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Hi, I'm using a fairly simple HP Compaq desktop PC running Windows 2K. When running a large process in R, the process "RGUI.exe" will never exceed 50% of the CPU usage. The program used to be able to use more of the computer, but does not now. I don't believe this is a multiple processor machine. Can anyone give any advice on how to solve the problem? Thanks, Michael

...not call polyroot in such way, because there is a need to call polyroot(c(0,0,fit$coefficients[1],0,fit$coefficients[2]). Is there any method to do it automagically? I would like to write small function solving polynomial optimized by stepAIC, regardless of missing terms. Sincerely -- Lukasz Komsta Department of Medicinal Chemistry Medical University of Lublin Jaczewskiego 4, 20-090 Lublin, Poland Fax +48 81 7425165

Dear useRs, Is there any more elegant way to convert dataframe to a vector of all its values than as.vector(as.matrix(x)) ? I did not have to do such conversion yet, so I am not sure... (of course as.vector() alone does not work). Regards, -- Lukasz Komsta Department of Medicinal Chemistry Medical University of Lublin Jaczewskiego 4, 20-090 Lublin, Poland Fax +48 81 7425165

...ts in groups of two columns - statistic, p-value, statistic, p-value etc. There would be nice to add significance stars, but printCoefmat allows to do it only to last column. Is there any way to do format such table without writing my own complicated function? Thank you in advance, -- Lukasz Komsta Department of Medicinal Chemistry Medical University of Lublin Jaczewskiego 4, 20-090 Lublin, Poland Fax +48 81 7425165

Dear useRs, I have a curve which is a mixture of Gaussian curves (for example UV emission or absorption spectrum). Do you have any suggestions how to implement searching for optimal set of Gaussian peaks to fit the curve? I know that it is very complex problem, but maybe it is a possibility to do it? First supposement is to use a nls() with very large functions, and compare AIC value, but it is

...Error in dixon.test(x) : Sample size must be in range 3-30 > > So it seems that most of the test in the "outliers" package are designed > for small samples. See also the Rnews article published in May 2006 (vol > 6/2) called "processing data for outliers" by Lukasz Komsta (the developer > of the package). > > However there is in that package a function called "scores" which works > for big samples. You can also see the p-values and z scores for the > observations you have and determine which values are considered outliers. > > Try th...

Dear All, I have small samples of data (between 6 and 15) for numerious time series points. I am assuming the data for each time point is normally distributed. The problem is that the data arrvies sporadically and I would like to detect the number of outliers after I have six data points for any time period. Essentially, I would like to detect the number of outliers when I have 6 data points then

...ostino test for skewness These three functions are not enough to make another small package, so I am waiting for ideas about implementing it in some existing package. If there is a need, I will contact maintainer and write manpages with appropriate examples and references. Regards, -- Lukasz Komsta Department of Medicinal Chemistry Medical University of Lublin 6 Chodzki, 20-093 Lublin, Poland Fax +48 81 7425165 Code: agostino.test <- function (x, alternative=c("two.sided","less","greater")) { DNAME <- deparse(substitute(x)) x <- sort(x[compl...

Hi, I need some help using the R outliers package. I would like to perform a Q-test (Dixon test) on my data set. I used the dixon.test function, but I cannot understand what is the confidence level used to perform the test. I have n=101 (n= number of data). So, can I use directly dixon.test ? What about qdixon and qtable functions? thank you so much! -- View this message in context: