similar to: Positive Smoothing in fda package

Hello: What might be the problem generating messages like "Warning: missing text for item ... in \describe" with "R CMD check" and "R CMD install"? With the current version of "fda" on R-Forge, I get the following: Warning: missing text for item 'fd' in \describe Warning: missing text for item 'fdPar' in \describe

Hello: Is there a recommended way to inform "R CMD check" that a function like "as.numeric" is NOT a method for the S3 generic function "as" for objects of class "numeric"? I ask, because I'm getting "NOTE" messages for many function names like this (e.g., "density.fd" in the "fda" package): If there

Hi all, I'm having major issues with smoothing my functional data I'm referring to Jim Ramsay's examples in his books. The following error message keeps appearing, despite all my data being numeric.... can anyone kindly offer any suggestions? isi - vector of argument values - i.e. the independent variable of the curves rlz - data array TMSfdPar - functional data parameter. >

Basically all I want to do is predict a scalar response using some curves. I've got as far as doing a regression (using fRegress from the fda package) but have no idea how to apply the results to a NEW set of curves (for prediction). I have N=536 curves, and 536 scalar responses. Here's what I've done so far: * I've created a basis for the curves. * I've created a

Hello. I am not certain even how to search the archives for this particular question, so if there is an obvious answer, please smack me with a large halibut and send me to the URLs. I have been experimenting with fitting curves by using both maximum likelihood and maximum spacing estimation techniques. Originally, I have been writing distribution-specific functions in 'R' which work

On Jul 24, 2011, at 3:02 AM, Duncan Sands wrote: > A big compile time regression. Any ideas? > > Ciao, Duncan. False alarm. For some reason that I have not yet been able to figure out, these tests run significantly more slowly when I run them during the daytime, which I did for that run. I checked a few of the worst regressions reported here and they all recovered in subsequent

A big compile time regression. Any ideas? Ciao, Duncan. On 22/07/11 19:13, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/253/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 253 0 2011-07-22 16:22:04