similar to: indicator or deviation contrasts in log-linear modelling

Dear R-helpers, Im trying to fit a Log-linear model on a dataset with bird counts from 60 sites over 14 years for 12 months each (factors for the models). One of the aims is to predict the missing values in this dataset with model predictions. Ive first tried to work with GLM's, that worked fine except for models with one or more interaction-terms. The GLMs run, run .. for hours. So 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

I have been tinkering with mosaicplot() and friends as a way of learning R. As part of this, I've written a pair.table() method for mosaic matrices, and would like to extend mosaicplot to work with loglin and logln (MASS) objects. I'm using R 1.4.0 on Win 98. I've been trying to figure out the formula interface, and think there's a bug, but not sure how to find it, yet alone fix

Hello All: I know the function loglin() and loglm() from librarary(MASS) performs analysis on nominal categorical data. Are there any libraries, functions or examples available for analysis of ordinal categorical data? I have in mind procedures that can replicate results in Alan Agresti (1984) "Analysis of Ordinal Categorical Data." Thanks, ANDREW

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''?

Hi, It appears that there is no package for estimating log linear models (L. Goodman's family) for data in the form of frequency tables. Am I wrong? Thanks Marwan Khawaja Research Coordinator Fafo, Institute for Applied Social Science N-0608 Oslo, Norway Tel +47 22 08 86 00 +47 22 08 86 94 (Direct) +47 22 67 33 05 (Private) Fax +47 22 08 87 00

Hello to all, I recently downloaded R to my PC and am enjoying getting acquainted with it. Thank you to everyone involved in the R-project! I am interested in doing a log-linear analysis with R on a data set with dichotomous variables. There are 11 variables (columns) and around 1000 subjects (rows). How do I aggregate my data, i.e. how do I make a new dataset that includes the variable giving

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

Hello, at the moment I am doing quite a lot of regression, especially logistic regression, on 20000 or more records with 30 or more factors, using the "step" function to search for the model with the smallest AIC. This takes a lot of time on this 1.8 GHZ Pentium box. Memory does not seem to be such a big problem; not much swapping is going on and CPU usage is at or close to

Estimados, Existe algún package especifico para modelos lob lineales en R? Idealmente algo que funcione como el programa LEM. Muchas gracias por su ayuda, Saludos -- Pablo Reyes [[alternative HTML version deleted]]

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 have so much questions about using R...but i will try my best to solve myself before bothering you guys.... firstly... yes no male old 130 70 young 34 48 female old 50 60 young

Dear R users, I have a large individual-level dataset (~700,000 records) which I am performing a conditional logistic regression on. Key variables include the dichotomous outcome, dichotomous exposure, and the stratum to which each person belongs. Using this individual-level dataset I can successfully use clogit to create the model I want. However reading this large .csv file into R and running

Dear R users, Is it possible to use a generalized linear model to do a binomial comparison of one list of proportions with a matched list of proportions to test for a difference? So, for example: list 1 list 2 a1 | b1 a2 | b2 3 | 4 7 | 9 6 | 7 5 | 1 9 | 1 3 | 1 I want to compare list 1 with list 2 and the samples

I apologize in advance if this is the wrong forum for this report/request, and for the fact that I have not read the code for ftable.formula in any detail. >From reading the documentation for ftable.formula, I expected that the following two calls to ftable would produce the same results: data(UCBAdmissions) ftable(UCBAdmissions, row.vars = "Dept", col.vars = c("Gender",

Hey, Whenever I set up a log linear model using glm(Y~. , data=data, family=poisson) I get the parameters in the form of deviation from the first cell kombination. I find this to be hard to interpret when I for instance want to know if there is a difference between two factors in the first category since those parameters are not shown directly. Is there any way to get the summary() command, or

Dear R-help community, I am working with multidimensional contingency tables and I am having trouble getting loglm to run on all dimensions without typing out each dimension. I have generated random data and provided output for the results I want below: d1.c1 <- rnorm(20, .10, .02) d1.c2 <- rnorm(20, .10, .02) d2.c1 <- rnorm(20, .09, .02) d2.c2 <- rnorm(20, .09, .02) d3.c1 <-

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

Suppose I have a table constructed from structable or simply just an object of class table. How can I convert it to a latex object? I looked in RSiteSearch, but only found info about matrices or data frames. Steve For example, here is a table t2 > str(t2) table [1:2, 1:2, 1:2] 6 8 594 592 57 ... - attr(*, "dimnames")=List of 3 ..$ Hospital : chr [1:2] "A"

Bug report: there is a problem with loglm (or some underlying code) when used with updating within a function. For example, this code works as expected, when used outside a function: ------------------------------------------------- library(MASS)