similar to: 2 x 2 x 10 x 2 binomial setup

-- Error in binomial(link = "identity") : identity link not available for binomial family, available links are "logit", "probbit", "cloglog" and "log" Hi, I have a question, dealing with this error response. I'm trying to make anova on percentages. The variablethat has a biological significance is actually the percentage itself. Is it

Karl, If you use one of the specialized packages to calculate your ICC, make sure that you know what you're getting. (I haven't checked the packages out myself, so I don't know either.) You might want to read David Futrell's article in the May 1995 issue of Quality Progress where he describes six different ways to calculate ICCs from the same data set, all with different

Hi group, I found a module for adaptive kernel density estimation for Stata users, but unfortunetly I don't have access to Stata, can I find a similar approach using R? Thank u so much 4 ur time. [[alternative HTML version deleted]]

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 for the Null

Hi, I have a question that I have not been succesful in finding a definitive answer to; and I was hoping someone here could give me some pointers to the right place in the literature. A. We have 4 sets of data, A(t), B(t), C(t), and D(t). Each of these consists of a series of counts obtained in sequential time-intervals: so for example, A(t) would be something like: Count A(t): 25,

Dear list members, I am trying to understand this output from the smoothing package locfit (1.5-4, running on R 2.8.1 on Windows Vista 64 bit). # sample code x<-1:100 y<-rnorm(100) fit<-locfit(y~x,family="gaussian") #default parameters are fine plot(fit,band="global") #plot seems "reasonable", confidence bands use a global estimate of variance

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

Hi im running several gentoo servers with Asterisk, only using IAX2 and SIP. Recently we decided to implement h323. All the necessary dependences for oh323-0.7.3 were installed by portage (package manager of Gentoo distro), including openh323, pwlib etc. The module is successfully loaded (load chan_oh323.so) but when asterisk is stopped (stop now) or the oh323 module is unloaded (unload

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 with G^2(RC) = 3.57 with df =

Hello, I am working on some examples of GLMM for my students but I am afraid 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,

Hi Peter and others, If it helps, I wrote a small function glm.scoretest() for the statmod package on CRAN to compute score tests from glm fits. The score test for adding a covariate, or any set of covariates, can be extracted very neatly from the standard glm output, although you probably already know that. Regards Gordon --------------------------------------------- Professor Gordon K

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<-

Hello all, I've been using binconf (package Hmisc) at a range of alpha 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

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

Dear R Help Community, I have a question and an answer (based on reading this forum and online research), but I though I should share both since probably there's a much better way to go about my solution. My question is specifically about how to best visualise multiple response contingency tables. What I mean by 'multiple response' is that the total number of responses per row of a

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

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

The problem occurs when the sample odds ratio is Inf, such as in the following example. Given the fact that both upper bounds of the two 95% confidence intervals are Inf, I would have expected that the two lower bounds be equal, but they aren't. x <- matrix(c(9,4,0,2),2,2) x # [,1] [,2] #[1,] 9 0 #[2,] 4 2 rbind("two.sided.95CI"=fisher.test(x)$conf.int,

Hi. I'm trying to create an Agresti-Coull confidence interval without using the binom package. Despite many trials, I keep getting the same problem- see below. > y=334 > n=1160 > alpha=.05 > b=(y+.5*qnorm(1-alpha/2)**2)/(n+qnorm(1-alpha/2)**2) > b [1] 0.288631 > ac=b+qnorm(1-alpha/2)*sqrt(b(1-b)/(n+qnorm(1-alpha/2)**2)) Error: could not find function "b" What am I

Hello, I have a question about the add1() function and quasilikelihoods for GLMs. I am fitting quasi-Poisson models using glm(, family = quasipoisson). Technically, with the quasilikelihood 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