similar to: Generalized Linear Model

I am fairly new to log-linear modelling, so as opposed to trying to fit modells, I am still trying to figure out how it actually works - hence I am looking at the interpretation of parameters. Now it seems most people skip this part and go directly to measuring model fit, so I am finding very few references to actual parameters, and am of course clear on the fact that their choice is irelevant for

Hello, When I put in the following script line: gap.boxplot(CLI3, CLI4, CLI5, CLI6, CLI7, gap=list(top=c(8000,280000), bottom=c(0,250)), range=50, outline=TRUE, par(ask=FALSE) I get a '+' telling me I am missing something. I have tried adding ')', 'width=NULL', etc and then I get this error: Error: unexpected symbol in: "gap.boxplot(CLI3, CLI4, CLI5, CLI6, CLI7,

Dear friends, I searched the R site and found a lot of results on general linear model and generalized linear model , and i was confused by them. Here, I only want to get some concise answers on the following questions and i'll study it by your hints: 1. Which function(package) could be used to fit the general linear model ? 2. Which function(package) could be used to fit the generalized

Hello UseRs, I've just tried to load the lme4 package and got the error message, "function 'cholmod_l_start' not provided by package 'Matrix'". I downloaded the latest version of lme4 and its required packages (lattice and Matrix) as suggested in the archives and still got this message. The FAQ and archives suggested to check the R version requirements, but I'm

Hello all, I have created a barplot that shows change in hardwood/softwood density from 1965 to 2005 in 5 year periods (1965,1970, etc). I would like to have an X-axis where the labels for the years line up after every two bars in the plot (there is one bar for hardwood, and another for softwood). Below is my script: density<-read.table("F:\\Megan\\Vtest.csv", header=TRUE,

Hello all, I am having trouble running a count function in R using RODBC to query a table I created in Oracle. It may very well be that my SQL coding is incorrect; I just started learning it. But if someone could point me in the right direction or tell me if I am going about this the correct way that would be greatly appreciated! The script I have right now is: >require(RODBC)

Hello list, I've been searching around trying to find whether somebody has written such a package of least angle regression on generalized linear models, like what Lasso2 package does. The extension to generalized linear models is briefly discussed in the comment by D. Madigan and G. Ridgeway. Is such a package available? Thanks, Mike [[alternative HTML version deleted]]

Hi, Is there an R implementation of least angle regression for binary response modeling? I know that this question has been asked before, and I am also aware of the "lasso2" package, but that only implements an L1 penalty, i.e. the Lasso approach. Madigan and Ridgeway in their discussion of Efron et al (2004) describe a LARS-type algorithm for generalized linear models. Has

Hello, I'm writng this message to see if anyone knows how to analyze a data with geometrically distributed outcome. My situation now is : I have a outcome which is geometrically distributed. And I've also self-derived the link function for geometric distribution. I want to use generalized linear models to analyze it, but there're only models for binary data, poisson distributed

Hello, I've been getting the following error when using the mlogit function from the mlogit package This is one of the examples provided in the Package "mlogit" January 27, 2010 description data("Fishing", package="mlogit") Fish <- mlogit.data(Fishing, varying = c(4:11), shape="wide", choice="mode") summary(mlogit(mode ~ pr + ca - 1,

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 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) >

Hi, I want to fit a linear model (without any random effect) with method "ml". I tried to use "glm" I found that there is no option for "ml" or "reml" and the default one is "reml". THen I tried to use "lme" but it requires a random effect. How can I fix this problem? Of course, it's not necessary to be "glm" or

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")

Greetings! I am working with Lustre-2.1.2 on RHEL 6.2. First I configured it using the standard defaults over TCP/IP. Everything worked very nicely usnig a real, static --mgsnode=a.b.c.x value which was the actual IP of the MGS/MDS system1 node. I am now trying to integrate it with Pacemaker-1.1.7. I believe I have most of the set-up completed with a particular exception. The "lctl

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

Greetings, I am interested in using a generalized linear mixed model with data that best fits a beta distribution (i.e., the data is bounded between 0 and 1 but is not binomial). I noticed that the beta distribution is not listed as an option in the "family objects" for glmmPQL or lmer. I found a thread on this listserve from 2006 ("[R] lmer and a response that is a

I have to fit a generalized linear model in R, and I have never done this before, so I'm in very much doubt. I have a dataset (of 4036 observations) claims sum grp 1 3852 34570293 1 2 1194 7776468 1 3 3916 26343305 1 4 1258 5502915 1 5 11594 711453346 1 ... there are 4 groups (grp). The task

Dear all, I have run a hierarchical log-linear analysis using loglin {stats} and came up with a specific model. loglin returns me the parameter estimates giving me an idea in which direction the observed frequencies deviate from the expected ones for my different factors. To assess the significance of any such effects I would need to calculate the ratio of the log-linear parameter

I am trying to estimate multinomial logit models off of a .csv table in IDCASE IDALT format where I have ROWS HHID PERID CASE ALTNUM NUMALTS CHOSEN IVTT OVTT TVTT COST DIST WKZONE HMZONE RSPOPDEN RSEMPDEN WKPOPDEN.... 1 1 2 1 1 1 5 1 13.38 2.00 15.38 70.63 7.69 664 726 15.52 9.96 37.26 2 2 2 1 1 2 5 0 18.38 2.00