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Try something like with(df, predict(smooth.spline(x = altitude, y = atm_values), deriv = 1)) Cheers, Andrew -- Andrew Robinson Chief Executive Officer, CEBRA and Professor of Biosecurity, School/s of BioSciences and Mathematics & Statistics University of Melbourne, VIC 3010 Australia Tel: (+61) 0403 138 955 Email: apro at unimelb.edu.au Website: https://researchers.ms.unimelb.edu.au/~apro at unimelb/ I acknowledge the Traditional Owners of the land...

Not a direct answer but you may find lm.fit worth experimenting with. Also try the high-performance computing task view on CRAN Cheers, Andrew -- Andrew Robinson Chief Executive Officer, CEBRA and Professor of Biosecurity, School/s of BioSciences and Mathematics & Statistics University of Melbourne, VIC 3010 Australia Tel: (+61) 0403 138 955 Email: apro at unimelb.edu.au Website: https://researchers.ms.unimelb.edu.au/~apro at unimelb/ I acknowledge the Traditional Owners of the land...

...r timing and after which png("lmfit-ex.png") boxplot(mb, notch=TRUE) dev.off() produces the attached nice image. > Also try the high-performance computing task view on CRAN > Cheers, > Andrew > -- > Andrew Robinson Chief Executive Officer, CEBRA and > Professor of Biosecurity, School/s of BioSciences and > Mathematics & Statistics University of Melbourne, VIC 3010 > Australia Tel: (+61) 0403 138 955 Email: > apro at unimelb.edu.au Website: > https://researchers.ms.unimelb.edu.au/~apro at unimelb/...

...t;) > boxplot(mb, notch=TRUE) > dev.off() > > produces the attached nice image. > > > Also try the high-performance computing task view on CRAN > > > Cheers, > > Andrew > > > -- > > Andrew Robinson Chief Executive Officer, CEBRA and > > Professor of Biosecurity, School/s of BioSciences and > > Mathematics & Statistics University of Melbourne, VIC 3010 > > Australia Tel: (+61) 0403 138 955 Email: > > apro at unimelb.edu.au Website: > > https://researchers.ms.unimelb.edu....

I have found more general questions, but I have a specific one. I have a few million (independent) short regressions that I would like to run (each reg has about 60 observations, though they can have missing observations [yikes]). So, I would like to be running as many `lm` and `coef(lm)` in parallel as possible. my hardware is Mac, with nice GPUs and integrated memory --- and so far completely

Hi everyone, I have a vector with atmospheric measurements (x-axis) that is obtained/calculated at different altitudes (y-axis). The altitude is uniformly distributed every 7 meters. For example my dataframe is: df <- dataframe( *altitude* = c(1005, 1012, 1019, 1026, 1033, 1040, 1047, 1054, 1061, 1068), *atm_values* = c(1.41, 1.40, 1.39, 1.38, 1.37, 1.37, 1.38, 1.36, 1.33, 1.31)

Dear R developers, This is a rather peculiar question, but nevertheless I would still need an answer for. It is about an R package which I created (namely QCA), and from versions 1.0-0 to 1.1-4 I had a co-author. The co-author recently withdrawn from the package development, but still requires to be left in the authors list and be cited for the package in the CITATION file. Obviously, one could

...egulation I might not be aware of. >> >> Best wishes, >> Adrian >> >> >> > ______________________________________________ > R-devel at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel > -- Andrew Robinson Deputy Director, CEBRA, School of Biosciences Reader & Associate Professor in Applied Statistics Tel: (+61) 0403 138 955 School of Mathematics and Statistics Fax: +61-3-8344 4599 University of Melbourne, VIC 3010 Australia Email: a.robinson at ms.unimelb.edu.au Website: http://www.ms.unimelb.e...