similar to: Friedman

All, I would like to apply a set of rules to each row of the sample data set below. The rule sets are the guidelines for determining an individual's date for retirement eligibility. The rules are found in this document, http://www.opm.gov/feddata/RetirementPaperFinal_v4.pdf. I am only interested in the top two categories for retirement eligibility, the CSRS and FERS plans. The data set has

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

Hello, Using the following lines of code, I created the following graph:

Hello, Using the following lines of code, I created the following graph:

> Date: Tuesday, January 31, 2017 18:16:36 -0500 > From: TE Dukes <tdukes at palmettoshopper.com> > >> From: CentOS [mailto:centos-bounces at centos.org] On Behalf Of >> Richard Sent: Tuesday, January 31, 2017 5:19 PM >> >> >> > Date: Tuesday, January 31, 2017 16:59:32 -0500 >> > From: TE Dukes <tdukes at palmettoshopper.com>

> Date: Tuesday, January 31, 2017 19:35:46 -0500 > From: TE Dukes <tdukes at palmettoshopper.com> > >> From: CentOS [mailto:centos-bounces at centos.org] On Behalf Of >> Richard Sent: Tuesday, January 31, 2017 6:46 PM >> >> > Date: Tuesday, January 31, 2017 18:16:36 -0500 >> > From: TE Dukes <tdukes at palmettoshopper.com> >> >

Hello everybody. In my case, calculating multiple comparisons (Tukey) after lmer produced the following two errors: > sv.mc <- glht(model.sv,linfct=mcp(comp="Tukey")) Error in x$terms : $ operator not defined for this S4 class Error in factor_contrasts(model) : no 'model.matrix' method for 'model' found! What I have done before: > sv.growth <-

The current llvm/llvm-gcc-4.2 2.6 branch passes all of the Polyhedron 2005 benchmarks built with its gfortran. The results compare as follows... Compile Command : gfortran -ffast-math -funroll-loops -msse3 -O3 %n.f90 -o %n benchmark gcc-4.2.4 llvm-gcc-svn llvm-gcc-2.6 llvm-gcc-2.6 at -m32 20081031 -m32 at -m32 at -m64 ac 18.30

Hello! I often plot average of y instead of all y values, since one can easily see the trend if there is to many points and/or x might be like 1, 2, 3, 4, 5, ... and you might get a cloud (which can also be informative) and/or columns of points. Anyway, learning with R i get stucked. I have the data in table burnins1, with the following names: names(burnins1) [1] "Model"

Sorry bit of a Newbie question, and I promise I have searched the forum already, but I'm getting a bit desperate! I have over-dispersed, zero inflated data, with variance greater than the mean, suggesting Zero-Inflated Negative Binomial - which I attempted in R with the pscl package suggested on http://www.ats.ucla.edu/stat/R/dae/zinbreg.htm However my data is non-integer with some pesky

Dear R experts, For quite some time I have been trying to solve a mistery of reading a seemingly trouble-free text file. The data is temperature reconstruction arranged as a huge grid, preceded by seven "header lines" (which you see better if file is opened in Firefox or Chrome). This is the data (gridded temperature reconstruction)

Since the fact that gfortran performance has improved over the major releases, I decided to benchmark the current releases on a MacPro with the Polyhedron 2005 benchmarks using -ffast-math -funroll-loops -msse3 -O3. The results are... gcc release gcc 4.2.4 gcc 4.3.3 gcc 4.4-pre gcc 4.3.3/ gcc 4.4-pre/

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Dear all i use R only a few days and don't understand the difference between fivenum(x) und summary(x). > x [1] 20.77 22.56 22.71 22.99 26.39 27.08 27.32 27.33 27.57 27.81 28.69 29.36 [13] 30.25 31.89 32.88 33.23 33.28 33.40 33.52 33.83 33.95 34.82 > fivenum(x) [1] 20.770 27.080 29.025 33.280 34.820 > summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. 20.77 27.14

Duncan, With the commit from http://lists.cs.uiuc.edu/pipermail/llvm-commits/Week-of-Mon-20121203/158488.html, the Polyhedron 2005 benchmarks complete again on x86_64-apple-darwin12. The result are similar to what were seen with FSF gcc 4.6.2svn and llvm/dragonegg 3.0 (which was the last release that passed pb05) http://lists.cs.uiuc.edu/pipermail/llvmdev/2011-October/044091.html. Jack

Ralf, et al., Attached is the latest version of my autovectorization patch. llvmdev has been CC'd (as had been suggested to me); this e-mail contains additional benchmark results. First, these are preliminary results because I did not do the things necessary to make them real (explicitly quiet the machine, bind the processes to one cpu, etc.). But they should be good enough for discussion.

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

Hi, I'm a new user of R and I'm trying to make a linear model from this kind of dataset x [1] 16.87 19.93 25.85 20.94 17.06 19.49 19.93 25.45 27.74 20.15 25.81 21.06 17.17 20.03 25.50 27.79 20.44 16.88 19.93 25.79 z<-x-10 y [1] 0.80 1.27 2.22 1.32 0.90 1.18 1.84 2.41 2.97 1.25 2.07 1.41 1.14 1.66 2.59 3.51 1.53 0.81 1.26 2.30 plot(x,y) I want to be able to force the line of