[R] How can I make my functions run faster
Laz
lmramba at ufl.edu
Mon Aug 19 16:34:52 CEST 2013
Yes Bert, I am a beginner in writing R functions. I just don't know what
to avoid or what to use in order to make the R functions faster.
When I run the individual functions, they run quite well.
However, calling all of them using the final function it becomes too slow.
So I don't know how to make it faster.
I used system.time()
Regards,
Laz
On 8/19/2013 10:13 AM, Bert Gunter wrote:
> ... and read the "R Language Definition" manual. I noticed unnecessary
> constructs
> (e.g., z <- f(something); return(z)) that suggest you have more basics
> to learn to write efficient, well-structured R code.
>
> -- Bert
>
> On Mon, Aug 19, 2013 at 3:55 AM, Michael Dewey <info at aghmed.fsnet.co.uk> wrote:
>> At 10:28 19/08/2013, Laz wrote:
>>> Dear R users,
>>>
>>> I have written a couple of R functions, some are through the help of the R
>>> group members. However, running them takes days instead of minutes or a few
>>> hours. I am wondering whether there is a quick way of doing that.
>>
>> Your example code is rather long for humans to profile. Have you thought of
>> getting R to tell where it is spending most time? The R extensions manual
>> tells you how to do this.
>>
>>
>>> Here are all my R functions. The last one calls almost all of the previous
>>> functions. It is the one I am interested in most. It gives me the correct
>>> output but it takes several days to run only 1000 or 2000 simulations!
>>> e.g. system.time(test1<-finalF(designs=5,swaps=20));test1
>>> will take about 20 minutes to run but
>>> system.time(test1<-finalF(designs=5,swaps=50));test1 takes about 10 hours
>>> and system.time(test1<-finalF(designs=25,swaps=2000));test1 takes about 3
>>> days to run
>>>
>>> Here are my functions
>>>
>>>
>>> #####################################################################
>>>
>>> ls() # list all existing objects
>>> rm(list = ls()) # remove them all
>>> rm(list = ls()[!grepl("global.var.A", ls())])
>>> # refresh memory
>>> gc()
>>> ls()
>>>
>>> ### Define a function that requires useful input from the user
>>> #b=4;g=seq(1,20,1);rb=5;cb=4;s2e=1; r=10;c=8
>>>
>>> #####################################
>>> ####################################
>>> # function to calculate heritability
>>> herit<-function(varG,varR=1)
>>> {
>>> h<-4*varG/(varG+varR)
>>> return(c(heritability=h))
>>> }
>>>
>>> ###################################
>>> # function to calculate random error
>>> varR<-function(varG,h2)
>>> {
>>> varR<- varG*(4-h2)/h2
>>> return(c(random_error=varR))
>>> }
>>>
>>> ##########################################
>>> # function to calculate treatment variance
>>> varG<-function(varR=1,h2)
>>> {
>>> varG<-varR*h2/(4-h2)
>>> return(c(treatment_variance=varG))
>>> }
>>>
>>>
>>> ###############################
>>>
>>> # calculating R inverse from spatial data
>>> rspat<-function(rhox=0.6,rhoy=0.6)
>>> {
>>> s2e<-1
>>> R<-s2e*eye(N)
>>> for(i in 1:N) {
>>> for (j in i:N){
>>> y1<-y[i]
>>> y2<-y[j]
