similar to: function logit() vs logistic regression

I am attempted to examine the temporal independence of my data set and think I need an unordered multinomial logistic regression (or logit model) with repeated measures to do so. The data in question is location of chickens. Chickens could be in any one of 5 locations when a snapshot sample was taken. The locations of chickens (bird) in 8 pens (pen) were scored twice a day (AMPM) for 20 days

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

Hello all: I've been given the following data and have been asked to run a logit model using glm(). The variable, Y, is a proportion ranging from 0 to 1, X is a covariate. Without a base number of observations from which Y is computed as a proportion, I believe there is not sufficient information. If I try the model below, R seems to grumble with a complaint. glm(cbind(Y,1-Y) ~ X,

Hello, Could anybody provide me with codes (procedure) how to obtain Conditional Logit (McFadden) and Mixed Logit (say, assuming normal distribution) estimates in R? Thanks, David U. -- View this message in context: http://www.nabble.com/Conditional-Logit-and-Mixed-Logit-tf4489238.html#a12802959 Sent from the R help mailing list archive at Nabble.com.

Dear R-Help, I'm hoping to find a Multinomial Nested Logit package in R. It would be great to find something analogous to "PROC MDC" in SAS: > The MDC (Multinomial Discrete Choice) procedure analyzes models > where the > choice set consists of multiple alternatives. This procedure > supports conditional logit, > mixed logit, heteroscedastic extreme value,

# 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

Hi all, I'm using a logistic regression model (created with 'glm') with 3 variables to separate true positives from errors in a data set. All in all it seems to perform quite well, but for some reason the logit values seem to be much lower that they should be. What I mean is that in order to get ~90% sensitivity and ~90% precision I have to set my logit cutoff at around -1 or 0. From

I'm running a multinomial logit in R using the Zelig packages. According to str(trade962a), my dependent variable is a factor with three levels. When I run the multinomial logit I get an error message. However, when I run 'model=logit' it works fine. any ideas on whats wrong? ## MULTINOMIAL LOGIT anes96two <- zelig(trade962a ~ age962 + education962 + personal962 + economy962 +

Good morning, I'd like to estimate a simple multinomial logit model in R (not a McFadden conditional logit). For instance, I'd like to estimate the probability of someone having one of eight titles in a company with the independent variables being the company characteristics. A binary logit is well documented. What about the multinomial? Thanks, Walt Paczkowski

Hi I am carrying out some logit regressions and want to (a) make sure I'm taking the right approach and (b) work out how to carry out some additional analysis. So, to carry out a logit regression where the dependent variable is a factor db, I use something like: res1_l <- glm(formula = db ~ y1 + + y5, family = binomial(link = "logit")) summary(res1_l) ...which is, I hope

Dear Listers, I have a general statistical question. Are hurdle logit-poisson model and posson model nested? Thank you so much?

Hi everyone? Does anybody know how can I estimate a Latent class multinomial (or conditional) logit using R? I have tried flexmix, poLCA, and they do not seem to support this model. thanks in advance adan -- View this message in context: http://r.789695.n4.nabble.com/Latent-class-multinomial-or-conditional-logit-using-R-tp4230083p4230083.html Sent from the R help mailing list archive at

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

I have a logit model with about 10 predictors and I am trying to plot the probability curve for the model. Y=1 = 1 / 1+e^-z where z=B0 + B1X1 + ... + BnXi If the model had only one predictor, I know to do something like below. mod1 = glm(factor(won) ~ as.numeric(bid), data=mydat, family=binomial(link="logit")) all.x <- expand.grid(won=unique(won), bid=unique(bid)) y.hat.new

Hi I am wanting to fit some multinomial logit models (multinom command in package nnet) Is it possible to do any model checking techniques on these models e.g. residual, leverage etc. I cannot seem to find any commands that will allow me to do this. Many thanks ---------------------- L.E.Gross L.E.Gross at maths.hull.ac.uk

Hello, I'd like to fit a logit function to my data. The data is distributed like a logit (like in this plot on wikipedia http://en.wikipedia.org/wiki/File:Logit.png) but the values on the x-axis are not between 0 and 1. I don't think using a glm is the solution because I simply want to infer the parameters of the logit function (offset, compression, slope...), so I can apply it to all

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 coefficients are combined across all the training races to come up with a final set of coefficients. My understanding is that they maximize log likelihood across the

Hi to all, I am a PH.D Student doing statistical analysis. I am totally new to R. I previously use Stata and am changing into R. I ususally do with logit regression with binary dependent variable (war occurence:1 or 0). I just want to know command to do that. More sepcifically, Let say, my Y is war occurence (occur=1, otherwise 0). And my independent variables (Xs) are trade, democracy,

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

Hello dear r-gurus! I have a question about the logit-model. I think I have misunderstood something and I'm trying to find a bug from my code or even better from my head. Any help is appreciated. The question is shortly: why I'm not having same coefficients from the logit-regression when using a link-function and an explicite transformation of the dependent. Below some details. I'm