search for: menarche

TASK: problem with "glm" with binomial errors STATUS: Open FROM: p.dalgaard@kubism.ku.dk in glm(,binomial) it's possible that loss of significant digits make expected values 0 or 1 even though there's no divergence of the fit. (Happened to me with menarche data, infants and grown-ups included) [ Need the example data. Glm needs a complete overhaul. ] OK, here's a way to fake some data (simulated as if menarche occurs uniformly between 10 an 15 years of age) There's no real problem with fitting a logistic regression to this,...

1) hist() does not take NA's. Incompatible with Splus, probably just a bug? 2) I do wish we could somehow get rid of the misfeatures of indexing with logical NA's: > table(juul$menarche,juul$tanner) I II III IV V No 221 43 32 14 2 Yes 1 1 5 26 202 > juul$menarche=="Yes"&juul$tanner=="I",] ...and you find yourself with a listing of 477 cases, of which the 476 are NA-filled. To find the interesting case, you need to go through the followi...

...alculate OR by exponentiating the coefficient, and then I need the 95% CL for the OR as well. For the following example (taken from P. Dalaagard's book), what would be the most straightforward method of getting what I need? Could anyone enlight me please? Thank you! Lucho > summary(glm(menarche~age,binomial)) Call: glm(formula = menarche ~ age, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -4.68654 -0.13049 -0.01067 0.09608 2.35254 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -17.9175 1.7074...

? stato filtrato un testo allegato il cui set di caratteri non era indicato... Nome: non disponibile URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20110830/7c33d4d0/attachment.pl>

Suppose I have a factor size with levels "small", "medium" and "large". Then, when I subset this factor: >ss<-size[size!="medium"] to get at the extremes, >levels(ss) .... Levels: large medium small The same happens with >subset( size, size!="medium") I understand that the resulting factor inherits the possible levels from its

glm(A~B+C+D+E+F,family = binomial(link = "logit"),data=tre,na.action=na.omit) Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels however, glm(A~B+C+D+E,family = binomial(link = "logit"),data=tre,na.action=na.omit) runs fine glm(A~B+C+D+F,family = binomial(link =

...t, Boston, cabbages, caith, Cars93, cats, cement, chem, coop, cpus, crabs, Cushings, DDT, deaths, drivers, eagles, epil, farms, fdeaths, fgl, forbes, GAGurine, galaxies, gehan, genotype, geyser, gilgais, hills, housing, immer, Insurance, leuk, lh, mammals, MASS.data.load, mcycle, mdeaths, Melanoma, menarche, michelson, minn38, motors, muscle, newcomb, nlschools, nottem, npk, npr1, oats, OME, painters, petrol, phones, Pima.te, Pima.tr, Pima.tr2, quine, Rabbit, road, rock, rotifer, Rubber, ships, shoes, shrimp, shuttle, Sitka, Sitka89, Skye, snails, SP500, steam, stormer, survey, synth.te, synth.tr, top...

Sorry if this is a duplicate: This is a re-post because the pdf's mentioned below did not go through. Hello, I'm new'ish to R, and very new to glm. I've read a lot about my issue: Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred ...including: http://tolstoy.newcastle.edu.au/R/help/05/07/7759.html

Hello all I have done some fitting of pnorm functions to dose-response data, so I could calculate EC50 values (dose where the response is 0.5). I used the nlm function for this, so I did not get any information about the confidence intervals of the fitted parameters. What would be a good way to do such a probit fit, or is there a package which I could use? Best regards Johannes Ranke