>>> x1<-x[i]
>>> x2<-x[j]
>>> R[i,j]<-s2e*(rhox^abs(x2-x1))*(rhoy^abs(y2-y1)) # Core AR(1)*AR(1)
>>> R[j,i]<-R[i,j]
>>> }
>>> }
>>> IR<-solve(R)
>>> IR
>>> }
>>>
>>> ped<<-read.table("ped2new.txt",header=FALSE)
>>> # Now work on the pedigree
>>> ## A function to return Zinverse from pedigree
>>>
>>> ZGped<-function(ped)
>>> {
>>> ped2<-data.frame(ped)
>>> lenp2<-length(unique(ped2$V1));lenp2 # how many Genotypes in total in
>>> the pedigree =40
>>> ln2<-length(g);ln2#ln2=nrow(matdf)=30
>>> # calculate the new Z
>>> Zped<-model.matrix(~ matdf$genotypes -1)# has order N*t = 180 by 30
>>> dif<-(lenp2-ln2);dif # 40-30=10
>>> #print(c(lenp2,ln2,dif))
>>> zeromatrix<-zeros(nrow(matdf),dif);zeromatrix # 180 by 10
>>> Z<-cbind(zeromatrix,Zped) # Design Matrix for random effect (Genotypes):
>>> 180 by 40
>>> # calculate the new G
>>> M<-matrix(0,lenp2,lenp2) # 40 by 40
>>> for (i in 1:nrow(ped2)) { M[ped2[i, 1], ped2[i, 2]] <- ped2[i, 3] }
>>> G<-s2g*M # Genetic Variance covariance matrix for pedigree 2: 40 by 40
>>> IG<-solve(G)
>>> return(list(IG=IG, Z=Z))
>>> }
>>>
>>> ##########################
>>> ## Required packages #
>>> ############################
>>> library(gmp)
>>> library(knitr) # load this packages for publishing results
>>> library(matlab)
>>> library(Matrix)
>>> library(psych)
>>> library(foreach)
>>> library(epicalc)
>>> library(ggplot2)
>>> library(xtable)
>>> library(gdata)
>>> library(gplots)
>>>
>>> #b=6;g=seq(1,30,1);rb=5;cb=6;r=15;c=12;h2=0.3;rhox=0.6;rhoy=0.6;ped=0
>>>
>>> setup<-function(b,g,rb,cb,r,c,h2,rhox=0.6,rhoy=0.6,ped="F")
>>> {
>>> # where
>>> # b = number of blocks
>>> # t = number of treatments per block
>>> # rb = number of rows per block
>>> # cb = number of columns per block
>>> # s2g = variance within genotypes
>>> # h2 = heritability
>>> # r = total number of rows for the layout
>>> # c = total number of columns for the layout
>>>
>>> ### Check points
>>> if(b==" ")
>>> stop(paste(sQuote("block")," cannot be missing"))
>>> if(!is.vector(g) | length(g)<3)
>>> stop(paste(sQuote("treatments")," should be a vector and more than
>>> 2"))
>>> if(!is.numeric(b))
>>> stop(paste(sQuote("block"),"is not of class", sQuote("numeric")))
>>> if(length(b)>1)
>>> stop(paste(sQuote("block"),"has to be only 1 numeric value"))
>>> if(!is.whole(b))
>>> stop(paste(sQuote("block"),"has to be an", sQuote("integer")))
>>>
>>> ## Compatibility checks
>>> if(rb*cb !=length(g))
>>> stop(paste(sQuote("rb x cb")," should be equal to number of
>>> treatment", sQuote("g")))
>>> if(length(g) != rb*cb)
>>> stop(paste(sQuote("the number of treatments"), "is not equal to",
>>> sQuote("rb*cb")))
>>>
>>> ## Generate the design
>>> g<<-g
>>> genotypes<-times(b) %do% sample(g,length(g))
>>> #genotypes<-rep(g,b)
>>> block<-rep(1:b,each=length(g))
>>> genotypes<-factor(genotypes)
>>> block<-factor(block)
>>>
>>> ### generate the base design
>>> k<-c/cb # number of blocks on the x-axis
>>> x<<-rep(rep(1:r,each=cb),k) # X-coordinate
>>>
>>> #w<-rb
>>> l<-cb
>>> p<-r/rb
>>> m<-l+1
>>> d<-l*b/p
