similar to: loglinear models

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

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'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

Dear R gurus, I am looking for a way to fit a predictive model for a contingency table which has counts. I found that glm( family=poisson) is very good for figuring out which of several alternative models I should select. But once I select a model it is hard to present and interpret it, especially when it has interactions, because everything is done "relative to reference cell". This

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

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

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

Hi, We have some sample data from the US census, and we know the marginal totals for the population. We need to make the population estimates add up to the correct sums. I have two questions: Is there some package in R which does this adjustment, by any means? Is there some more modern reference for this problem than Deming's 1943 monograph, ``Statistical Adjustment of Data''?

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 netters, Sorry for cross-posting this question. I am sure R-Help is not a research methods discussion list, but we have many statisticians in the list and I would like to hear from them. Any function/package in R would be able to deal with the problem from this researcher? ---------- Forwarded message ---------- From: Heidi Bertels Date: Tue, Jun 5, 2012 at 4:31 PM Subject: How to best

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'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,

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

Hello everybody, Can anyone tell me how I could analyze data that are at a contigency table form? I already found function cfa in the cfa package but I still don't understand how I could use this function in order to elaborate a contigency table. Every answer is welcome! --------------------------------- ÁðïêôÞóôå ôçí äùñåÜí óáò@yahoo.gr [[alternative HTML version deleted]]

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 <-

Hi everyone, I have a question regarding a C function of the "stats" package in R. I tried to understand the ?loglin? basic function of the ?stats? package implemented in R. The implemented function itself runs without any problem (perhaps see sample). When I ran it line by line it stopped at the lines 23-24 of the loglin-function; (the following line): z <- .Call(C_LogLin,

H i, all! First of all, I'd like to apologize for my poor English. It's for years I don't use it. This is a R-version of a function I wrote a long ago for my HP48 calculator. It works with the binary expression of the power and just need to duplicate the mem used by X. Hope this helps. mtx.exp<-function(X,n) #Function to calculate the n-th power of a matrix X; { phi <-

Hi All, I am trying to creat a simple example byusing ipf function in R, but i could not get it succefully...I am very new to R, does anyone could help, to instruct me about this ipf fucntion? Actually, this is what I mean 50 | 50 ---------------------- 33.4| 28.57 | 14.29 33.3| 23.81 | 4.762 33.3| 9.523 | 19.05