similar to: Interaction Terms versus Interaction Effects in logistic regression

I recently became aware of the article by Ai and Norton (2003) about how interaction terms are problematic in nonlinear regression (such as logistic regression). They offer a correct way of estimating interaction effects and their standard errors. My question is: Does the glm() function take these corrections into account when estimating interaction terms for a logistic regression

Dear All, I have been struggling to understand why for the housing data in MASS library R and stata give coef. estimates that are really different. I also tried to come up with many many examples myself (see below, of course I did not have the set.seed command included) and all of my `random' examples seem to give verry similar output. For the housing data, I have changed the data into numeric

I was just curious if anyone knew of an alternative model to logistic regression where the probabilities seems pretty linear to the predictor rather than having that S shape that probit and logit assume. Maybe there is there some kind of other GLM that could accomplish that. Any textbook references or suggestions are appreciated. I have most of the texts but if someone knows of a text that talks

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Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

A student brought this question to me and I can't find any articles or examples that are directly on point. Suppose there are 2 ordinal logistic regression models, and one wants to set them into a simultaneous equation framework. Y1 might be a 4 category scale about how much the respondent likes the American Flag and Y2 might be how much the respondent likes the Republican Party in America.

Dear all, How can i obtain odds ratio in logistic regression when the model contains interaction terms in R? how can i obtain OR for a special case? Thanks in advance for any help. Amin

Version 1.03 of the R package boolean has been uploaded to CRAN and is now available. boolean implements partial-observability logit and probit models for testing Boolean hypotheses. It permits researchers to model the probability of the occurrence of a given outcome as a complex function of the probabilities that other outcomes will occur (or other conditions will be fulfilled). For

Version 1.03 of the R package boolean has been uploaded to CRAN and is now available. boolean implements partial-observability logit and probit models for testing Boolean hypotheses. It permits researchers to model the probability of the occurrence of a given outcome as a complex function of the probabilities that other outcomes will occur (or other conditions will be fulfilled). For

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,

Hi, I am fitting two models, a generalized linear model and a generalized additive model, to the same data. The R-Help tells that "A generalized additive model (GAM) is a generalized linear model (GLM) in which the linear predictor is given by a user specified sum of smooth functions of the covariates plus a conventional parametric component of the linear predictor." I am fitting the GAM

Dear Oscar, and any other R-project person, Can you please help me to figure out the meaning of the following error message in red ? Error in eval(predvars, data, env) : numeric 'envir' arg not of length one I computed ordered logit models using 'polr' in R (I just followed the guidance a handout I found on princeton.edu about logit, probit and multinomial logit models) . The

Dear All, I have some questions about probit regressions. I saw a nice introduction at http://bit.ly/bU9xL5 and I mainly have two questions. (1) The first is almost about data manipulation. Consider the following snippet ################################################## mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/binary.csv")) names(mydata) <-

> I make a study in health econometrics and have a categorical > dependent variable (take value 1-5). I would like to fit an ordered > probit or ordered logit but i didn't find a command or package who > make that. Does anyone know if it's exists ? R is very fancy. You won't get mundane things like ordered probit off the shelf. (I will be very happy if someone will show

Hello, I am trying to estimate a choice model with varying choice set for each individual. I would like to fit different kinds of model (logit ,nested logit, probit...). So far I have found that package *mnp* allows me to estimate a probit model with varying choice set. But for estimation of a logit model, I have only found function *multinom* of package *nnet* which does not seem to allow for

Hello, The following code: > binomial(identity) Generates an error message: Error in binomial(identity) : link "identity" not available for binomial family; available links are ?logit?, ?probit?, ?cloglog?, ?cauchit?, ?log? While : > binomial("identity") Yields an identity-binomial object that works as expected with stats::glm The error in the first example mislead

Hi, does anyone have a quick answer to the question of how to carry out interval regression in R. I have found "ordered logit" and "ordered probit" as well as multinomial logit etc. The thing is, though, that I want to apply logit/probit to interval-coded data and I know the cell limits which are used to turn the quantitative response into an ordered factor. Hence, it does

Dear All, Anyone know if there is a package that fits Generalized Linear Models(GAM) to data with ordered dependent variable(response) ? Simon Wood's mgcv has probit, logit,... other links, however, I could not find a way to do GAM *ordered *probit. Yee's VGAM claims to fit ordinal proportional odds model(cumulative logit model) (see: http://www.stat.auckland.ac.nz/~yee/VGAM/) but I

Hello, I'm working on the multiple categorical data (5-points scale) using linear mixed effect model and wondering if anyone knows about or works on the linear mixed effect model with ordered logit or probit link. I found that the "lmer" function in R is very flexible and supports various models, but not ordered logit/probit models. I may conduct my analysis by turning my DVs

Howdy everyone I?m trying to get Odds ratio and OR confidence intervals using a probit model, but I'm not getting. Do you think you can help me? I?m new with R L naive = summary(glm(pcr.data[,7]~boldBeta_individual+pcr.data$age,family=binomial(link=probit))) naive_answer = c(naive$coefficients[,1:3]) #naive estimates for