search 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 <- rnorm(20, .11, ....

...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 switched to using LOGLM (MASS-library), and that worked swift. The deviances were the same so that worked well. The only problem is that my data contains both zero's (rarely) and quite a lot of NA's. LOGLM doesn't report the params in these cases (the help file reports this) but I still want to use them for p...

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) cts<-c(25,13,99,45,128,78,151,153,173,171,49,62,1308,236,989,264,622,201,492,244,382,237,...

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 fitti...

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 iterations: deviation $lrt [1] 475.8113 $pearson [1] 467.8069 $df [1] 3 $margin NULL > chisq.test(t1) Chi-squared test for given probabilities data: t1 X-squ...

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(min...

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 fo...

...On No 4998 3 BB Off Yes 25 4 BB Off No 339 5 AA On Yes 305 6 AA On No 2752 7 AA Off Yes 31 8 AA Off No 692 9 CC On Yes 251 10 CC On No 1677 11 CC Off Yes 17 12 CC Off No 557 First, I run the independence model and found a poor fit: > library(MASS) > loglm(n~A+T+L) Call: loglm(formula = n ~ A + T + L) Statistics: X^2 df P(> X^2) Likelihood Ratio 1001.431 7 0 Pearson 1006.287 7 0 Thus, I went on and run the two-way association model and found a good fit: > loglm(n~A:T+A:L+T:L) Call: loglm(formu...

...t; - attr(*, "class")= chr [1:2] "xtabs" "table" - attr(*, "call")= language xtabs(formula = as.formula(paste("freq ~", paste(tvars, collapse = "+"))), data = table) # fit baseline log-linear model for Status as response hoyt.mod0 <- loglm(~ Status + (Sex*Rank*Occupation), data=Hoyt1) > (hoyt.add1 <- add1(hoyt.mod0, ~.^2, test="Chisq")) Single term additions Model: ~Status + (Sex * Rank * Occupation) Df AIC LRT Pr(Chi) <none> 2166.36 Status:Sex 1 2129.54 38.82 4.658e-10 *** Status:Rank 2 1430.03 740.33 < 2...

...divided by its degrees of freedom to heterogeneity G divided by its degrees of freedom. The F ratio would (I think) test the hypothesis that the badness-of-fit in the pooled data is greater than would be expected by chance from the heterogeneity among replicates. It seems that the function loglm is the closest within R to what I want. But I can¢t see how it can be used when the expected proportions are externally provided. I¢ve appended here a function I wrote that more or less does what I want (with the nesting factor ignored) but I would prefer to use something like loglm because...

...Extra 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 examples using MASS::loglm(), using R Under development (unstable) (2013-09-11 r63906) > Caesar.mod0 <- loglm(~Infection + (Risk*Antibiotics*Planned), data=Caesar) Error: could not find function "loglm" In DESCRIPTION I have Depends: R (>= 2.10), vcd, gnm (>= 1.0.3) Suggests: ca,gmodels,Fahrmeir,e...

Dear all, I have a problem to make figures with two columns in package vcd. Here an example code I take from "\library\vcd\html\plot.loglm.html" What I need, I want to make two figures in one plot. How could I do that. I have tried with layout(rbind(c(1, 1, 2, 2))) but the same result, two plot. Best wishes, Muhammad Subianto library(vcd) oldpar <- par(mfrow=c(1, 2)) ## mosaic display for PreSex model data(PreSex) fm <- l...

...ot;No") > tk <- c("On","Off") > ats <- c("S","V","M") > L <- gl(2,1,12,yes.no) > T <- gl(2,2,12,tk) > A <- gl(3,4,12,ats) > n <- c(1056,4774,22,283,326,2916,27,360,274,1770,15,226) > library(MASS) > l.loglm <- data.frame(A,T,L,n) > l.loglm A T L n 1 S On Yes 1056 2 S On No 4774 3 S Off Yes 22 4 S Off No 283 5 V On Yes 326 6 V On No 2916 7 V Off Yes 27 8 V Off No 360 9 M...

...rent major version (6.1 not 5.3) and please don't use it unless you like testing pre-release software. BTW, the name is MASS, not mass or Mass: it is an acronym. > have encountered a minor problem you might want to be informed about : > > If used the following command: > mydata.loglm <- loglm(n~ s+c+m, mydata) > > I got an the following error : Error in get(x, envir, mode, inherits) : > variable "FUN" was not found > > traceback() > [1] "get(as.character(f), mode = \"function\")" > [2] "match.fun(FUN)" > [3]...

Hi all: For stratified count data,how to perform regression analysis? My data: age case oc count 1 1 1 21 1 1 2 26 1 2 1 17 1 2 2 59 2 1 1 18 2 1 2 88 2 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

...circumstances: when the workspace contains a formula object, a terms object, a function, or some other object with a non-NULL environment, which has been created in the environment of a packaged function. In particular, this would not always occur with a packaged model fitting function, e.g. (from ?loglm in MASS) > library(MASS) > minn38a <- array(0, c(3,4,7,2), lapply(minn38[, -5], levels)) > minn38a[data.matrix(minn38[,-5])] <- minn38$f > fm <- loglm(~1 + 2 + 3 + 4, minn38a) > environment(fm$terms) <environment: R_GlobalEnv> in this case because the terms compone...

....ans)) 4: apply(x, margin, sum) 3: margin.table(data, match(varnames, names(dimnames(data)))) 2: mosaicplot.formula(~Hair + Eye + Sex + Hair:Eye, data = HairEyeColor) 1: mosaicplot(~Hair + Eye + Sex + Hair:Eye, data = HairEyeColor) > but this works correctly: > library(MASS) > lm1 <- loglm(~Hair*Eye + Sex, HairEyeColor) > lm1 Call: loglm(formula = ~Hair * Eye + Sex, data = HairEyeColor) Statistics: X^2 df P(> X^2) Likelihood Ratio 29.34982 15 0.01449443 Pearson 28.99286 15 0.01611871 > Can anyone help? -- Michael Friendly fri...

...satisfied", "Little dissatisfied", "Moderately satisfied", "Very satisfied")) dimnames(data.3) <- list.3 ftable(data.3) I am able to obtain Agresti's results for the independence model which assumes the data is nominal, not ordinal, using either glm() or loglm(). library(MASS) options(contrasts=c("contr.sum", "contr.poly")) X <- as.integer(gl(4, 4, 16)) - 1 Y <- as.integer(gl(4, 1, 16)) - 1 data.2 <- data.frame(Freq, X = factor(X), Y = factor(Y)) summary(fm3 <- glm(Freq ~ X + Y, data = data.2, family = poisson())) dumm...

...circumstances: when the workspace contains a formula object, a terms object, a function, or some other object with a non-NULL environment, which has been created in the environment of a packaged function. In particular, this would not always occur with a packaged model fitting function, e.g. (from ?loglm in MASS) > library(MASS) > minn38a <- array(0, c(3,4,7,2), lapply(minn38[, -5], levels)) > minn38a[data.matrix(minn38[,-5])] <- minn38$f > fm <- loglm(~1 + 2 + 3 + 4, minn38a) > environment(fm$terms) <environment: R_GlobalEnv> in this case because the terms compone...

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