similar to: 2x2 Contingency table with much sampling zeroes

Hi, I'm using a non linear model to fit experimental survival curves. This model describes the fraction of "still active" experiments as a function of time t as follows: f(t)=(1+exp(-etaD*cD)) / (1+exp(etaD(t-cD))) Moreover, when experiments are still active, they may change of state (from 0 to 1). But they may fall inactive before changing their state (their state still

Oups, I sent the email by error, as I was still writing my reply… Spencer, Le 22-avr.-09 à 03:33, spencerg a écrit : > Is your first model a special case of the second with eta1 = 0? > If yes, what about using 2*log(likelihood ratio) being approximately > chi-square? Yes, the first model is a special case of the second with eta1=0… Could you give me more explanation about

Hi, my question is not exactly about R... What I am looking for are hints and directions on suitable methods (available in R or elsewhere) to solve a grouping (or pattern recognition) problem of environmental features in an environmental gradient as described below. Given environmental sampling data set (Depth, Presence of sand, Presence of boulders, Presence of clay). 1 1 1 0 1 1 0 0 1 1

Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope coefficients and sb1,b2 the pooled standard error of the slope (b) which can be calculated in R this way: > df1 <-

Hi, there I'm trying to get the value of the Mean Square from the ANOVA model summary that comes from specifying the error term, and am wondering if one can actually do this ( I know it's possible when using anova(lm) objects and the like, but I'm having a tough time with it under this framework). There does appear to be some indexing in the output of this type, but perhaps not

Hi, A have small technical question about the calculation of R-squared using lm(). In a study case with experimental values, it seems more logical to force the regression line to pass through origin with lm(y ~ x +0). However, R-squared values are higher in this case than when I compute the linear regression with lm(y ~ x). It seems to be surprising to me: is this result normal ? Is there

I'm just getting to grips with using ode function and have used the examples and vignettes to produce a small model of a one-pool, michaelis-menten, enzyme kinetic reaction. The rate of flux of substrate into pool A is constant (fluxoa) however the rate of flux out of pool A is controlled by the HMM equation (v = Vmax/ ( 1 + (Km / Concentration A )) ). This function works fine and

Hi all, I have a very easy questions (I hope). I had measure a property of plants, growing in three different substrates (A, B and C). The rest of the conditions remained constant. There was very high variation on the results. I want to do address, whether there is any difference in the response (my measurement) from substrate to substrate?

Hello, I'm planning to start using R. Before getting into it, I'd like to ask a couple of questions. Does R carry out loglinear model analysis? That is, will it provide the chi-squared goodness of fit test statistic for a given hierarchical loglinear model? Maybe even do a model selection procedure (like Brown's two-step procedure, or forward/backward selection)? Thanks

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

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

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

Hi,Dear all R experts, I am trying to do the 2-way contingency table analysis by fitting the loglinear models. However, I found my table has several empty cells which are theoretically missing values.I have no idea of how to solve them coz we cannot compute the simulated p-value with zero marginals.Does someone have some suggestions? Please help me out, thanks a lot! Cheers, Yan

Hi there, I am fairly new to R and I have been trying to figure out how to separate a column of substrate data that I have into separate parts using R. An example of my data looks like this is: gmtTime Classification ClassValue Species Count 1449 Sand 30 1449 Mud 70 Brittle Star 1 1449 Mud 70

Dear List: Most of the analysis I do involves contingency tables. I am migrating to R from Stata and I have a number of questions about using contingency tables in R. I suspect that most of the things I want to do are very short R scripts that people on this list probably have. I wonder if you would be willing to share them. First, the presentation of tables by table() is not

Hello everybody, Can anyone tell me how I could analyze data that are at a contigency table form? I already found function cfa in the cfa package but I still don't understand how I could use this function in order to elaborate a contigency table. Every answer is welcome! --------------------------------- ÁðïêôÞóôå ôçí äùñåÜí óáò@yahoo.gr [[alternative HTML version deleted]]

UseRs, I am working on a dataset (see small example below) where individuals were followed on a specific date-time combo and multiple repeated measurements were taken (e.g., height in meters, behavior class in 2 letter code). Observation numbers varied between individual (ranging from 1 observation for each date-time combo to >50) I am trying to summarize the data into 1 row per

Hi all; I'm trying to display a 2 panel plot for the Puromycin data from R with 2 different non-linear models fitted to each group. The problem is that as far as I know panel.number doesn't work in the latest version of R. Can anyone give a hint how to solve this? Here is the code that I used before and now doesn't work xyplot(rate ~conc| state,Puromycin,

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

1) deleting path/to/(Something) Word Word Anotherword--Word Word.pdf IO error encountered -- skipping file deletion I think either the -- is causing it to fail, or more likely, the next filename, which it didn't bother to print out, which has an ampersand in the name, causes it to fail. I am using SSH as a substrate, of course, and I think you guys failed to escape things, so the ampersand