similar to: Sig differences in Loglinear Models for Three-Way Tables

Displaying 20 results from an estimated 200 matches similar to: "Sig differences in Loglinear Models for Three-Way Tables"

2013 Jul 06
0
fitting the null loglinear model with MASS::loglm??
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
2000 Oct 06
1
quasi-symmetry loglinear models
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
2009 May 19
1
loglinear analysis
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
2005 Apr 30
0
lmer for mixed effects modeling of a loglinear model
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,
2012 Jun 08
1
Fwd: How to best analyze dataset with zero-inflated loglinear dependent variable?
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
2005 Aug 30
1
loglinear model selection
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])
2002 Oct 23
2
loglinear models
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
2009 Apr 20
4
automatic exploration of all possible loglinear models?
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
2008 Mar 07
1
Finding Interaction and main effects contrasts for two-way ANOVA
I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-
2010 Nov 08
1
unknown dimensions for loglm
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 <-
1999 Apr 14
2
No LOGLM coefficients
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
1999 Feb 18
1
[R] possible bug in loglm (PR#122)
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)
2003 Oct 30
0
loglm() uses only a reference to data, and not data itsel f - is that on purpose??
loglm() is a port of an original written for S-PLUS. The fact that it carries only a reference to the data frame is neither intentional nor unintentional, but an unnoticed side-effect. I can see advantages both ways. (I'm not so sure, either, that what you say is standard behaviour for model fitting functions really is so universal.) It would not be too hard to come up with a version that
2011 Jan 19
3
question about result of loglinear analysis
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
2009 Jan 14
1
loglm fitting
Dear all, sorry to bother you all with this but I've been trying to use the loglm in MASS package (v2.8.0) and cannot get any sensible output. I'm wondering am I doing something very foolish or missing something obvious. For example, I tried the documentation help(loglm) example - here's the code # Case 1: frequencies specified as an array. sapply(minn38,
2006 Nov 26
2
Fixed zeros in tables
Hello All R Users, Function loglm() in library MASS can be cajoled to accomodate structural zeros in a cross-classification table. An example from Fienberg demonstrates how this can be done. My question is: Can the function glm() perform the same task? Can glm() estimate a log-linear model with fixed zeros like loglm()? Thanks for your help, Andrew ## Fienberg, The Analysis of Cross-Classified
2010 Mar 01
0
MASS::loglm - exploring a collection of models with add1, drop1
I'd like to fit and explore a collection of hierarchical loglinear models that might range from the independence model, ~ 1 + 2 + 3 + 4 to the saturated model, ~ 1 * 2 * 3 * 4 I can use add1 starting with a baseline model or drop1 starting with the saturated model, but I can't see how to get the model formulas or terms in each model as a *list* that I can work with further. Consider
2010 Aug 03
2
How to extract ICC value from irr package?
Hi, all There are 62 samples in my data and I tested 3 times for each one, then I want to use ICC(intraclass correlation) from irr package to test the consistency among the tests. *combatexpdata_p[1:62] is the first text results and combatexpdata_p[63:124] * is the second one and *combatexpdata_p[125:186]* is the third. Here is the result:
2007 May 23
0
Replicated LR goodness-of-fit tests, heterogeneity G, with loglm?
I have numerous replicated goodness-of-fit experiments (observed compared to expected counts in categories) and these replicates are nested within a factor. The expected counts in each cell are external (from a scientific model being tested). The calculations I need within each level of the nesting factor are a heterogeneity G test, with the total G and the pooled G across replicates. Then I
2013 Sep 12
6
declaring package dependencies
I received the following email note re: the vcdExtra package > A vcd update has shown that packages TIMP and vcdExtra are not > declaring their dependence on colorspace/MASS: see > > http://cran.r-project.org/web/checks/check_results_vcdExtra.html But, I can't see what to do to avoid this, nor understand what has changed in R devel. Sure enough, CRAN now reports errors in