similar to: Coefficients, OR and 95% CL

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

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

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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

I want that postfix check the users in the ldap db that a have running but the only way that i find is compiling a source of postfix, and if i do that i have no anymore updates of postfix, so i have to update manualy each time thats be needed, in debian exists a package of postfix compiled with ldap support, and with updates support, but no in centos, in the redhat networks exists ? or if i

Dear R-experts, I am running R version 2.7.1 on Windows Vista. I have a small dataset which consists of ?chick ID?, ?year (0, 1)?, ?hatching order [HO, defined as first, second and third-hatched chick]?, and the binary outcome of interest ?death (0, 1)?. So a subset of my dataset looks like this on a txt file: y ID Yr HO 1 1 1 First 0 2 1 First 0 3 1 Second 0 4 1 First 1 5 1 First 0 6 1 Third

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 =

Ben Bolstad experienced something odd while running R CMD check over a package of ours using r-devel (yesterday's fresh). He tracked down the problem to a vignette calling 'library(MASS)'. Please kindly ignore if this is a known (and temporary) issue. Laurent ----- Forwarded message from Ben Bolstad <bolstad@stat.berkeley.edu> ----- Return-Path:

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

Hi, I've built customs kernels from the SRPMs with the following: rpmbuild -bp --target=i686 /path/to/spec cd /usr/src ln -s /usr/src/redhat/BUILD/kernel-x.y.z/linux-x.y.z linux cd linux make oldconfig make menuconfig make all make modules make modules_install And it works just fine. The problem now is that I think installing that way isn't

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

Hello everyone, I am trying to built an xts object and i have run into some problems on the data handling. I would really appreciate if someone could help me with the following: 1) I have a OHLC dataset with Time and date in different columns. How could i combine date and time in one column in order to pass on the new column to xts? I have use cbind and data.frame before but i did not manage to

Hi all, I have run into a problem and some help would be highly appreciated. I have a .csv with the following columns: Date Time Open High Low Close 1/2/2005 17:05 1.3546 1.3553 1.3546 1.35495 1/2/2005 17:10 1.3553 1.3556 1.3549 1.35525 1/2/2005 17:15 1.3556 1.35565 1.35515 1.3553 1/2/2005 17:25 1.355 1.3556 1.355 1.3555 ?. ?.. 2/13/2006 5:20 1.18895 1.18925 1.18835 1.1885 1)

Hi folks, I have assumed that ratios of variance components (Fst and Qst in population genetics) could be estimated using the output of mcmcsamp (the series on mcmc sample estimates of variance components). What I have started to do is to use the matrix output that included the log(variances), exponentiate, calculate the relevant ratio, and apply either quantile or or HPDinterval to get

Is the labeling/naming of levels in the documentation for the predict.glmmPQL function "backwards"? The documentation states "Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions". Taking the sample in the documentation: fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, family =

Hello! I am still a beginner. Can you tell me how to delete a row (from a matrix or data.frame) if the row name starts with U? See example below: U179971039 0.058663 0.087575 empty1 -0.1296 -0.09105 empty2 0.136259 0.398073 empty3 0.446041 -0.33997 Sally [[alternative HTML version deleted]]

Hello, I am reading in a file called fit2.txt (Limma). fit2.txt has 38 columns but when I dim(fit2) I only get 6 columns. The first column that it does not read in is df.residual. fit2<-read.table(fit2, file="fit2.txt",sep="\t",quote="",comment.char="",as.is=TRUE) The first few lines of fit2.txt (does not include all 38 columns) looks like this:

Hi All, I'm fitting an proportional odds model using vglm() from VGAM. My response variable is the severity of diseases, going from 0 to 5 (the severity is actually an ordered factor). The independent variables are: 1 genetic marker, time of medical observation, age, sex. What I *need* is a p-value for the genetic marker. Because I have ~1.5 million markers I'd rather not faffing

I am having trouble understanding results I'm getting back from coxph doing a recurrent event analysis. I've included the model below and the summary. In some cases, with minor variations, the Robust variance and Wald tests are significant, but the individual covariates may or may not be significant. My main question is: If Wald and robust tests both take into account the

Hi, I would like to use ssh in a quite a strange setup. For every ssh connection the near end starts a service (listening on a free TCP port) that has to be available to the program running on the far end. I would like to have the connection going through SSH (for encryption) so I decided to use remote port forwarding. The problem is that there could be multiple connections of that type to the