similar to: Bug in loglin with variable called "c" (PR#198)

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

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

In running the following example of a loglinear model for the Titanic data, I was surprised to see NaN reported for the Pearson chisq > loglin(Titanic, margin=list(1:3, 4)) 2 iterations: deviation 2.273737e-13 $lrt [1] 671.9622 $pearson [1] NaN $df [1] 15 $margin $margin[[1]] [1] "Class" "Sex" "Age" $margin[[2]] [1] "Survived" Tracing it back,

Kai, Apologies for the double message, it didn't go to the list last time. On Thu, Oct 22, 2015 at 9:59 AM, Kai Nitschke < kai.nitschke at uniklinik-freiburg.de> wrote: > > ?.Call? calls the C/C++ function ?C_LogLin?. But when I am running it line > by line I get > the following error on line 23/24: > Error: object 'C_LogLin' not found > > Hence, my

> tab <- array( sample(3^5), rep(3,5) ) > loglin( tab, list(1,2,3,4,5) )[[1]] # AOK 2 iterations: deviation 5.456968e-12 [1] 10909.89 > loglin( tab, list(1,2,3,4,5), c(1,2,3) )[[1]] # OUCH! Process R bus error at Wed Jul 21 17:03:55 2004 this is inconsistent - sometimes issuing this msg several times before barfing: Error in switch(z$ifault, stop("This should not

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 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,

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

anova() on three or more objects behaves inconsistently in R. In R anovalist.lm does a sequential ANOVA using pairwise F tests, ignoring all the other objects, so the larger of the two models provides the denominator. In S anova.lmlist uses the denominator from the largest model (smallest residual df) in the set, as does anova.glmlist in both. I suggest that R's anovalist.lm is wrong (that

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

predict.lm has been broken by recent changes to the patched branch. It fails for all singular fits. An example: library(MASS) data(quine) quine.hi <- aov(log(Days + 2.5) ~ .^4, quine) quine.nxt <- update(quine.hi, . ~ . - Eth:Sex:Age:Lrn) predict(quine.nxt) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 NA NA NA NA NA NA NA NA NA NA NA NA NA

> Date: Fri, 7 Apr 2000 09:55:08 -0400 (EDT) > From: Faheem Mitha <faheem at email.unc.edu> [I have given a more meaningful subject line.] > On Fri, 7 Apr 2000, Prof Brian D Ripley wrote: > > > On Thu, 6 Apr 2000, Faheem Mitha wrote: > > > > > I'm running splus 5 on a solaris platform remotely, and running R on linux > > > on my home machine.

Dear all, I would like to Boostrap the stepAIC() procedure from package MASS for variety of model objects, i.e., fn <- function(object, data, B = 2){ n <- nrow(data) res <- vector(mode = "list", length = B) index <- sample(n, n * B, replace = TRUE) dim(index) <- c(n, B) for (i in 1:B) { up.obj <- update(object, data = data[index[, i], ])

Last week Giovanni Parrinello posted a message asking why various packages were loaded when he loaded an .Rdata file. Brian Ripley replied saying he thought it was because the saved workspace contained a reference to the namespace of ipred. (Correspondence copied below). This begs the question: how did the reference to the namespace of ipred come to be in the .Rdata file? Brian did say it is

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

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 <-

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 Folks, I'm playing with glm.nb() in MASS. Reference: the negative binomial distribution P(y) = (Gamma(theta+y)/(Gamma(theta)*y!))*(p^theta)*(1-p)^y y = 0,1,2,... in the notation of the MASS book (section 7.4), where p = theta/(mu + theta) so (1-p) = mu/(mu + theta) where mu is the expected value of Y. It seems from ?glm.nb that an initial value of theta is either supplied, or

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 <-