similar to: Ask about glm()

Hi I am trying this > x <- read.table("/home/kenji/1245/GDS1_2grps_.cls", header = F, skip = 2) > x <- read.table("/home/kenji/1246/MYCset.cls", header = F, skip = 2) Warning message: In read.table("/home/kenji/1246/MYCset.cls", header = F, skip = 2) : incomplete final line found by readTableHeader on '/home/kenji/1246/MYCset.cls' Here are the

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Hollo I'm new, My name is Ariel Arelovich I've encountered the following problem trying to sue metaflac. I used the following command: c:\Archivos de programa\FLAC>metaflac --set-tag-from-file="CUESHEET=sola.cue" so la.flac sola.flac: ERROR: file 'sola.cue' for 'CUESHEET' tag value has embedded NULs And I've got that error. See I have the flac that is

I am creating a model attempting to predict the probability someone will reoffend after being caught for a crime. There are seven total inputs and I planned on using a logistic regression. I started with a null deviance of 182.91 and ended up with a residual deviance of 83.40 after accounting for different interactions and such. However, I realized after that my code is different from that in

My name is Kenji Ichinoseki and I am in charge of a project at Sumitomo Electric Networks, Inc. Please give me cooperation by all means about the affair in the account of a title. Now, I included "Samba 3.0.37" in the Linux system currently developed (us). And it is testing using "smbtorture(samba3.5.8 source4) ". The point in question was found in our entry of this test. It

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

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 =

Hi I am trying to make glm() work to analyze a toy logit system. I have a dataframe with x and y independent variables. I have L=1+x-y (ie coefficients 1,1,-1) then if I have a logit relation with L=log(p/(1-p)), p=1/(1+exp(L)). If I interpret "p" as the probability of success in a Bernouilli trial, and I can observe the result (0 for "no", 1 for

Hi, I had a logit regression, but don't really know how to handle the "Warning message: non-integer #successes in a binomial glm! in: eval(expr, envir, enclos)" problem. I had the same logit regression without weights and it worked out without the warning, but I figured it makes more sense to add the weights. The weights sum up to one. Could anyone give me some hint? Thanks a lot!

Dear all, I am using glm function in order to estimate a logit model i.e. glm(Y ~ data[,2] + data[,3], family = binomial(link = "logit")). I also created a function that estimates logit model and I would like it to compare it with the glm function. So, does anyone know what optimizer or optimization method glm uses in order to derive the result? Thank you Dimitris -- View this

Hello everyone, I am currently teaching an intermediate stats. course at UCSD Extension using R. We are using Venables and Ripley as the primary text for the course, with Freund & Wilson's Statistical Methods as a secondary reference. I recently gave a homework assignment on logistic regression, and I had a question about glm. Let n be the number of trials, p be the estimated

Hi all, The nobs method of (MASS:::polr class) takes into account of weight, but nobs method of glm does not. I wonder what is the rationale of such design behind nobs.glm. Thanks in advance. Best Regards. > library(MASS) > house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) > house.logit <- glm(I(Sat=='High') ~ Infl + Type + Cont, binomial,weights

m1.logit <-lm(default ~ amt.fac + age.fac + duration + chk_acct + history + purpose + sav_acct + employment + install_rate + pstatus

Hello R-users. I haven't use R for a life time and this might be trivial - I hope you do not mind. I have a questions about arguments in the Glm-function. There seems to be something that I cannot cope. The basics are ok: > y <- as.double(rnorm(20) > .5) > logit.model <- glm(y ~ rnorm(20), family=binomial(link=logit), trace = TRUE) Deviance = 28.34255 Iterations - 1

Actually, the best thing do to would be to use __int64 *unless* it's not supported. So there needs to be a configure check... Jean-Marc Le mar 01/06/2004 à 14:50, Kenji Chan a écrit : > I emailed Jean-Marc the arch.h about one week ago, not quite sure > whether he actually received that email. > > > > Anyway, here I have an improved version of arch.h that I

Posted about this earlier. Didn't receive any response But, some further research leads me to believe that MAYBE a GLMM or a GEE function will do what I need. Hello, I have a bit of a tricky puzzle with trying to implement a logit model as described in a paper. The particular paper is on horseracing and they explain a model that is a logit trained "per race", yet somehow the

Hi all, Trying to understand the logistic regression performed by glm (i.e. when family='binomial'), and I'm curious to know how it treats perfect success. That is, lets say I have the following summary data x=c(1,2,3,4,5,6) y=c(0,.04,.26,.76,.94,1) w=c(100,100,100,100,100,100) where x is y is the probability of success at each value of x, calculated across w observations.

Hi, I am running glm logit regressions with R and I would like to test a linear combination of coefficients (H0: beta1=beta2 against H1: beta1<>beta2). Is there a package for such a test or how can I perform it otherwise (perhaps with logLik() ???)? Additionally I was wondering if there was no routine to calculate pseudo R2s for logit regressions. Currently I am calculating the pseudo R2

I'm having sporadic problems with glm on my alpha workstation running Compaq Tru64 UNIX V5.0 (I can't replicate the problem on my Linux PC). I have the same problem whether I use a version of R built with the native C-compiler or a version compiled with gcc. In both cases I've built with g77. I want to do some simulations with a call to glm in each rep. A simplified version would be

I got the following warning messages when I did a binomial logit regression using glm(): Warning messages: 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, Can some one share your thoughts on how to