>>> y<<-c(rep(1:l,r),rep(m:d,r)) # Y-coordinate
>>>
>>> ## compact
>>> matdf<<-data.frame(x,y,block,genotypes)
>>> N<<-nrow(matdf)
>>> mm<-summ(matdf)
>>> ss<-des(matdf)
>>>
>>> ## Identity matrices
>>> X<<-model.matrix(~block-1)
>>> h2<<-h2;rhox<<-rhox;rhoy<<-rhoy
>>> s2g<<-varG(varR=1,h2)
>>> ## calculate G and Z
>>> ifelse(ped == "F",
>>> c(IG<<-(1/s2g)*eye(length(g)),Z<<-model.matrix(~matdf$genotypes-1)),
>>> c(IG<<- ZGped(ped)[[1]],Z<<-ZGped(ped)[[2]]))
>>> ## calculate R and IR
>>> s2e<-1
>>> ifelse(rhox==0 | rhoy==0, IR<<-(1/s2e)*eye(N),
>>> IR<<-rspat(rhox=rhox,rhoy=rhoy))
>>> C11<-t(X)%*%IR%*%X
>>> C11inv<-solve(C11)
>>> K<<-IR%*%X%*%C11inv%*%t(X)%*%IR
>>> return(list(matdf=matdf,summary=mm,description=ss))
>>>
>>> }
>>>
>>>
>>> #setup(b=6,g=seq(1,30,1),rb=5,cb=6,r=15,c=12,h2=0.3,rhox=0.6,rhoy=0.6,ped="F")[1]
>>>
>>> #system.time(out3<-setup(b=6,g=seq(1,30,1),rb=5,cb=6,r=15,c=12,h2=0.3,rhox=0.6,rhoy=0.6,ped="F"));out3
>>>
>>> #system.time(out4<-setup(b=16,g=seq(1,196,1),rb=14,cb=14,r=56,c=56,h2=0.3,rhox=0.6,rhoy=0.6,ped="F"));out4
>>>
>>>
>>> ####################################################
>>> # The function below uses shortcuts from textbook by Harville 1997
>>> # uses inverse of a partitioned matrix technique
>>> ####################################################
>>>
>>> mainF<-function(criteria=c("A","D"))
>>> {
>>> ### Variance covariance matrices
>>> temp<-t(Z)%*%IR%*%Z+IG - t(Z)%*%K%*%Z
>>> C22<-solve(temp)
>>> ##########################
>>> ## Optimality Criteria
>>> #########################
>>> traceI<<-sum(diag(C22)) ## A-Optimality
>>> doptimI<<-log(det(C22)) # D-Optimality: minimize the det of the inverse
>>> of Inform Matrix
>>> #return(c(traceI,doptimI))
>>> if(criteria=="A") return(traceI)
>>> if(criteria=="D") return(doptimI)
>>> else{return(c(traceI,doptimI))}
>>> }
>>>
>>> # system.time(res1<-mainF(criteria="A"));res1
>>> # system.time(res2<-mainF(criteria="D"));res2
>>> #system.time(res3<-mainF(criteria="both"));res3
>>>
>>>
>>> ##############################################
>>> ### Swap function that takes matdf and returns
>>> ## global values newnatdf and design matrices
>>> ### Z and IG
>>> ##############################################
>>>
>>> swapsimple<-function(matdf,ped="F")
>>> {
>>> # dataset D =mat1 generated from the above function
>>> ## now, new design after swapping is
>>> matdf<-as.data.frame(matdf)
>>> attach(matdf,warn.conflict=FALSE)
>>> b1<-sample(matdf$block,1,replace=TRUE);b1
>>> gg1<-matdf$genotypes[block==b1];gg1
>>> g1<-sample(gg1,2);g1
>>> samp<-Matrix(c(g1=g1,block=b1),nrow=1,ncol=3,
>>> dimnames=list(NULL,c("gen1","gen2","block")));samp
>>> newGen<-matdf$genotypes
>>> newG<-ifelse(matdf$genotypes==samp[,1] &
>>> block==samp[,3],samp[,2],matdf$genotypes)
>>> NewG<-ifelse(matdf$genotypes==samp[,2] & block==samp[,3],samp[,1],newG)
>>> NewG<-factor(NewG)
>>>
>>> ## now, new design after swapping is
>>> newmatdf<-cbind(matdf,NewG)
>>> newmatdf<<-as.data.frame(newmatdf)
>>> mm<-summ(newmatdf)
>>> ss<-des(newmatdf)
>>>
>>> ## Identity matrices
