search for: logits

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,

The following is a simple example with a poor solution that shows my difficulties with scope. The function /logit.test /has 3 arguments: /model.start,/ an initial model; /model.finish/, an all-inclusive model, /my.data/, a dataset, in this case trivial. There are 2 function calls in l/ogit.test,/ first to /glm/ to get an initial fit (local variable /logit/) using /model.start;/ then a call

# 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

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

Hell all. I¡¯m trying to estimate mixed logit model using MSLE. In order to see that mixed logit model works better than simple logit model ( the logit model with fixed coefficient) I simulated a dataset with random coefficients and tried to fit the data with both mixed logit and simple logit model. Because my mixed logit model contains analytically intractable integrations, I applied

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.

Hi. I am using the Matching package for propensity score matching. For each treated unit, I want to find all control units whose propensity scores lie within a certain distance from the treated unit. The sample code is as follows: > library(Matching) > x <- rnorm(100000) > y <- rnorm(100000) > z <- rbinom(100000,1,0.002) > logit.reg <-

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 +

I know this has been dealt with before on this list, but the previous messages lacked detail, and I haven't figured it out yet. The model is: \x_{ij} = \mu + \alpha_i + \beta_j \alpha is a random effect (subjects), and \beta is a fixed effect (condition). I have a link function: p_{ij} = .5 + .5( 1 / (1 + exp{ -x_{ij} } ) ) Which is simply a logistic transformed to be between .5 and 1.

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

Hi all, Thank you for taking the time to read my message. I'm trying to make a figure that plots p-values by a range of different adjustment values. (Using the **logit** function in package **car**) My Statistical analyses were conducted on probability estimates ranging from 0% to 100%. As it's not ideal to run linear models on percentages that are bounded between 0 and 1, these

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

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'm new to the glm and logit world... and I'm reading some lecture notes and examples. I would like to try and generate the same result in R.. but I don't seem to be able to find the proper way to specify the formula.... let's say i have Desire Using Drugs Not Using Drugs Yes 219 753 No 288 347 Desire

Dear R users this may be a simple question - but i would appreciate any thoughts does anyone know how you would get one lower and one upper confidence interval for a set of data that consists of proportions. i.e. taking a usual confidence interval for normal data would result in the lower confidence interval being negative - which is not possible given the data (which is constrained between

Hi, I'm using gl1ce with family=binomial like so: >yy succ fail [1,] 76 23 [2,] 32 67 [3,] 56 43 ... [24,] 81 18 >xx c1219 c643 X1 0.04545455 0.64274145 X2 0.17723669 0.90392792 ... X24 0.80629054 0.12239320 >test.gl1ce <- gl1ce(yy ~ xx, family = binomial(link=logit), bound = 0.5 ) or >omit <- c(2,3) >test.gl1ce

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 All, I am synchronizing a few hundred dovecot mailboxes with a perl script via a crontab. ? It works for a while (ie exit status=0)? but the rest then start failing with exit code 19200.?? I tried redirecting STDERR but showed nothing.? I suspect the cron job is hitting some sort of resource limit?? If I run the perl script from the command line it appears to dsync all the accounts OK.?? The

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! When I am analyzing proportion data, I usually apply logistic regression using a glm model with binomial family. For example: m <- glm( cbind("not realized", "realized") ~ v1 + v2 , family="binomial") However, sometimes I don't have the number of cases (realized, not realized), but only the proportion and thus cannot compute the binomial model. I just