similar to: Fwd: How to best analyze dataset with zero-inflated loglinear dependent variable?

Hi all: Here's a question about result of loglinear analysis. There're 2 factors:area and nation.The raw data is in the attachment. I fit the saturated model of loglinear with the command: glm_sat<-glm(fre~area*nation, family=poisson, data=data_Analysis) After that,I extract the coefficients: result_sat<-summary(glm_sat) result_coe<-result_sat$coefficients I find that all the

Is there a way to automate fitting and assessing loglinear models for several nominal variables . . . something akin to step or drop1 or add1 for linear or logistic regression? Thanks. --Chris -- Christopher W. Ryan, MD SUNY Upstate Medical University Clinical Campus at Binghamton 40 Arch Street, Johnson City, NY 13790 cryanatbinghamtondotedu "If you want to build a ship, don't drum

Dear R Users, A would like to fit a loglinear analysis to a three dimensional contingency table. But I Don't want to run a full saturated modell. Is there any package in R that could handle somekind of stepwise search to choose out the best soultion? And how can I fit a non fully saturated modell, which only use the important interactions? Best Regards Zoltan Kmetty [[alternative HTML

Hi R-masters! I have a problem and need your help. I have 9 discrete variables with 2 levels each. In exploratory analisys I generate one matrix with chi-square for tables with 2 ariables each with this script setwd("F:/") dados<-read.csv("log.csv")[,2:10] dados.x<-matrix(NA,ncol=9,nrow=9) for(i in 1:8){ for(j in (i+1):9){ tab<-table(dados[,i],dados[,j])

Hi all, I've been running loglinear models for three-way tables: one of the variables having three levels, and the other two having two levels each. An example looks like below: > yes.no <- c("Yes","No") > switch <- c("On","Off") > att <- c("BB","AA","CC") > L <- gl(2,1,12,yes.no) > T <-

I am using the loglin function of the base package to fit log-linear models. I am interested in obtaining the parameter values and their standard errors. Parameters are easily obtained, but I haven't found the way of obtaining their standad errors. Is this possible with the loglin function? If not, is there any other function to get them? Many thanks, -- Vicente Piorno Departamento de Ecolox?a

Hello, I'm planning to start using R. Before getting into it, I'd like to ask a couple of questions. Does R carry out loglinear model analysis? That is, will it provide the chi-squared goodness of fit test statistic for a given hierarchical loglinear model? Maybe even do a model selection procedure (like Brown's two-step procedure, or forward/backward selection)? Thanks

The null loglinear model is an intercept-only model for log frequency, log(f) = \mu For a one-way table the test of the null model is the same as the chisq.test. This can be fit using loglin(), but I don't think there is any way to specify this using MASS::loglm > t1<- margin.table(Titanic,1) > t1 Class 1st 2nd 3rd Crew 325 285 706 885 > loglin(t1, NULL) 0

Hello, I am almost new in R, so perhaps my question will be silly. I try to use R for analyzing capture-recapture data in epidemiology. A cancer registry has different sources of patients. We know in each list, patients already known in all other list. The aim is to use capture-recapture models for estimating the number of patients unknow of all the sources. Because no order in sources, one

Hi, I'm analyzing experimental results where two different events ("T1" and "T2") can occur or not during an experiment. I made my experiments with one factor ("Substrate") with two levels ("Sand" and "Clay"). I would like to know wether or not "Substrate" affects the occurrence probability of the two events. Moreover, for each

Hi,Dear all R experts, I am trying to do the 2-way contingency table analysis by fitting the loglinear models. However, I found my table has several empty cells which are theoretically missing values.I have no idea of how to solve them coz we cannot compute the simulated p-value with zero marginals.Does someone have some suggestions? Please help me out, thanks a lot! Cheers, Yan

Dear all, can somebody give me some pointer how I can fit a "linear-by-linear association model" (i.e. loglinear model for the ordinal variables) in R? A brief description can be found here 'https://onlinecourses.science.psu.edu/stat504/node/141'. Thanks for your help

Hi there! I'm not sure I can create a minimal example of my problem, so I'm linking to a minimal .RData file that has only two objects: obs and exp, each is a 6x9 matrix. http://dl.dropbox.com/u/10364753/test.RData link to dropbox file (I hope this is acceptable mailing list etiquette!) Here's what happens: > obs[1, 1] [1] 118 > exp[1, 1] [1] 118 > obs[1, 1]-exp[1, 1] [1]

Hi all: For stratified count data,how to perform regression analysis? My data: age case oc count 1 1 1 21 1 1 2 26 1 2 1 17 1 2 2 59 2 1 1 18 2 1 2 88 2 2 1 7 2 2 2 95 age: 1:<40y 2:>40y case: 1:patient 2:health oc: 1:use drug 2:not use drug My purpose: Anaysis whether case and

Hi im new in the mailing list, i am trying to apply a loglinear model at my data in order to seek for significant correlations and effects. i have 27 variables kai 146 observations. I want to make a contingency table for this data set in R and i write table(data) but it doesn't work and appears this error message: Error in table(data) : attempt to make a table with >= 2^31 elements How

summary.table has a bug in the parameter line. It currently gives df = (K1 - 1) * (K2 - 1) * .. * (Kp - 1) (1) where Ki is the number of categories of variable i. The documentation and the expected counts say that the test is for independence among the variables (equivalent to a loglinear model [1][2]...[p]). So equation (1) is incorrect for this hypothesis, except for

I've posted a sample file for estimating loglinear models for square tables (mobility models) at http://www.xs4all.nl/~jhckx/mcl/R/ Comments and suggestions are welcome. John Hendrickx

I've computed a loglinear model on a categorical dataset. I would like to test whether an interaction can be dropped by comparing the log-likelihoods from two models(the model with the interaction vs. the model without). Since R does not immediately print the log-likelihood when I use the "glm" function, I used SAS initially. After searching for an extracting function, I found one

Hi All, I'm trying to implement a quasi symmetry model for data on twin pairs. A crosstabulation of twin 1 by twin 2 (assumed symmetrical) stratified by another variable. There is a good paper on this by Phil (?) McCloud and Darroch in Biometrika (1995) which explains the method, but I've not done this before so am not clear how to code these models. Any help would be greatly

I have a dataset with 25 subjects and 25 items. For each subject-item combination, there's a 0/1 score for two parts, A and B. I'm thinking of this as a set of 2 x 2 tables, 25 x 25 of them. I'd like to fit a log-linear model to this data to test the independence of the A and B scores. If I ignore the subject and item parts, the following works just fine: glm(count ~ A * B,