>>> ifelse(ped == "F",
>>> c(IG<<-(1/s2g)*eye(length(g)),Z<<-model.matrix(~newmatdf$NewG-1)), c(IG<<-
>>> ZGped(ped)[[1]],Z<<-ZGped(ped)[[2]]))
>>> ## calculate R and IR
>>> C11<-t(X)%*%IR%*%X
>>> C11inv<-solve(C11)
>>> K<<-IR%*%X%*%C11inv%*%t(X)%*%IR
>>> return(list(newmatdf=newmatdf,summary=mm,description=ss))
>>> }
>>> #swapsimple(matdf,ped="F")[c(2,3)]
>>> #which(newmatdf$genotypes != newmatdf$NewG)
>>> ###########################################
>>> # for one design, swap pairs of treatments
>>> # several times and store the traces
>>> # of the successive swaps
>>> ##########################################
>>>
>>> optmF<-function(iterations=2,verbose=FALSE)
>>> {
>>> trace<-c()
>>>
>>> for (k in 1:iterations){
>>>
>>> setup(b=6,g=seq(1,30,1),rb=5,cb=6,r=15,c=12,h2=0.3,rhox=0.6,rhoy=0.6,ped="F")
>>> swapsimple(matdf,ped="F")
>>> trace[k]<-mainF(criteria="A")
>>> iterations[k]<-k
>>> mat<-cbind(trace, iterations= seq(iterations))
>>> }
>>>
>>> if (verbose){
>>> cat("***starting matrix\n")
>>> print(mat)
>>> }
>>> # iterate till done
>>> while(nrow(mat) > 1){
>>> high <- diff(mat[, 'trace']) > 0
>>> if (!any(high)) break # done
>>> # find which one to delete
>>> delete <- which.max(high) + 1L
>>> #mat <- mat[-delete, ]
>>> mat <- mat[-delete,, drop=FALSE]
>>> }
>>> mat
>>> }
>>>
>>> #system.time(test1<-optmF(iterations=10));test1
>>>
>>> ################################################
>>> ###############################################
>>>
>>> swap<-function(matdf,ped="F",criteria=c("A","D"))
>>> {
>>> # dataset D =mat1 generated from the above function
>>> ## now, new design after swapping is
>>> matdf<-as.data.frame(matdf)
>>> attach(matdf,warn.conflict=FALSE)
>>> b1<-sample(matdf$block,1,replace=TRUE);b1
>>> gg1<-matdf$genotypes[block==b1];gg1
>>> g1<-sample(gg1,2);g1
>>> samp<-Matrix(c(g1=g1,block=b1),nrow=1,ncol=3,
>>> dimnames=list(NULL,c("gen1","gen2","block")));samp
>>> newGen<-matdf$genotypes
>>> newG<-ifelse(matdf$genotypes==samp[,1] &
>>> block==samp[,3],samp[,2],matdf$genotypes)
>>> NewG<-ifelse(matdf$genotypes==samp[,2] & block==samp[,3],samp[,1],newG)
>>> NewG<-factor(NewG)
>>>
>>> ## now, new design after swapping is
>>> newmatdf<-cbind(matdf,NewG)
>>> newmatdf<<-as.data.frame(newmatdf)
>>> mm<-summ(newmatdf)
>>> ss<-des(newmatdf)
>>>
>>> ## Identity matrices
>>> #X<<-model.matrix(~block-1)
>>> #s2g<<-varG(varR=1,h2)
>>> ## calculate G and Z
>>> ifelse(ped == "F",
>>> c(IG<<-(1/s2g)*eye(length(g)),Z<<-model.matrix(~newmatdf$NewG-1)), c(IG<<-
>>> ZGped(ped)[[1]],Z<<-ZGped(ped)[[2]]))
>>> ## calculate R and IR
>>> C11<-t(X)%*%IR%*%X
>>> C11inv<-solve(C11)
>>> K<-IR%*%X%*%C11inv%*%t(X)%*%IR
>>> temp<-t(Z)%*%IR%*%Z+IG - t(Z)%*%K%*%Z
>>> C22<-solve(temp)
>>> ##########################
>>> ## Optimality Criteria
>>> #########################
>>> traceI<-sum(diag(C22)) ## A-Optimality
>>> doptimI<-log(det(C22)) #
>>> #return(c(traceI,doptimI))
>>> if(criteria=="A") return(traceI)
>>> if(criteria=="D") return(doptimI)
>>> else{return(c(traceI,doptimI))}
>>> }
>>>
