similar to: multinom() and multinomial() interpretation

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

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]]

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

I am trying to create a LaTex table based on a multinom (nnet) object using the stargazer command. I have created a small data frame to demonstration the problem: data <- data.frame(age=1:21, hight=20:40, ed=c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3)) data$ed <- as.factor(data$ed) I then make a multinomial model using the command multinom from the nnet package: model <- multinom(ed ~

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),

Running a binary logit model on the data df <- data.frame(y=sample(letters[1:3], 100, repl=T), x=rnorm(100)) reveals some residual deviance: summary(glm(y ~ ., data=df, family=binomial("logit"))) However, running a multinomial model on that data (multinom, nnet) reveals a residual deviance: summary(multinom(y ~ ., data=df)) On page 203, the MASS book says that "here the

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

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 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

I want to analyse data with an unordered, multi-level outcome variable, y. I am asking for the appropriate method (or R procedure) to use for this analysis. > N <- 500 > set.seed(1234) > data0 <- data.frame(y = as.factor(sample(LETTERS[1:3], N, repl = T, + prob = c(10, 12, 14))), x1 = sample(1:7, N, repl = T, prob = c(8, + 8, 9, 15, 9, 9, 8)), x2 = sample(1:7, N, repl =

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

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

Please, could you tell me how to enter a mixed or a purely conditional multinomial logit model in NNET. I know how to do a multinomial logit in NNET but I don't know how to do conditional or mixed models using this package. I do know how to do this with VGAM - but would like to compare my results to those obtained by NNET. Thanks. Raffaele.

Hi all, I have a dataset with a response variable with three categories (1, 2, 3) and a lot of continuous variables. I'd like to make a MLR with these variables. I've been watching the libraries nnet and zelig for this purpose but I don't understand them well. I use a training sample data to make the MLR. train.set <- sample(1:1000,1000*0.7) I have done this: library(nnet) net

I would like to perform a multinomial logistic regression on a large data set, but do not know how. I've only thought of a few possibilities and write to seek advice and guidance on them or deepening or expanding my search. On smaller data sets, I have successfully loaded the data and issued commands such as: length(levels(factor(data$response))) [1] 6 # implies polychotomy library(nnet)

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

Dear R users, The nnet package includes the multinom method for the confint function. The R Help file (?confint) for the generic function in the stats package and the help files for the glm and nls methods in the MASS package indicate that one can use the "parm" argument as "a specification of which parameters are to be given confidence intervals, either a vector of numbers or

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,

Dear all, I have a problem for multinomial logistic regression: the response variable is multinomial (score 1-5) and the two predictors are categorical; all that comes from panelists (it's a kind of preference study), which I treat as a block and include in the model (is it correct?). I would like to see the results in the ANOVA-like framework. Fitting the multinom() function from the nnet

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