similar to: Multinomial Analysis

I am learning five mlogits as follows v1.model<-mlogit(v1~1|v2+v3+v4+v5, data=mlogit.v1.data, reflevel="1") v2.model<-mlogit(v2~1|v1+v3+v4+v5, data=mlogit.v2.data, reflevel="1") v3.model<-mlogit(v3~1|v1+v2+v4+v5, data=mlogit.v3.data, reflevel="1") v4.model<-mlogit(v4~1|v1+v2+v3+v5, data=mlogit.v4.data, reflevel="1")

[Added CC:virtualization at lists.linux-foundation.org] On Mon, Feb 18, 2008 at 11:28:41AM +0800, Dong, Eddie wrote: > Hi, Tony & all: > Recently Xen-IA64 community is considering to add paravirt_ops > support to keep sync with X86 and reduce maintenance effort. With > pv_ops, sensitive instructions or some high level primitive > functionalities (such as MMU ops) are replaced

[Added CC:virtualization at lists.linux-foundation.org] On Mon, Feb 18, 2008 at 11:28:41AM +0800, Dong, Eddie wrote: > Hi, Tony & all: > Recently Xen-IA64 community is considering to add paravirt_ops > support to keep sync with X86 and reduce maintenance effort. With > pv_ops, sensitive instructions or some high level primitive > functionalities (such as MMU ops) are replaced

Hi all, I have been working through the examples in one of the vignettes associated with the 'mlogit' package, 'Kenneth Train's exercises using the mlogit package for R.' In spite of using the code unchanged, as well as the data used in the examples, I have been unable to run a Wald test to test two models. Specifically, I have run the following command, where mc and mi2 are

I've been working on a multinomial logit model, trying to predict vegetation types as a function of total phosphorus. Previous responses to my postings have pointed me to the mlogit package. I'm now trying to work examples and my data using this package. data("Fishing", package = "mlogit") Fish <- mlogit.data(Fishing, varying = c(4:11), shape = "wide",

I am new to R but I have managed to use mlogit to run multivariate logit models successfully. My data violates the Independence of Irrelevant Alternatives assumption and now I would like to run a mixed logit model. It is a "wide" data set with 9 independent (individual) variables and three choices (variable Y). The database is in a cvs file called CAU. This is the code I have run

I am trying to follow this example for multinomial logistic regression http://www.ats.ucla.edu/stat/r/dae/mlogit.htm However, I cannot get it to work properly. This is the output I get, and I get an error when I try to use the mlogit function. Any ideas as to why this happens? > mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/mlogit.csv")) > attach(mydata) >

I'm trying to fit a multinominal logistic model using package mlogit. I have 15 independent variables. The code looks like this: m<-mlogit(score~0|f1+f2+f3+f4+f5+f6+f7+f8+f9+f10+f11+f12+f13+f14+f15, data, reflevel="1") And it gives the following error message: Error in parse(text = x) : unexpected ')' in "score ~ 0 + alt:(f1 + f2 + f3 + f4 + f5 + f6 + f7 + f8 + f9

Hi All, I am attempting to build a Multinomial Logit model with dummy variables of the following form: Dependent Variable : 0-8 Discrete Choices Dummy Variable 1: 965 dummy varsghpow at student.monash.edu.augh@gp1.com Dummy Variable 2: 805 dummy vars The data set I am using has the dummy columns pre-created, so it's a table of 72,381 rows and 1770 columns. The first 965 columns represent

Hello. I am beginning to analyze my work and have realized that a simple chi-square analysis will not suffice for my research, with one notable reason is that data are not discrete.  Since my data fit the assumptions of a logistic regression, I am moving forward with this analysis.  With that said, I am a beginner with R and would grealty appreciate any help!  Essentially, the point of my work

I am using the mlogit packages and get a data problem, for which I can't find any clue from R archive. code below shows my related code all the way to the error #--------------------------------------------------------------------------- mydata <- data.frame(dependent,x,y,z) mydata$dependent<-as.factor(mydata$dependent) mldata<-mlogit.data(mydata, varying=NULL,

--- silk/NSQ.c | 18 ++------------- silk/NSQ.h | 27 ++++++++++++++++++++++ silk/arm/NSQ_neon.c | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++ silk/arm/NSQ_neon.h | 10 ++++++++ 4 files changed, 105 insertions(+), 16 deletions(-) diff --git a/silk/NSQ.c b/silk/NSQ.c index d8513dc..ec81f3b 100644 --- a/silk/NSQ.c +++ b/silk/NSQ.c @@ -205,7 +205,7 @@ void

--- silk/NSQ.c | 18 ++------------- silk/NSQ.h | 27 ++++++++++++++++++++++ silk/arm/NSQ_neon.c | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++ silk/arm/NSQ_neon.h | 10 ++++++++ 4 files changed, 105 insertions(+), 16 deletions(-) diff --git a/silk/NSQ.c b/silk/NSQ.c index d8513dc..ec81f3b 100644 --- a/silk/NSQ.c +++ b/silk/NSQ.c @@ -205,7 +205,7 @@ void

Hi, I have the following test case: define void @foo(<2 x float>* noalias nocapture %out, <2 x float>* noalias nocapture %data0) nounwind { entry: %val1 = load <2 x float>* %data0, align 8 store <2 x float> %val1, <2 x float>* %out, align 8 fence acq_rel %val2 = load <2 x float>* %data0, align 8 store <2 x float> %val2, <2 x float>*

Hello: Has anyone tried to model-based recursive partition (using mob from package party; thanks Achim and colleagues) a data set based on a multinomial logit model (using mlogit from package mlogit; thanks Yves)? I attempted to do so, but there are at least two reasons why I could not. First, in mob I am not quite sure that a model of class StatModel exists for mlogit models. Second, as

Hi, For a long time we've been having a problem we've been working around in OpenCL where loads are moving across an intrinsic used for a barrier. Attached is the testcase, and the result of opt -S -basicaa -gvn on it. This example is essentially this: void foo(global float2* result, local float2* restrict data0, ...) { int id = get_local_id(0); // ... data0[id] = ...;

Hello, I would like to use the parfm package: https://cran.r-project.org/web/packages/parfm/parfm.pdfhttps://cran.r-project.org/web/packages/parfm/parfm.pdf in my work. This package fits parametric frailty models to survival data. To ensure I was using it properly, I started by running some small simulations to generate some survival data (without any random effects), and analyse the data using

To back up Ber's please have a look at http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and/or http://adv-r.had.co.nz/Reproducibility.html John Kane Kingston ON Canada > -----Original Message----- > From: jose.ferraro at logiteng.com > Sent: Wed, 13 Apr 2016 17:18:35 +0000 > To: cdesjard at umn.edu > Subject: Re: [R] reduced set of

Hi: When I call str() on an mlogit object, I seem to get warnings. This code is from an example provided in the mlogit documentation: library(mlogit) data("Train", package="mlogit") tr<-mlogit.data(Train, shape="wide", choice="choice", varying=4:11, sep="", alt.levels=c(1,2), id="id")

In R, square brackets [] are called "extraction operators" as they are interpreted so as to "extract" the parts of an object specified by the information within them. Your message contained only part of the line below: AltB<-svdatstr[row,indicesA][svdatstr[row,indicesA]<svdatstr[row,"maxB"]] Extraction operators may be specified sequentially or nested. Both