I'm overwhelmed. Questioners seem to get quicker, more informative responses to questions that are short, well written and easily and quickly understood, and preferably include a toy example that someone else can run quickly to reproduce the problem and to evaluate alternative solutions.
I see you are using "source" and would like to get more output. Did you check "?source"? This function in R has arguments "echo" and "verbose", which may produce what you want. If you've already tried that, then the problem may be output buffering, and I don't know how to modify that parameter. A search of "www.r-project.org" -> search -> "R Site Search" might help. Or ask a question focused specifically on that, giving also which version of R you are using under which operating system.
I rarely use "source". More often, I have R commands in another file and copy and paste into R the commands I want to run. That makes it easier for me to isolate errors, etc.
This does not address all your questions, but it's a start.
Hope this helps. spencer graves
Bruce Moore wrote:
I'm writing a program to perform linear regressions to estimate the number of bank teller transactions per hour of various types based upon day of week, time of day, week of month and several prices. I've got about 25,000 records in my dataset, 85 columns of transaction counts (used 1 at a time), about 50 columns of binary indicators (day, week, pay period, hour, branch), and a half dozen real valued prices.
My program hangs on some regressions as I add interactions, probably due to logic problems in my code or collinearity problems in the data.
1) I'm running my program via the source() command. It appears that source() does not print any messages
until it completes.
---->Is there a way to get diagnostic messages to print immediately rather than when the source() command has completed?
2) I'm fairly certain that I've got some collinearity
in the data set and the interactions. I've found an
append (Ott Toomet 5/30/2003) that talks about a
procedure to find collinearity problems using
model.matrix() to generate the dataset with
interactions and kappa() to determine the condition
number of the matrix.
---->Is there a more automated way to find collinear variables?
3) Is there a way to get lm() and/or step() or some other package to give a model with only coefficients that are significant at a particular level?
4) Is there a way to suppress display of a password when using the RODBC odbcConnect() function, or to get the function to prompt for a password?
5) What is the practical size limit on the number of terms in model? I know that I won't be able to consider all interactions, but would like to have some idea when to give up and go with what I've got.
===== Bruce Moore
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