search for: agresti

Hello All: I am looking for you help. I am trying to replicate the results of an example found in Alan Agresti's "Categorical Data Analysis" on pages 267-269. The example is one of a 2 x 2 cross-classification table of ordinal counts: job satisfaction and income. I am able to get Agresti's results for the independence model (G^2 = 12.03 with df = 9) assuming as he does that the data is no...

...;presnell at stat.ufl.edu> > Cc: r-devel at r-project.org > Subject: Re: [Rd] Standardized Pearson residuals > > > On Mar 15, 2011, at 04:40 , Brett Presnell wrote: > >>>> Background: I'm currently teaching an undergrad/grad-service course >>>> from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" >>>> and deviance residuals are not used in the text. For now I'll just >>>> provide the students with a simple function to use, but I prefer to >>>> use R's native capabilities whe...

Dear Sirs, Could you please be so kind as to send us some information on residuals in multinomial logistic models? Is it possible to use R software? We thank you in advance. Sincerely yours Luciana Alves,MSc Beatriz Leimann, MD -- Luciana Correia Alves Doutoranda em Sa??de P??blica ENSP - Fiocruz

Hello All: I hope I can get someone interested in this problem: Agresti in "Analysis of Categorical Data," p. 289, applies a "row and column effects model" to analyze a two-dimensional cross-classification of ordinal data. He got his results in either SAS or GLIM. Is there a way to replicate his results with R? He claims the RC model fits well wit...

Dear list members, I want to perform in R the analysis "simultaneous confidence interval for multiple proportions", as illustrated in the article of Agresti et al. (2008) "Simultaneous confidence intervals for comparing binomial parameter", Biometrics 64, 1270-1275. If I am not wrong the R function implementing the Agresti et al. method is prop.test(). I ask an help because I have some difficulties in reading the output of that function. As...

Dear All, We are about to purchase the second edition of Agresti's "Categorical Data Analysis" (my old copy of the first ed. of that wonderful book is falling apart). I would appreciate suggestions about other comparable books which, if possible, have examples using R/S code (instead of SAS). Thanks, Ram?n -- Ram?n D?az-Uriarte Bioinformati...

...2 is different for the 2 conditions A and B. I guess I could also state this as looking for an effect at the highest stratum, after correctly pooling over all the lower strata... i.e. Is there an "interaction" between the Effect of Condition and the Effect of Test. I looked through Agresti and Pinheiro/Bates and couldn't find an example covering this situation. I would be really grateful if you could suggest a way to go about this analysis in R, or a place where I could read about this. I considered: Pool data from all the sessions for a given condition and test together,...

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" and deviance residuals are not used in the text. For now I'll just provide the students with a simple function to use, but I prefer to use R's native capabilities whenever possible. I think something along the following...

...values and noticed that using the 'Wilson' method when alpha is larger (i.e. narrow CI), results in the upper value being smaller than the lower value. The 'exact' and 'asymptotic' methods give results in the realm I'd expect. But the help file suggests: "Following Agresti and Coull, the Wilson interval is to be preferred and so is the default." Suggestions and clarifications gratefully received. The following code shows the curious results: #calc 5% CI's. require(Hmisc) alpha <-.95 bin.prob <- binconf(1, 100, alpha=alpha,method='all') colna...

...that my way of preparing a dataframe to pass to lmer will make them think that R is a very difficult and un-natural language. Here is for example a simple data set about approval ratings on two different surveys for a random sample of 1600 individuals. > ## Example: Ratings of prime minister (Agresti, Table 12.1, p.494) > rating <- matrix(c(794, 86, 150, 570), 2, 2) > dimnames(rating) <- list(First = c("approve", "disapprove"), + Second = c("approve", "disapprove")) > rating Second First approve d...

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 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe"...

Dear R-list, Hi everyone, Im trying to make an analysis of multidimensional contingency tables using R. I' working with the Agresti example where you have the data from 3 categories. The thing is how can I do the analisys using the G2 statistics. Somebody can send me an Idea? I attach the program where you can find the data. Best Regards, > prob1<- data.frame(victim=c(rep('white',4),rep('black',4)), + p...

Hello, I am searching for an R-Package that does an exentsion of the Mantel-Haenszel test for ordinal data as described in Liu and Agresti (1996) "A Mantel-Haenszel type inference for cummulative odds ratios". in Biometrics. I see packages such as Epi that perform it for binary data and derives a varaince for it using the Robbins and Breslow variance method. As well as another pacakge that derives it for nominal variables bu...

Hello, I am looking at the job satisfaction data below, from a problem in Agresti's book, and I am not sure where the degrees of freedom come from. The way I am fitting a binomial model, I have 168 observations, so in my understanding that should also be the number of fitted parameters in the saturated model. Since I have one intercept parameter, I was thinking to get 167 df...

...I"=fisher.test(x,alt="greater")$conf.int) # [,1] [,2] #two.sided.95CI 0.2985103 Inf #greater.95CI 0.4625314 Inf Using the noncentral hypergeometric distribution, we can calculate the probability mass of each possible table with same marginals as x. Ref.: Alan Agresti (1990). Categorical data analysis. New York: Wiley. Page 67. Hence, the result below suggests that the two-sided confidence interval has a confidence level of 97.5% as opposed to 95%. n11 <- 7:9 theta <- 0.2985103 choose(9,n11)*choose(15-9,13-n11)*theta^n11/ sum(choose(9,n11)*choose(15-...

I've noticed that glm and lrm give extremely different results if you attempt to fit a saturated model to a dataset with zero cells. Consider, for instance the data from, Agresti's Death Penalty example [0]. The crosstab table is: , , PENALTY = NO VIC DEF BLACK WHITE BLACK 97 52 WHITE 9 132 , , PENALTY = YES VIC DEF BLACK WHITE BLACK 6 11 WHITE 0 19 Regression with an unsaturated model produces essentially t...

Hi: I have asked this question on Cross-Validated. So it might be a cross posting but havent received any responses to it. I am trying to see which distribution will best fit the data I am working on. The dataset is as following: Site Nausea headache Abdominal Distension 1 17 5 10 2 12

If I do not make a mistake,the partial association model is an extension of log-linear model.I read a papers which gives an example of it.(Sloane and Morgan,1996,An Introduction to Categorical Data Analysis,Annual Review of Sociology.22:351-375) Can R fit such partial association model? ps:Another somewhat off-topic question.What's the motivations to use log-linear model?Or why use

...ihood approach the deviance does not have the interpretation as a likelihood-based measure of sample information. Functions such as stepAIC() cannot be used. The function add1() returns the change in the scaled deviance between the null model and the alternative model along with a chi-sq p-value. Agresti, (Categorical Data Analysis p.140 last sentence before Section 4.5.2), makes the statement "For some GLMs the scaled deviance has an approximate chi-squared distribution." with no details, no references, and no degrees of freedom. Does anyone know: 1) How the p-value is computed in add1...

Does anybody know if there is an implementation of Goodman-Kruskal lambda measures of association in R? Also, how we can analyze ordered contingency tables and compute the relative measures of associations in R? Thank in advance --------------------------------- [[alternative HTML version deleted]]