search for: 0.0072

I want to get access to the code of an S4 method in order to possibly modify a function to accomplish my particular needs: in my case the function in is cenfit() from the package NADA Now, given my reproducible example: my.ex<-structure(list(TEC = c(0.21, 0.077, 0.06, 0.033, 0.014, 0.0072), LR = c(0L, 0L, 1L, 0L, 1L, 0L)), .Names = c("TEC", "LR"), class =

Hi r-sers, I have a data of relative frequencies for the interval of 0-20, 20-40,...380-400.  I would like the two data on the same graph using the same x-axis label.  My question is how to get a smooth curve using kernel density code if it possible for this data.   > cbind(rel_obs,rel_gen)           rel_obs rel_gen  [1,] 0.000000000  0.0000  [2,] 0.092534175  0.0712  [3,] 0.105152471  0.1092

Dear R contributors, I have a problem in selecting in an efficient way, rows of a data frame according to a condition, which is a part of a row name of the table. The data frame is made of 64 rows and 2 columns, but the row names are very long but I need to select them according to a small part of it and perform calculations on the subsets. This is the example: X Y "Unique to

Hello at all, working with a dataset I try to optimize a non-linear function with constraint. test<-read.csv2("C:/Users/Herb/Desktop/Opti/NORM.csv") fkt<- function(x){ a<-c(0) s<-c(0) #Minimizing square error for(j in 1:107){ s<-(test[j,2] - (x[1] * test[j,3]) - (x[2] * test[j,4]) - (x[3]*test[j,5]) - (x[4]*test[j,6]) - (x[5]*test[j,7]))^2 a<- a+s} a<-as.double(a)

Hello again, everytime I think I got something to work, the next issue comes up... I have the following data.frame, I want to visualize: > data_rb tld spam1 spam2 share 1 ca 826436 73452 0.0889 2 org 470550 25740 0.0547 3 de 156042 15531 0.0995 4 com 140753 7527 0.0535 5 edu 34845 2507 0.0719 6 net 12781 382 0.0299 7 ru 7648 18 0.0024

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

All, Given the following commands: > ag.m35<-read.table("m35.txt",header=TRUE,sep=",") > ag.m35.lp<-subset(ag.m35, race=="lp") > aov.m35.lp=aov(year~time,data=ag.m35.lp) > anova.m35.lp=anova(aov.m35.lp) > anova.m35.lp Analysis of Variance Table Response: year Df Sum Sq Mean Sq F value Pr(>F) time 1 11.56 11.5649 1.7282

Hi, I encountered a weird problem when using the Design and Hmisc problem. I have 2 data frame called "a" and "b", both have 3 columns: "time", "status" and "scores", a sample of the data frame is like: data frame "a": time status scores 1 21 1 99.61 2 38 0 101.11 3 51 0 100.62 4 48 0 87.52 5 78 0

Dear all, I would like to carry out a stepwise regression using the function step. If I use either ~ (A + B + C + D)^4 or explicitly all main effects and interactions for the scope argument, the procedure only considers the four main effects for addition or elimination in each iteration step. What did I do wrong? I'm using R version 1.1.1 on Windows NT. (I'm sorry if this is a stupid

Hi dear sirs, I am wondering why the fit of the time serie x with an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078

Quoting Francesca PANCOTTO <f.pancotto at unimore.it>: > Dear R contributors, > > I have a problem in selecting in an efficient way, rows of a data > frame according to a condition, > which is a part of a row name of the table. > > The data frame is made of 64 rows and 2 columns, but the row names > are very long but I need to select them according to a small

Dear users, I think my questions are pretty simple, but I got lost in the hundreds of par() and plot() arguments and plot functions, so I don't know in which direction I should go. Here is my sample dataset: test <- structure(list(DIET = structure(c(1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L), .Label = c("G", "GG", "L",

sorry, I had a typo there, it's datadist(b) for the analysis of data frame "b". --- Robert Balshaw <Robert.Balshaw at syreon.com> wrote: > Not sure if this will help, but did you mean to use > datadist(a) for > the analysis of B? > > Rob > > > -----Original Message----- > > From: r-help-bounces at stat.math.ethz.ch > >

#is there a way to get NA in the table of descriptive statistics instead of the function stopping Thank you in advance #data x.f <- structure(list(Site = structure(c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("BC", "HC", "RM119", "RM148", "RM179", "RM185",

On Fri, Sep 14, 2007 at 11:42:18PM -0700, Tanya Lattner wrote: > The 2.1 pre-release (version 1) is available for testing: > http://llvm.org/prereleases/2.1/version1/ > > [...] > > 2) Download llvm-2.1, llvm-test-2.1, and the llvm-gcc4.0 source. > Compile everything. Run "make check" and the full llvm-test suite > (make TEST=nightly report). > > Send

Hadley, I don't know if I am doing something wrong or if it is ggplot please see the two graphs at the bottom of the page (code). melt.nut <- (structure(list(RiverMile = c(119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 179L, 179L, 179L, 179L, 179L, 179L, 179L, 185L, 185L, 185L, 185L, 185L, 185L, 185L, 190L, 190L, 190L, 190L, 190L, 190L,

Hi all, I've started looking at the GlobalMerge pass, enabled by default on ARM and AArch64. I think we should reconsider that, at least for AArch64. As is, the pass just merges all globals together, in groups of 4KB (AArch64, 128B on ARM). At the time it was enabled, the general thinking was "it's almost free, it doesn't affect performance much, we might as well use it".

On 23.11.2012, at 15:12, john skaller <skaller at users.sourceforge.net> wrote: > > On 23/11/2012, at 5:46 PM, Sean Silva wrote: > >> Adding LLVMdev, since this is intimately related to the optimization passes. >> >>> I think this is roughly because some function level optimisations are >>> worse than O(N) in the number of instructions. >>

LLVMers, The 2.1 pre-release (version 1) is available for testing: http://llvm.org/prereleases/2.1/version1/ I'm looking for members of the LLVM community to test the 2.1 release. There are 2 ways you can help: 1) Download llvm-2.1, llvm-test-2.1, and the appropriate llvm-gcc4.0 binary. Run "make check" and the full llvm-test suite (make TEST=nightly report). 2) Download

Target: FreeBSD 6.2-STABLE on i386 autoconf says: configure:2122: checking build system type configure:2140: result: i386-unknown-freebsd6.2 [...] configure:2721: gcc -v >&5 Using built-in specs. Configured with: FreeBSD/i386 system compiler Thread model: posix gcc version 3.4.6 [FreeBSD] 20060305 [...] objdir != srcdir, for both llvm and gcc. Release build. llvm-gcc 4.2 from source.