search for: rbar

I'm testing some modifications in arima.c. I've noticed that a big internal array of double (rbar) is usually sparse and I'd like to add an option to store it as key-value mapping. Is there a library function or some other approach already used inside the R core for key-value mappings? Thanks, Matteo Bertini

Dear all, I like the way the Rcmdr package computes reliability. E.g reliability(cov(d[,c("q1", "q2", "q3", "q4", "q5", "q6")], use="complete.obs")) will not only give me the alpha score, but also for each variable, alpha.score if deleted. However - when writing scripts it's very tiresome to load the whole Rcmdr GUI just

I guess this is more r-devel than r-help. Note, I am just the messenger - I have no idea what the user is trying to model here. arima() crashes R (segfault) with Linux R-2.7.0, Solaris R-2.6.0: *** caught segfault *** address 42400000, cause 'memory not mapped' Traceback: 1: .Call(R_getQ0, phi, theta) 2: makeARIMA(trarma[[1]], trarma[[2]], Delta, kappa) 3: arima(x, c(1, 0, 1), c(1,

I have windows version of R v1.6.2 I have downloaded the qtoolbox.zip from http://www.cmis.csiro.au/S-PLUS/qtoolbox/ My R command line is > install.packages("C:/Program Files/R/qtoolbox.zip", .libPaths()[1], CRAN = NULL) and I get the following error message: updating HTML package descriptions Warning message: error -1 in extracting from zip file > Is there another way to

...irc.summary and circ.disp seems to conclude that the dimension is 1. I am new to R so maybe I'm just missing something. > dim(data) [1] 67 1 > circ.mean(data) [1] 0.2200071 > circ.summary(data) n mean.dir rho 1 1 0.2200071 43.12534 > circ.disp(data) n r rbar var 1 1 43.12534 43.12534 -42.12534 > -- Robert Mark, Ph.D. rmark at infomagic.net Rupestrian CyberServices Phone/FAX 928-526-3625 3644 N. Stone Crest St. http://www.rupestrian.com/ Flagstaff, AZ 86004-6811 USA RCS: Computer science in the servic...

Dear R-Community, I'm currently trying to find a way to conduct a meta-analysis in R. I would like to analyze data from mostly-cross-sectional survey-studies. The effect sizes would be correlations. The R packages "meta" and "rmeta" are, as far as I can see, set up for analysis with effect sizes for differences (i.e. comparison of the means/odds-ratios of experimental