search for: unq

.... dfm dfm1= subset(dfm, DF==1) dfm2= subset(dfm, DF==2) plot(c(15:30),seq(from=0,to=60,by=4),pch=19,col=NULL,xlab='Value B',ylab='Value C') Color = as.factor(dfm1$A) colordist = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)] # for unique colors Color.unq = sample(colordist,length(Color)) points(dfm1[,3],dfm1[,4],col=Color.unq,pch=1) points(dfm2[,3],dfm2[,4],col=Color.unq,pch=19) legend('bottom',as.character(Color.unq),col=Color.unq,lwd=rep(2,length(Color.unq)),cex=.6,ncol=5) legend('bottom',as.character(Color),col=Color.unq,lwd=3,c...

...; > plot(c(15:30),seq(from=0,to=60,by=4),pch=19,col=NULL,xlab='Value > > B',ylab='Value C') > > Color = as.factor(dfm1$A) > > colordist = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), > > invert = T)] # for unique colors > > Color.unq = sample(colordist,length(Color)) > > > > points(dfm1[,3],dfm1[,4],col=Color.unq,pch=1) > > points(dfm2[,3],dfm2[,4],col=Color.unq,pch=19) > > legend('bottom',as.character(Color.unq),col=Color.unq,lwd= > rep(2,length(Color.unq)),cex=.6,ncol=5) > > legend(...

...; dfm2= subset(dfm, DF==2) > plot(c(15:30),seq(from=0,to=60,by=4),pch=19,col=NULL,xlab='Value > B',ylab='Value C') > Color = as.factor(dfm1$A) > colordist = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), > invert = T)] # for unique colors > Color.unq = sample(colordist,length(Color)) > > points(dfm1[,3],dfm1[,4],col=Color.unq,pch=1) > points(dfm2[,3],dfm2[,4],col=Color.unq,pch=19) > legend('bottom',as.character(Color.unq),col=Color.unq,lwd=rep(2,length(Color.unq)),cex=.6,ncol=5) > legend('bottom',as.character(Colo...

...,seq(from=0,to=60,by=4),pch=19,col=NULL,xlab='Value >> > B',ylab='Value C') >> > Color = as.factor(dfm1$A) >> > colordist = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), >> > invert = T)] # for unique colors >> > Color.unq = sample(colordist,length(Color)) >> > >> > points(dfm1[,3],dfm1[,4],col=Color.unq,pch=1) >> > points(dfm2[,3],dfm2[,4],col=Color.unq,pch=19) >> > legend('bottom',as.character(Color.unq),col=Color.unq,lwd=re >> p(2,length(Color.unq)),cex=.6,ncol=5)...

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I have a rectangular matrix of size 920 by 85. I'd like to coerce it into a square matrix such that all row/col names are present in the new matrix and the additional values are zero. As an example: A B C D A 1 2 3 4 E 5 6 7 8 F 9 10 11 12 Would be coerced to: A B C D E F A 1 2 3 4 5 9 B 2 0 0 0 6 10 C 3 0 0

...versions of R. Any suggestions on how to preserve the comment lines would be greatly appreciated. Many apologies if this is a stupid question. Cleridy ## this function has comments at the top: > mapsp.sqrtcpd.f function (input.frm, cpd.numerator, cpd.denominator, basis.dim, npts, nyrs.unq, prop.ctch) { # here are some comments # and more # year.min<- 1975 ...... ## copy function to a dummy function: > tmp.f<-mapsp.sqrtcpd.f ## now edit it with fix and remove a ) so that it complains: > fix(tmp.f) Error in edit(name, file, title, editor) : unexp...

I have a binary matrix of size N x 300. I then create the following: > set.seed(1234) > (key_file <- sample(letters[1:4], 300, replace=TRUE)) [1] "a" "c" "c" "c" "d" "c" "a" "a" "c" "c" "c" "c" "b" "d" "b" "d" "b"

Hi, I searched the archives and did not find a good solution to that. assume I have 10 sets and I want to have the common character elements of them. how could i do that? -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III

folks, if i have three matrices, a, b, cc with some colnames in common, and i want to create a matrix which consists of the common columns added up, and the other columns tacked on, what's a good way to do it? i've got the following roundabout code for two matrices, but if the number of matrices increases, then i'm a bit stymied. > a <- matrix(1:20,ncol=4); colnames(a) <-

...ling(length(data$x)/4)),each=2) b=rep(c(0,1),ceiling(length(data$x)/4)) rk.up=c(1,(a*4+b))[1:ceiling(length(data$x)/2)] rk.down=rev(c(a*4+b-2)[1:floor(length(data$x)/2)]) cat("Performing Siegel-Tukey rank transformation...","\n","\n") rks=c(rk.up,rk.down) unqs=unique(sort(data$x)) corr.rks=tapply(rks,data$x,mean) cbind(unqs,corr.rks) rks.data=data.frame(unqs,corr.rks) names(rks.data)=c("unique values of x","tie-adjusted Siegel-Tukey rank") print(rks.data,row.names=F) names(rks.data)=c("unqs","corr.rks"...

If you want colors mapped to the _values_ in DF1$C, there are a number of ways to do it: Color_unq<-color.scale(DF1$C,c(1,0),c(0,0,c(0,1)) This will produce colors from the lowest values (red) through the highest (blue). See the help page for color.scale to get different colors. With this you can use color.legend to add a mapping of the values and colors. If you just want different colors,...

Hi, I am just wondering if there is an easy way to count in a numeric vector how many numbers don't have replicates. For example, a=c(1,1,2,2,3,4,5), how can I know there are three numbers (3, 4 and 5) without replicates? Thank you! Jun =====

...1=0,link/target 2=0,link/target/subdir now with -e: 0=0 1=0,link/target 2=1,link/target/subdir pass ./check.t:env-prompt pass ./check.t:expand-ugly pass ./check.t:expand-unglob-dblq pass ./check.t:expand-unglob-unq pass ./check.t:expand-threecolons-dblq pass ./check.t:expand-threecolons-unq pass ./check.t:expand-weird-1 pass ./check.t:expand-weird-2 pass ./check.t:expand-weird-3 pass ./check.t:eglob-bad-1 pass ./check.t:eglob-bad-2 pass ./check.t:eglob-infinite-plus pass ./check.t:eglob-subst-1 pass ./check.t...

On Wed, 9 Oct 2013 10:44:27 +0000 (UTC) Thorsten Glaser <tg at mirbsd.de> wrote: > Anil Singhar dixit: > > >Manual testing as provided within the package has been done with all > >tests passing. This includes the units tests available under > >usr/klibc/tests, usr/utils, usr/dash and usr/gzip. For dash and > >gzip, only sanity testing has been done. > >

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Dear Everyone, The document of `tsdiag? says > These tests are sometimes applied to the residuals from an ARMA(p, q) fit, in which case the references suggest a better approximation to the null-hypothesis distribution is obtained by setting fitdf = p+q, provided of course that lag > fitdf. This implies that we should pass the `fitdf' parameter when applying `Box.test' to