similar to: R-alpha: model.matrix(.) does not allow `contrasts' ..

Hi, I found that using different contrasts (e.g. contr.helmert vs. contr.treatment) will generate different fitted probabilities from multinomial logistic regression using multinom(); while the fitted probabilities from binary logistic regression seem to be the same. Why is that? and for multinomial logisitc regression, what contrast should be used? I guess it's helmert? here is an example

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 users, Does anyone know if it is possible to use multinom to do a polychotomous fit using one categorical and one numeric variable as response. The doc. for multinom states that for formula , response can be K>2 classes. Is this 2 and more, or as I have understood it only greater than 2. I have tried fitting my data, but have only encountered error messages. On another note, Is it

DeaR useRs: I am looking for a function which fits a multinomial model and in Baron?s page I find the function "multinom" in package "nnet" but this package is deprecated. I suppose that this function is now in other package but I can't find it. Can you help me? Thanks.

Hi all, I'm working on a multinomial (or "polytomous") logistic regression using R and have made great progress using multinom() from the nnet library. My response variable has three categories, and there are two different possible predictors. I'd like to use the likelihoods of certain models (ie, saturated, fitteds, and null) to calculate Nagelkerke R-squared values for

Is anyone aware of a way to check whether multinom has converged by checking a component of the output ? I'm not familar with nnet but, since multinom calls nnet , maybe there is an extra argument once can send to multinom to capture this information. Thanks.

Dear All: I am interested in multinomial logit models (function multinon, library nnet) but I'm having troubles in choose whether to define the predictors as factors or not. I had posted earlier this example (thanks for the reply ronggui): worms<- data.frame(year= rep(2000:2004, c(3,3,3,3,3)),age=rep(1:3,5),

Full_Name: Rob Foxall Version: 2.2.0 OS: Windows XP Submission from: (NULL) (149.155.96.5) I was using multinom from nnet package, when I did something stupid -- I entered in an incorrect factor variable as response. This factor had only one level. Instead of R telling me not to be so dumb, it crashed, clicking on debug coming up with the message "An exception 'Unhandled Win32

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

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

Hi: I'm working with multinomial models with library nnet, and I'm trying to get the explained deviance (pseudo R^2) of my models. I am assuming that: pseudo R^2= 1 - dev(model) / dev (null) where dev(model) is the deviance for the fitted model and dev(null) is the deviance for the null model (with the intercept only). library(nnet) full.model<- multinom(cbind(factor1,

Hi, I want to know is there other functions in R to estimate qualitative response model besides multinom() in library nnet, if this is the only possibility, I have a question about the application: for example: there is three transportation choice : car, bus , subway. each alternative has own characteristic variables, I want to apply conditional logit model to analysis the choice of three

Hi, Just wonder if someone could comment on using linear discriminant analysis (LDA) vs. multinomial logistic regression in multi-class classification/prediction (nomial dependent variable, not ordinal)? What kind of difference in results can I expect from the 2 methods, which is better or more appropriate, or under what condiditon should I used one instead of the other? And is there other

Hi, I am having difficulty entering a 'programmable' argument into the multinom function from the nnet package. Interactively, I can get the function to work fine by calling it this way: z1=multinom(formula = class.ind(grp[-outgroup])~ (PC1 + PC2 + PC3), data=data.frame(scores)) However I need to be able to change the number of variables I am looking for in 'scores' and so am

Hi, I am trying to specify a multinomial logit model using the multinom function from the nnet package. Now I add another independent variable and it halves the AIC as given by summary(multinom()). But when I call Anova(multinom()) from the car package, it tells me that this added variable is insignificant (Pr(>Chisq)=0.39). Thus, the improved AIC suggests to keep the variable but the Anova

Hola todos Cuando realizo las predicciones del modelo multinomial con el paquete nnet, estas cambian cada vez que lo ejecuto ... saben por qué pasa esto ?? Gracias por la ayuda. [[alternative HTML version deleted]]

Hi, I am working on a project which needs a multinomial logit regression. So I want to reference the code of multinom in nnet package. I found nnet package is no longer in the CRAN list. But I cannot find the source code in R Core source code package either. Anyone knows how to see the source code of nnet? Luo

Hello, I am VERY new to R (one week) and I am trying to run a multinomial logit model. The model I am using is > model1 <- multinom(Y ~ X1 + X2 + , ..., Xn) if I put in > summary(model1) I get #Error in function (classes, fdef, mtable) : unable to find an inherited method for function "fitted", for signature "multinom" and if I put in > coef(model1)

Hello, I apologize if this seems like an obvious question, but I have been looking everywhere and have yet to find an answer. I am doing a multinomial regression with multinom() in the nnet package. I have a 3 level ordered response (ordered()) variable and 4 predictors, 3 of which are numerical and one which is an ordered factor (also ordered()) with 5 levels (a, b, c, d, e). My question is in

I am hoping for some advice regarding resolving error messages I have received when trying to use the expand.grid command. library(nnet) library(MASS) library(car) mod.multacute <-multinom(kc$group ~ kc$in.acute.danger * kc$violent.convictions, na.rm=T) summary(mod.multacute, cor=F, Wald=T) Anova (mod.multacute) confint (mod.multacute) > predictors <- expand.grid(group=1:3,