIC, and the classification power is better. Code and output: (need to install package: brglm) ma...

Dear List, Apologies for this off-topic post but it is R-related in the sense that I am trying to understand what R is telling me with the data to hand. ROC curves have recently been used to determine a dissimilarity threshold for identifying whether two samples are from the same "type" or not. Given the bashing that ROC curves get whenever anyone asks about them on this list (and

...ion) is not a statistical artifact but it is something real. As suggested in http://r.789695.n4.nabble.com/OT-quasi-separation-in-a-logistic-GLM-td875726.html#a3850331 http://r.789695.n4.nabble.com/OT-quasi-separation-in-a-logistic-GLM-td875726.html#a3850331 (the last post is mine) I tried to use brglm procedure that uses a penalized maximum likelihood but it made no difference. What would you do if you were in my shoes? Thanks in advance for any help. Simone -- View this message in context: http://r.789695.n4.nabble.com/binomial-GLM-quasi-separation-tp3901687p3901687.html Sent from the R he...

...e non-fair game, none of neutral-mood participants did so (0/20). Thus, if drawing a 2x2 (mood x response, in the non-fair game) table, there was an empty cell. I've learned that I can use Firth's penalized likelihood method for bias reduction, which could be achieved using R packages "brglm" or "logistf". However, I found the packages only deal with non-clustered data, which is not the case for my data. I included game type as a within-subject variable and mood as a between-subject variable, and I am interested in their interaction. So, when involving the interaction te...

Hi all, As you can see from below, the result is strange... I would imagined that the bb result should be much higher and close to 1, any way to improve the fit? Any other classification methods? Thank you! data=data.frame(y=rep(c(0, 1), times=100), x=1:200) aa=glm(y~x, data=data, family=binomial(link="logit")) newdata=data.frame(x=6, y=100) bb=predict(aa, newdata=newdata,

...(1.6.0), aroma.core (1.6.0), asbio (0.3-10), ascii (0.7), automap (1.0-7), bayesmix (0.7-1), bbmle (0.9.5.1), bcp (2.2.0), bear (2.5.3), bibtex (0.2-0), bifactorial (1.4.4), bigmemory (4.2.3), binGroup (1.0-5), biOps (0.2.1.1), bipartite (1.12), blighty (3.1-0), bnlearn (2.1.1), bootruin (1.0-156), brglm (0.5-5), cairoDevice (2.13), caret (4.43), catspec (0.95), cgh (1.0-7.1), cluster (1.13.1), clusterSim (0.38-1), cmprskContin (1.6), coarseDataTools (0.2), coin (1.0-12), copula (0.9-7), corrplot (0.30), cshapes (0.2-4), ctv (0.6-0), cudaBayesreg (0.3-6), dagR (1.1.1), data.table (1.4.1), dclone (1...