search for: loglinear

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...

...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 analyze dataset with zero-inflated loglinear dependent variable? To: RMNET <rmnet at listserv.unc.edu> Dear colleagues, I have what I think is an interesting dataset, but I have never analyzed anything alike. As background: over a 12 week period, employees developed a business plan and attempted to obtain funding for their project i...

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 for your help. ---Harry Khamis -- Harry...

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<-r...

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?...

...27 61.573 [6,] 0.851 26.284 12.189 35.587 80.876 NA 57.346 0.741 49.738 [7,] 2.955 8.580 19.811 22.401 54.954 57.346 NA 1.520 80.615 [8,] 1.188 3.608 0.192 1.950 0.127 0.741 1.520 NA 0.311 [9,] 1.975 6.574 11.744 26.631 61.573 49.738 80.615 0.311 NA Now I need fit a loglinear model with this variables, but I need know have a command with generate ALL models with the set this 8 vairables (ALL minus [,8]) incluindind the interactions. Can Anyone Help me? Thanks in advance Bernardo Rangel Tura, MD, MSc National Institute of Cardiology Laranjeiras Rio de Janeiro Braz...

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

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...

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",&q...

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,

...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 cannot use classical models for closed animal populations (models M(bht) of Otis ) so I use hierarchical loglinear model, moreover for controlling interactions (dependence) between sources. For me it sound a little strange when I read in papers that authors use 'glm' and 'predict' for this problem even if I know that loglinear are in generalized linear model. Why don't use 'loglin'?...

...). However, my problem is that several cells have sampling zeroes. My experiments can't be done again to fill these cells. Thus Chi-square requirements are not fulfilled and I have to find another statistical method. After spending hours searching for a solution, I thought I could use loglinear model to answer my questions, but : - I'm not sure I can use loglinear model = do I fulfill the required conditions ? - would this method answer to my hypothesis ? - I not sure to really understand how I have to use loglin()? Here is the data frame of my results. DF<-data.frame(Subs=c(re...

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

...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] 2.842171e-14 Problem is, both obs and exp should be identical. They are the result of a saturated loglinear model, and I've run the same code across about 400 tables, all of which result in sum(obs-exp)=0, except for this one. I can't figure it out? Anyway, I need help understanding why 118 and 118 are not really the same. I appreciate some may be wary of downloading my .Rdata file (I'm on u...

...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 oc are correlated, and age is a stratified variable. My solution: 1,Mantel-Haenszel test by using function "mantelhaen.test" 2,loglinear regression by using function glm(count~case*oc,family=poisson).But I don't know how to handle variable "age",which is the stratified variable. Many thanks for your help. My best. [[alternative HTML version deleted]]

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

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]]

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...