>>> #swap(matdf,ped="F",criteria="both")
>>>
>>> ###########################################
>>> ### Generate 25 initial designs
>>> ###########################################
>>> #rspatf<-function(design){
>>> # arr = array(1, dim=c(nrow(matdf),ncol(matdf)+1,design))
>>> # l<-list(length=dim(arr)[3])
>>> # for (i in 1:dim(arr)[3]){
>>> # l[[i]]<-swapsimple(matdf,ped="F")[[1]][,,i]
>>> # }
>>> # l
>>> #}
>>> #matd<-rspatf(design=5)
>>> #matd
>>>
>>> #which(matd[[1]]$genotypes != matd[[1]]$NewG)
>>> #which(matd[[2]]$genotypes != matd[[2]]$NewG)
>>>
>>>
>>> ###############################################
>>> ###############################################
>>>
>>> optm<-function(iterations=2,verbose=FALSE)
>>> {
>>> trace<-c()
>>>
>>> for (k in 1:iterations){
>>>
>>> setup(b=6,g=seq(1,30,1),rb=5,cb=6,r=15,c=12,h2=0.3,rhox=0.6,rhoy=0.6,ped="F")
>>> trace[k]<-swap(matdf,ped="F",criteria="A")
>>> iterations[k]<-k
>>> mat<-cbind(trace, iterations= seq(iterations))
>>> }
>>>
>>> if (verbose){
>>> cat("***starting matrix\n")
>>> print(mat)
>>> }
>>> # iterate till done
>>> while(nrow(mat) > 1){
>>> high <- diff(mat[, 'trace']) > 0
>>> if (!any(high)) break # done
>>> # find which one to delete
>>> delete <- which.max(high) + 1L
>>> #mat <- mat[-delete, ]
>>> mat <- mat[-delete,, drop=FALSE]
>>> }
>>> mat
>>> }
>>>
>>> #system.time(res<-optm(iterations=10));res
>>> #################################################
>>> ################################################
>>> finalF<-function(designs,swaps)
>>> {
>>> Nmatdf<-list()
>>> OP<-list()
>>> Miny<-NULL
>>> Maxy<-NULL
>>> Minx<-NULL
>>> Maxx<-NULL
>>> for (i in 1:designs)
>>> {
>>>
>>> setup(b=4,g=seq(1,20,1),rb=5,cb=4,r=10,c=8,h2=0.3,rhox=0.6,rhoy=0.6,ped="F")[1]
>>> mainF(criteria="A")
>>> for (j in 1:swaps)
>>> {
>>> OP[[i]]<- optmF(iterations=swaps)
>>> Nmatdf[[i]]<-newmatdf[,5]
>>> Miny[i]<-min(OP[[i]][,1])
>>> Maxy[i]<-max(OP[[i]][,1])
>>> Minx[i]<-min(OP[[i]][,2])
>>> Maxx[i]<-max(OP[[i]][,2])
>>> }
>>> }
>>> return(list(OP=OP,Miny=Miny,Maxy=Maxy,Minx=Minx,Maxx=Maxx,Nmatdf=Nmatdf))
>>> # gives us both the Optimal conditions and designs
>>> }
>>>
>>> #################################################
>>> sink(file= paste(format(Sys.time(),
>>> "Final_%a_%b_%d_%Y_%H_%M_%S"),"txt",sep="."),split=TRUE)
>>> system.time(test1<-finalF(designs=25,swaps=2000));test1
>>> sink()
>>>
>>>
>>> I expect results like this below
>>>
>>>> sink()
>>>> finalF<-function(designs,swaps)
>>> +{
>>> + Nmatdf<-list()
>>> + OP<-list()
>>> + Miny<-NULL
>>> + Maxy<-NULL
>>> + Minx<-NULL
>>> + Maxx<-NULL
>>> + for (i in 1:designs)
>>> + {
>>> +
>>> setup(b=4,g=seq(1,20,1),rb=5,cb=4,r=10,c=8,h2=0.3,rhox=0.6,rhoy=0.6,ped="F")[1]
>>> + mainF(criteria="A")
>>> + for (j in 1:swaps)
>>> + {
>>> + OP[[i]]<- optmF(iterations=swaps)
>>> + Nmatdf[[i]]<-newmatdf[,5]
>>> + Miny[i]<-min(OP[[i]][,1])
>>> + Maxy[i]<-max(OP[[i]][,1])
>>> + Minx[i]<-min(OP[[i]][,2])
>>> + Maxx[i]<-max(OP[[i]][,2])
>>> + }
>>> + }
>>> +
>>> return(list(OP=OP,Miny=Miny,Maxy=Maxy,Minx=Minx,Maxx=Maxx,Nmatdf=Nmatdf)) #
