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Hi all, simple Q: how do I extract the upper and lower CI for predicted probabilities directly for a glm - I'm sure there's a one line to do it but I can't find it. the predicted values I get with the predict (.. "response") Thanks Gerard ********************************************************************************** The information transmitted is intended only for

Dear All, 1. Does anyone have experience of running R on a server inside a Citrix shell - I'd like to get R onto the server and would be greatful for any tips or direction on the matter. 2. This may seem like a silly question so forgive my ignornace. Most of the data I currently work with is held on a number of Lotus Notes (LN) Databases (well it's called a DB here but it's really a

Hi all, I have a multiple queing situation I'd like to simulate to get some idea of the distributions - waiting times and allocations etc. Does R has a package available for this - many years ago there used to be a language called "simscript" for discrete event simulation and I was wondering if R has an equivalent (or hopefully with graphics, something better!). Apologies if there

Dear all, I've replicated the cheese tasting example on p175 of GLM's by McCullagh and Nelder. This is a 4 treatment (rows) by 9 ordinal response (cols) table. Here's my simple code: #### cheese library(MASS) options(contrasts = c("contr.treatment", "contr.poly")) y = c(0,0, 1, 7, 8,8,19, 8,1, 6,9,12,11, 7,6, 1, 0,0, 1,1, 6, 8,23,7,

Dear all, The standard call to ARIMA in the base package such as arima(y,c(5,0,0),include.mean=FALSE) gives a full 5th order lag polynomial model with for example coeffs Coefficients: ar1 ar2 ar3 ar4 ar5 0.4715 0.067 -0.1772 0.0256 -0.2550 s.e. 0.1421 0.158 0.1569 0.1602 0.1469 Is it possible (I doubt it but am

Hi everyone, newbee query! I've installed R 2.8.0 and tried to run this simple glm - x is no of cars in a given year, y is the number voted in an election that year while n is the population 18+: votes <- data.frame(x = c(0.62,0.77,0.71,0.74,0.77,0.86,1.13,1.44), + y=c(502,542,711,653,771,806,934,1123), n=

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(minn38,

dhabby wrote: Last week I run in to a lot a problems triyng to fit an ARIMA model to a time series. The problem is that the internal process of the arima function call function "optim" to estimate the model parameters, so far so good... but my data presents a problem with the default method "BFGS" of the optim function, the output error looks like this:

HI, I'd like to perform a weighted linear least squares fit with R on data with varying errors on both vectors. I can do this with one axis using lm, but have no idea where to go from here. I've tried googling, but no idea. Any suggestions? Thanks, James

Dear List, Please find a frank interview with Phil Rack, creator of Bridge to R ( from both SAS and WPS interfaces). For those unaware of WPS- it is basically a SAS language compiler (read SAS code,writes SAS code,Reads and writes SAS datasets) ,priced at 660 $ a licence ( or estimated 10 times cheaper than Base SAS. The UK based WPC held, WPS doesnt have advanced statistical facilities like

What function and package I use to conduct ordinal regression?? My data is composed 2colums and 180rows. The first colum indicate level of mass and second colum is intensity. So, I want to calculate how much intensity are related mass. [[alternative HTML version deleted]]

Hi! I have two time series. Both measure the same thing and I would like to determine which one is noisier. Would it be a good measure of the noise in each time series the absolute lag difference? Is this a good measure? Any other measure I could use? Thanks for help :) David Riano Center for Spatial Technologies and Remote Sensing (CSTARS) University of California 250-N, The Barn One Shields

Hi list, I would like to normalize a matrix (two actually for comparison) using iterative proportional fitting. Using ipf() would be the easiest way to do this, however I can't get my head around the use of the function. More specifically, the margins settings... for a matrix: mat <- matrix(c(65,4,22,24,6,81,5,8,0,11,85,19,4,7,3,90),4,4) using fit <-

Is Pearson's Chi-Square test for contingency tables asymptotically unbiased for large tables (large degrees of freedom) regardless of the expected values in each cell? The rule of thumb is that Pearson's Chi-square should not be used when large numbers of cells have expected values < 5. However, I compared the results on 4x4 contingency tables for R's chisq.test using chi-square

Dear R experts and statisticians, I have some time series datasets, they are several years vegetation indices (about 50 data points per year) sampled from different station. These indices have similar dynamics with seasonal change. My questions are, 1) How can I compare the difference among the indices, and how can I say there is significant differnce between two time series. They

Dear list fellows, I want to study time series and use R to analyse time series of fishing data from several species (landings and cpue) investigating the correlation between them and with environmental factors (water temperature, wind, etc.). Searching at Amazon I found three books with examples in R: Time Series Analysis: With Applications in R by Jonathan D. Cryer and Jonathan D. Cryer

Hello, Anyone who knows a fast and accurate algorithm for generating draws from an underdispersed Poisson distribution. Or even better, if there is a package containing such an implementation. Thanks Rene

Hello, Guys: I'm from China, my English is poor and I'm new to R. The first message I sent to R help meets some problems, so I send again. Hope that I can get useful suggestions from you warm-hearted guys. Thanks. I builded a multiplicative seasonal ARMA model to a series named "cDownRange". And the order is (1,1)*(0,1)45 The regular AR=1; regular MA=1; seasonal AR=0; seasonal

I am a beginner in using R and I need help in the interpretation of AR result by R. I used 12 observations for my AR(2) model and it turned out the intercept showed 5.23 while first and second AR coefficients showed 0.40 and 0.46. It is because my raw data are in million so it seems the intercept is too small and it doesn't make sense. Did i make any mistake in my code? My code is as follows:

Dear all, I have the following dataset: each row corresponds to count of forest floor small mammal captured in a plot and vegetation characteristics measured at that plot > sotr plot cnt herbc herbht 1 1A1 0 37.08 53.54 2 1A3 1 36.27 26.67 3 1A5 0 32.50 30.62 4 1A7 0 56.54 45.63 5 1B2 0 41.66 38.13 6 1B4 0 32.08 37.79 7 1B6 0 33.71 30.62