similar to: Factors and Multinomial Logistic Regression

Hello, I'd like to create a model with a factor-type response variable. This is an example: > mydata <- data.frame(factor_var = as.factor(c(rep('one', 100), rep('two', 100), rep('three', 100))), real_var = c(rnorm(150), rnorm(150) + 5)) > summary(mydata) factor_var real_var one :100 Min. :-2.742877 three:100 1st Qu.:-0.009493 two

Hi, I was trying to get error bars in my matplot. I looked at an earlier thread, and the sample code that I made is: #------------------ library(plotrix) mat1 <- matrix(sample(1:30,10),nrow=5,ncol=2) ses <- matrix(sample(1:3,10,replace=T),nrow=5,ncol=2) vect <- seq(20,100,20) rownames(mat1) <- rownames(ses) <- vect colnames(mat1) <- colnames(ses) <- letters[1:2]

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

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

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]]

Dear all, I have found some difference of the results between multinom() function in R and multinomial logistic regression in SPSS software. The input data, model and parameters are below: choles <- c(94, 158, 133, 164, 162, 182, 140, 157, 146, 182); sbp <- c(105, 121, 128, 149, 132, 103, 97, 128, 114, 129); case <- c(1, 3, 3, 2, 1, 2, 3, 1, 2, 2); result <- multinom(case ~ choles

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 am looking into using the multinomial logistic regression option in the nnet library and have two questions about formatting the data. 1. Can data be analysed in the following format or does it need to be transformed into count data, such as the housing data in MASS? Id Crime paranoia hallucinate toc disorg crimhist age 1 2 1 0 1 0 1 25 2 2 0 1 1 1 1 37 3 1 1 0 1 1 0 42 4 3 0

Dear all, I'm interested in extracting the score from multinomial logistic regression models fit using multinom, to assess the stregth of assocation of the parameter with the response (akin to the score from clogit/cox regression). currently I'm using R 1.8.1. Is there a function that will extract the score from a multinom object or how i can get back to it? or from using glm? I

Sir, I want to fit a multinomial logistic regression in R.I think mlogit() is the function for doing this. mlogit () is in packege globaltest.But, I can not install this package. I use the following: install.packages("globaltest") Can you help me? Regards, Suman Dhara [[alternative HTML version deleted]]

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)

Is it possible to use function "glm" in case when my outcome variable has 5 different classes? I have seen examples only when using binomial outcome variable. What about using function "multinom"? How do I to get the signifigance and the confidence levels of the coefficients and the value of goodness of the model with this function? Thank You for Your help! -- View this

Hi guys, I need your help. I'm trying to sort the data by the variable "obs". This is how I tried to sort the data below. The problem is, I have a variable name "obs"; this is.. a counter variable. something like _n_ in SAS. I do not know why it is not working. I even tried a similar example in UCLA webpage: http://www.ats.ucla.edu/stat/R/faq/sort.htm :it also does not

Dear Colleagues I have been fitting some multinomial logistic regression models using R (version 1.6.1 on a linux box) and Stata 7. Although the vast majority of the parameter estimates and standard errors I get from R are the same as those from Stata (given rounding errors and so on), there are a few estimates for the same model which are quite different. I would be most grateful if

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

Hello and thanks in advance for any help, I am using the 'multinom' function from the nnet package to calculate a multinomial logistic regression. I would like to get a matrix estimates of the estimated coefficient variances and covariances. Am I missing some easy way to extract these? Grant [[alternative HTML version deleted]]

I applied bootpred for multinomial logistic reg. (with nnet package). I used same as theta.fit and theta.predict of R for my data. but give me error. Can I do this with response vriable;7 levels predictor variables:5 (1 classifier, 4 continuous)?   Thanks alot Azam   [[alternative HTML version deleted]]

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,

Disclaimer first: I only heard about R fairly recently, so I apologize if this is either a simple or impossible request, but R looked like it might be a good framework for this sort of thing... Is it possible to write a script to run stepwise multinomial regressions on many *dependent* variables, and then compare results to a validation data set (e.g., Chow test)? Essentially, automate the

Hi, how can I do multinomial logistic regression in R? I think glm() can only handle binary response variable, and polr() can only handle ordinal response variable. how to do logistic regression with multinomial response variable? Thanks __________________________________