similar to: Inefficiency of SAS Programming

I think SAS Macros has capability to call R, and execute it without it being in the picture anywhere. So you can use SAS Macros in a file called R.sas In this you can create a macro called %Describe that can call R , load Hmisc ,run the describe function Note you will need repeated use of %put in this %describe for the mapping to take place Use %INCLUDE to include that file in all SAS

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

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

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 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

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 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,

Hi all, apologies if this is obvious - but I can't see it and would appreciate some quick help! the matrix mhouse is 26x3 and I'm computing odds ratios. The simple code below "should" compute the odds vector for every pair (325) i.e. 26C2 in cols 1 and 2. On the first i=1 outer loop the inner j loop runs from 2 to 26 ok and then I get the error (Error: subscript out of bounds)

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 there a way to associate text descriptions with variables in a data.frame? For example... Let's say that in my data.frame I have a variable named var1. var1 represents the responses to the question "When was the last time you saw your physician?" When I tabulate the variable var1 I'd like the output to be a bit more descriptive and contain the more descriptive

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:

Is there a function in R which performs stepwise estimation in ways similar to SAS/STATA (i.e., allows the analyst to specify the significance levels for removal/addition of terms). I've been asked to evaluate two final models: one resulting from a backwards selection in R (stepAIC) and one resulting from a backwards selection using PROC LOGISTIC in SAS. The final terms are slightly

I'm looking for an R equivalent of Beaton's (1964) Sweep algorithim for partial inversion of a matrix by pivoting. It implemented in SAS/IML as sweep(matrix, indices), described here http://support.sas.com/documentation/cdl/en/imlug/59656/HTML/default/langref_sect266.htm and here for python http://adorio-research.org/wordpress/?p=262 -- Michael Friendly Email: friendly AT yorku