similar to: multinomial estimation output stat question - not R question

I was hoping for some advice regarding possible explanations for the fitted probability values I obtained for a multinomial logistic regression. The analysis aims to predict whether Capgras delusions (present/absent) are associated with group (ABH, SV, homicide; values = 1,2,3,), controlling for previous violence. What has me puzzled is that for each combination the fitted probabilities are

Dear all, 1)how can I easily get p value for the coefficients of factors in a multinomial model? 2)why the p values for "type III" test with Anova are not identical to that from SAS? for example: A,B and C are categorical variables,but the proportions of each level in each categorical variables are not balance. Y is a nominal variables(>=3 categories); To do

Hi, I am trying to obtain p-values for coefficient estimates in a multinomial logit model. Although I am able to test for significance using other methods (e.g., Wald statistics), I can't seem to get R to give me simple p-values. I am sure there is a very simple solution to this, but the R archives seem to have nothing on this issue. I would appreciate any help. Thanks in advance! Best,

Buenos días, Gracias Carlos, siguiendo el ejemplo que comentas, esto es lo que he introducido en el Scrip de RStudio: *library(xlsx)* *library(xlsxjars)* *library(rJava)* *library(aod)* *R<-read.csv("2002.CSV", sep=";", dec=",", header=T)* *attach(R)* *group<-gl(2,670,1340,labels= c("AVE", "Log.Imports.Value.in.1000.USD"))*

Hi, I'm interested in building a Cox PH model for survival modeling, using 2 covariates (x1 and x2). x1 represents a 'baseline' covariate, whereas x2 represents a 'new' covariate, and my goal is to figure out where x2 adds significant predictive information over x1. Ideally, I could get a p-value for doing this. Originally, I thought of doing some kind of likelihood ratio

Sent this mail in rich text format before. Excuse me for this. ------------------------ Dear all, I'm using the lrm function from the package "Design", and I want to extract the p-values from the results of that function. Given an lrm object constructed as follows : fit <- lrm(Y~(X1+X2+X3+X4+X5+X6+X7)^2, data=dataset) I need the p-values for the coefficients printed by calling

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the

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

Hello dear R-users, does any of you know a way to perform a multinomial regression with clustered data (i.e. repeated measurements)? I made the first analysis with Stata option vce cluster in the mlogit command but was looking for a similar functionality in R too... thanks all! niccolò [[alternative HTML version deleted]]

At work after updating to R 2.5 I get an error using epitab from package epitools, when at home (R 2.4) I get no error. Could someone help me? Thanks Pietro Bulian Servizio di Onco-Ematologia Clinico-Sperimentale I.R.C.C.S. Centro di Riferimento Oncologico Via Franco Gallini 2 33081 Aviano (PN) - Italy phone: +39 0434 659 412 fax: +39 0434 659 409 e-mail: pbulian at cro.it (at work)

Hello R-Users,   I have one binary dependent variable and a set of independent variables (glm(formula,…,family=”binomial”) ) and I am using the function stepAIC (“MASS”) for choosing an optimal model. However I am not sure if stepAIC considers significance properties like Likelihood ratio test and Wald test (see example below).     > y <- rbinom(30,1,0.4) > x1 <- rnorm(30) > x2

Hola a todos y todas, Gracias por vuestro apoyo en cantidad de preguntas anteriores, de nuevo os escribo para compartir una duda: Estoy trabajando con un modelo bien sencillo, es una regresión simple, pero me gustaría comprobar la significación estadística de cada uno de los coeficientes de regresión en el modelo. La idea es hacer un contraste de hipótesis. Me he descargado el paquete

Dear members, here is my trouble: My data consists of counts of trapped insects in different attractive traps. I usually use GLMs with a poisson error distribution to find out the differences between my traitments (and to look at other factor effects). But for some dataset where one traitment contains only zeros, GLM with poisson family fail to find any difference between this particular traitment

Dear all, I am trying to compare the estimated coefficients of a quantile regression model between two different samples. It is a Wald test, but I cannot find one way to do that in R.The samples are collected conditional on a specific characteristic and I would like to test whether such characteristic indeed affect the estimators. The problem in the test anova.rq is that the response variable

If one estimates a model using multinom, is it possible to perform the omnibus LR test ( the analogue to omnibus F in linear models ) using the output from multinom ? The residual deviance is there but I was hoping I could somehow pull out the deviance based on just using an intercept ? Sample code is below from the CAR book but I wasn't sure how to do it based on that example. Thanks

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are

hello, I work since a few time on R and i wanted to know how to obtain the Wald chi square value when you make a binary logistic regression. In fact, i have the z value and the signification but is there a script to see what is the value of Wald chi square. You can see my model below, Best regards, S??verine Erhel [Previously saved workspace restored] > m3 = glm(reponse2 ~ form +

I want to get the logLik to calculate McFadden.R2 ,ML.R2 and Cragg.Uhler.R2, but the value from multinom does not have logLik.So my quetion is : is logLik meaningful to multinomial logistic model from multinom?If it does, how can I get it? Thank you! ps: I konw VGAM has function to get the multinomial logistic model with logLik, but I prefer use the function from "official" R

I have two questions: 1. If I want to do a binomial logit, how to handle the categorical response variable? Data for the response variables are not numerical, but text. 2. What if I want to do a multinomial logit, still with categorical response variable? The variable has 5 non-numerical response levels, I have to do it with a multinomial logit. Any input is highly appreciated! Thanks! Ed

I have a doubt about the calculation of tests for fixed effects in mixed-effects models. I have read that, except in well-balanced designs, the F statistic that is usually calculated for ANOVA tables may be far from being distributed as an exact F distribution, and that's the reason why the anova method on "mer" objects (calculated by lmer) do not calculate the denominator df nor a