similar to: resolving expand.grid & NA errors

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

I am hoping for some advice regarding limiting decimal points to 3. 'Round' produces the desired results except for the 97.5% confidence interval. Any advice as to how I modify the code to obtain output to 3 decimal points for all ouput is appreciated, regards Bob Green mod.multgran <-multinom(offence ~ grandiose * violent.convictions, data = kc, na.action = na.omit)

On 05/06/2016 11:03 AM, Jonathan Roelofs wrote: > > > On 5/6/16 11:43 AM, Philip Reames via llvm-dev wrote: >> >> >> On 05/06/2016 09:02 AM, Rafael Espíndola via llvm-dev wrote: >>>>> Say what you want about the Linux kernel community, but you can't >>>>> call >>>>> it immature. You can call the behaviour of some of its

I came across this one: http://www.nysun.com/article/32787 which says that the violent death rate in Iraq (which presumably includes violent deaths from the war) is lower than the violent death rate in major American cities. Does anyone have any insights from statistics on how to interpret this?

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

Hi Hugo, I've been able to replicate your bug, including for other distributions (runif, rexp, rgamma, etc) which shouldn't be surprising since they're probably all drawing from the same pseudo-random number generator. ?Interestingly, it does not seem to depend on the choice of seed, I am not sure why that is the case. I'll point out first of all that the R-devel mailing list is

Hi Hugo, I've been able to replicate your bug, including for other distributions (runif, rexp, rgamma, etc) which shouldn't be surprising since they're probably all drawing from the same pseudo-random number generator. ?Interestingly, it does not seem to depend on the choice of seed, I am not sure why that is the case. I'll point out first of all that the R-devel mailing list is

Hi all, I am trying to run a two factor anova, but one of the factors is a random factor, now I am also running in SPSS and it seems its dividing by the wrong term to get the appropriate F term. here is my data. In SPSS the F scores about double the ones in R, how can I specify one of my factors as a random factor or change it to where it does the right model fitting? I am using the lm command

Hello all. I am doing one part of an evaluation of a mandatory welfare-to-work programme in the UK. As with all evaluations, the problem is to determine what would have happened if the initiative had not taken place. In our case, we have a number of pilot areas and no possibility of random assignment. Therefore we have been given control areas. My problem is to select for survey individuals in

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),

Dear all R-users, I am a new user of R and I am trying to build a discrete choice model (with more than two alternatives A, B, C and D) using logistic regression. I have data that describes the observed choice probabilities and some background information. An example below describes the data: Sex Age pr(A) pr(B) pr(C) pr(D) ... 1 11 0.5 0.5 0 0 1 40 1 0 0 0 0 34 0 0 0 1 0 64 0.1 0.5 0.2 0.2 ...

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

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-helpers, I need to compute probabilties of multinomial observations, eg by doing the following: y=sample(1:3,15,1) prob=matrix(runif(45),15) prob=prob/rowSums(prob) diag(prob[,y]) However, my question is whether this is the most efficient way to do this. In the call prob[,y] a whole matrix is computed which seems a bit of a waste. Is there maybe a vectorized version of dmultinom which

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

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

All: I am new to R and would like your help in identifying the appropriate process to follow in order to modify the output from an existing package. I've had difficulty finding an answer online, perhaps because I am using incorrect terminology. A package that I am using (mmlcr) invokes another package (multinom). An output of multinom is the standard errors, but this output is not

Does anyone try to run freeware game called "Babo Violent 2" ? It can be downloaded from http://www.rndlabs.ca/bv2. # wine --version Wine 0.9.24 I've got an error : "Error creating fmod" fixme:win:WIN_CreateWindowEx Parent is HWND_MESSAGE What Can I do with that?

I have RedHat linux 9 with R 1.8. I'm estimating models with multinom with a dependent variable that has 3 different values. Sometimes the models run fine and I can understand the results. Sometimes when I put in another variable, I see an indication that the estimation did work, but then I can't get the summary method to work. It's like this: > votemn1 <-

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