[R] proj() function for lm objects

explanation, preferable containing some examples of the calculations involved ? More particularly, I am interested in the relation between the coefficients produced by the 'coef()' function on an 'lm' object, and the corresponding projections. regards, Piet -- Piet van Remortel Intelligent

[R] disregarded projections warning when fitting lm model

anybody clarify this ? I don't seem to find any pointer to what this might mean. Too many/little data points ? too many terms in the model ? thanks Piet -- Piet van Remortel Intelligent Systems Lab University of Antwerp Belgium http://www.islab.ua.ac.be

[R] matching vectors against vectors

Hi all. I have a re-occuring typical problem that I don't know how to solve efficiently. The situation is the following: I have a number of data-sets (A,B,C,...) , consisting of an identifier (e.g. 11,12,13,...,20) and a measurement (e.g. in the range 100-120). I want to compile a large

[R] for-loop with multiple variables changing

Hi all, Never really managed to build a for-loop with multiple running variables in an elegant way. Can anybody hint ? See below for an example of what I would like. EXAMPLE a-c(1,2,3) b-c(name1,name2,name3) for( number in a, name in b ) { print( number ) ##take a value

[R] large fonts on plots

compared to the entire plot. I am sure it is pretty simple, can anybody give me a hint ? Please reply to: [EMAIL PROTECTED] Thanks, Piet -- Piet van Remortel PhD Student [EMAIL PROTECTED] http://como.vub.ac.be/Members/Piet.htm __ [EMAIL PROTECTED

[R] tics and grids

Hi, Whats the easiest way to set a desired interval for tics on axes in R ? And will the 'grid' command put gridlines on all tics automatically ? I tend to get stuck with graphs with ranges 0-1 with only 2 tics and 2 gridlines in the 0-1 range, while I would like 10 tics (or every 0.1) tnx,

[R] data storage/cubes and pointers in R

Hi all, I am faced with the situation where I want to store/analyze relatively large, organized sets of numerical data, which depend on a number of conditions (biological properties, exposure times, concentrations etc etc). Imagine about a hundred dataframes of a few thousand numerical