similar to: not R question : alternative to logistic regression

You can fit a linear probability model with glm and a bit of arm twisting. First, make your own copy of the binomial function: > dump('binomial', file='mybinom.R') Edit it to change the function name to "mybinom" (or anything else you like), and to add 'identity' to the list of okLinks. Source the file back in, and use mybiom('identity') to fit

Full_Name: Chanseok Park Version: R 2.2.1 OS: RedHat EL4 Submission from: (NULL) (130.127.112.89) pbinom(any negative value, size, prob) should be zero. But I got the following results. I mean, if a negative value is close to zero, then pbinom() calculate pbinom(0, size, prob). dbinom() also behaves similarly. > pbinom( -2.220446e-22, 3,.1) [1] 0.729 > pbinom( -2.220446e-8, 3,.1)

Full_Name: Chanseok Park Version: 1.8.1 OS: linux-gnu Submission from: (NULL) (130.127.112.183) digamma with any negative value does not give a right answer. It gives -1.797693e+308 for any negative arguments. For example, digamma(-1.1) gives -1.797693e+308. The right answer should be 10.15416 This bug can be easily fixed by using the following digamma identity. digamma(x) = digamma(1-x) -

This news item in a data mining newsletter makes various claims for a technique called "Reduced Error Logistic Regression": http://www.kdnuggets.com/news/2007/n08/12i.html In brief, are these (ambitious) claims justified and if so, has this technique been implemented in R (or does anyone have any plans to do so)? Tim C

Greetings: I'm running R2.6.2 on a WinXP DELL box with 2 gig RAM. I have created a new glm link function to be used with family = binomial. The function works (although any suggested improvements would be welcome), logit.FC <- function(POD.floor = 0, POD.ceiling =1) { if (POD.floor < 0 | POD.floor > 1) stop ("POD.floor must be between zero and one.") if

Not real data. It was gererated randomly. The original codes are the following: par(mfrow=c(2,1)) n <- 500 ######################### #DATA GENERATING PROCESS# ######################### x1 <- rnorm(n,0,1) x2 <- rchisq(n,df=3,ncp=0)-3 sigma <- 1 u1 <- rnorm(n,0,sigma) ylatent1 <-x1+x2+u1 y1 <- (ylatent1 >=0) # create the binary indicator ####################### #THE

Hi! I was trying to implement a probit model on a dichotomous outcome variable and found that the predictions were outside the (0,1) interval that one should get. I later tried it with some simulated data with a similar result. Here is a toy program I wrote and I cant figure why I should be getting such odd predictions. x1<-rnorm(1000) x2<-rnorm(1000) x3<-rnorm(1000)

Dear Friends, My problem is related to how to measure probabilities from a probit model by changing one independent variable keeping the others constant. A simple toy example is like this Range for my variables is defined as follows y=0 or 1, x1 = -10 to 10, x2=-40 to 100, x3 = -5 to 5 Model output <- glim(y ~ x1+x2+x3 -1, family=binomial(link="probit")) outcoef <-

Hi, I am trying to run two Non-Gaussian regressions: logistic and probit. I am receiving two different errors when I try to run these regressions and I am not sure what they mean or how to fix my syntax. Here is the logistic regression error: Error in family$linkfun(mustart) : Argument mu must be a nonempty numeric vector Here is the probit regression error: Error in pmax(eta, -thresh) :

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

Hi, I have a problem with the ordered probit model -polr function (library MASS). My independent variables are countinuos. I am not able to understand two main points: a) how to calculate marginal effects b) how to calculate the relative importance of each independent variables If required i will attach my model output. Thanks Franco

Hi Everyone: It turns out that there's still a small ( I hope ) problem. I'm close but that only counts in horse shoes and hand grenades. Here's my problem: When trying to load a package that I am writing, the load is looking for the packageDescription function in the utils package but not finding the utils package. I looked on cran and utils is not there which makes me think that it

I am trying to use the LME package to run a multilevel logistic model using the following code: ------------------------------------------------------------------------ ------------------------------------------- Model1 = GLMM(WEAP ~ TSRAT2 , random = ~1 | GROUP , family = binomial, na.action = na.omit ) ------------------------------------------------------------------------

Help needed with lm function: Dear R's, Could anyone tell me how to replace the link function (probit logit, loglog etc.) in lm with an abitrary user-defined function? The task is to perform ML Estimation of betas for a dichotome target variable. Maybe there is already a package for this (I did not find one). Any hints or a code excerpt would be welcome! Thank you -Jeff jeff.pr2 (at)

Hi Everyone: Gavin's been so generous and patient that I figured I'd give him a break and send this snag to the list. When I do get this working, I will send an "How to build R from the tarball " instructions message to this list for posterity's sake ( and myself and anyone else who doesn't know how to build R from the tarball ). So, here's my latest snag: Gavin

Dear everyone, the following problem: Our group has written a lengthy program in c++, to which we would like to add some additional features. Because we are not sure if those features are actually useful, we would prefer to take a "quick and dirty" approach just to try them out. The additional feature is that in every iteration of the algorithm membership-probabilities should by

Hi, in order to save space for a publication, it would be nice to have a combined scatter and density plot similar to what is shows on http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=78 I wonder if anybody perhaps has already developed code for this and is willing to share. This is the reproducible code for the histogram version obtained from the site: def.par <-

Dear R Team, First, thank you for incredibly useful software! Now the bad news: The attached script (or the original version with real data) will reliably crash R on my machine. I am using: R version: either 2.2.1 or 2.3.1 Windows 2000 Professional, Service Pack 4 512 MB of RAM On my machine the script will crash R on line 42 [ probits21 <- lapply(... ]. In both this script and the

I am new to R, ordered logistic regression, and polr. The "Examples" section at the bottom of the help page for polr<http://stat.ethz.ch/R-manual/R-patched/library/MASS/html/polr.html>(that fits a logistic or probit regression model to an ordered factor response) shows options(contrasts = c("contr.treatment", "contr.poly")) house.plr <- polr(Sat ~ Infl +

> Dear All > > I have data as follows. > > D T M L > 0.20 1 03 141 > 0.32 1 07 62 > 0.50 1 05 49 > 0.80 1 04 46 > 0.20 2 14 130 > 0.32 2 17 52 > 0.50 2 13 41 > 0.80 2 14 36 > 0.20 3 24 120 > 0.32