>>> gives us both the Optimal conditions and designs
>>> +}
>>>> sink(file= paste(format(Sys.time(),
>>>> "Final_%a_%b_%d_%Y_%H_%M_%S"),"txt",sep="."),split=TRUE)
>>>> system.time(test1<-finalF(designs=5,swaps=5));test1
>>> user system elapsed
>>> 37.88 0.00 38.04
>>> $OP
>>> $OP[[1]]
>>> trace iterations
>>> [1,] 0.8961335 1
>>> [2,] 0.8952822 3
>>> [3,] 0.8934649 4
>>>
>>> $OP[[2]]
>>> trace iterations
>>> [1,] 0.893955 1
>>>
>>> $OP[[3]]
>>> trace iterations
>>> [1,] 0.9007225 1
>>> [2,] 0.8971837 4
>>> [3,] 0.8902474 5
>>>
>>> $OP[[4]]
>>> trace iterations
>>> [1,] 0.8964726 1
>>> [2,] 0.8951722 4
>>>
>>> $OP[[5]]
>>> trace iterations
>>> [1,] 0.8973285 1
>>> [2,] 0.8922594 4
>>>
>>>
>>> $Miny
>>> [1] 0.8934649 0.8939550 0.8902474 0.8951722 0.8922594
>>>
>>> $Maxy
>>> [1] 0.8961335 0.8939550 0.9007225 0.8964726 0.8973285
>>>
>>> $Minx
>>> [1] 1 1 1 1 1
>>>
>>> $Maxx
>>> [1] 4 1 5 4 4
>>>
>>> $Nmatdf
>>> $Nmatdf[[1]]
>>> [1] 30 8 5 28 27 29 1 26 24 22 13 6 17 18 2 19 14 11 3 23 10 15 21
>>> 9 25 4 7 20 12 16 14 17 15 5 8 6 19
>>> [38] 4 1 10 11 3 24 20 13 2 27 12 16 28 21 23 30 25 29 7 26 18 9 22
>>> 24 21 26 2 13 30 5 28 20 11 3 7 18 25
>>> [75] 22 16 4 17 19 27 29 10 23 6 12 15 14 1 9 8 12 11 3 8 5 20 23
>>> 22 7 15 19 29 24 27 13 2 6 1 21 26 25
>>> [112] 10 16 14 18 4 30 17 9 28 29 9 7 27 11 2 30 18 8 14 19 20 15 21
>>> 4 3 16 24 13 28 26 10 12 6 5 25 1 17
>>> [149] 23 22 21 2 23 16 4 10 9 22 30 24 1 27 3 20 12 5 26 17 28 11 7
>>> 14 8 25 19 13 18 29 15 6
>>> Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
>>> 26 27 28 29 30
>>>
>>> $Nmatdf[[2]]
>>> [1] 5 13 30 2 21 23 6 27 16 19 8 26 18 4 20 9 22 28 7 3 15 10 11
>>> 17 25 24 29 1 14 12 28 18 23 19 21 16 17
>>> [38] 29 13 7 15 27 25 22 10 1 2 5 30 9 20 3 14 24 26 4 6 12 11 8
>>> 8 18 25 12 5 23 21 4 9 17 20 1 2 6
>>> [75] 22 7 16 26 30 29 3 15 19 14 13 11 24 28 27 10 16 21 26 23 25 4 9
>>> 24 15 14 22 1 20 27 2 7 17 18 13 8 12
>>> [112] 5 6 19 28 3 10 30 11 29 11 30 14 9 26 5 1 10 29 28 4 18 8 24
>>> 20 13 3 23 27 6 15 16 21 2 17 7 25 12
>>> [149] 19 22 7 28 8 11 26 24 12 29 9 16 21 27 22 23 18 19 13 6 15 3 1
>>> 30 2 17 14 5 25 20 4 10
>>> Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
>>> 26 27 28 29 30
>>>
>>> $Nmatdf[[3]]
>>> [1] 7 25 4 30 12 11 14 13 26 1 10 21 15 22 29 19 27 16 2 24 28 20 3
>>> 5 23 8 18 6 17 9 6 21 9 15 11 17 13
>>> [38] 29 24 4 20 7 23 14 2 16 18 26 19 25 8 1 12 10 28 27 22 30 5 3
>>> 20 12 8 2 11 18 24 19 9 22 15 7 30 27
>>> [75] 17 29 6 3 5 1 21 25 28 14 23 4 16 26 13 10 20 29 26 25 15 22 9
>>> 10 28 17 18 21 6 16 7 1 3 24 11 2 4
>>> [112] 14 8 5 13 27 23 30 19 12 6 30 1 2 7 28 18 8 20 10 4 25 14 19
>>> 27 11 13 29 12 9 3 26 22 21 16 15 17 24
>>> [149] 5 23 17 6 25 11 21 29 5 26 13 7 15 2 9 4 18 30 3 8 20 24 27
>>> 22 19 16 28 12 1 23 14 10
>>> Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
>>> 26 27 28 29 30
>>>
>>> $Nmatdf[[4]]
>>> [1] 24 8 17 30 10 20 4 28 25 16 14 13 7 12 26 29 21 19 1 22 11 6 23
>>> 18 15 5 27 2 3 9 1 24 27 15 26 14 28
>>> [38] 20 8 5 4 29 2 25 9 13 6 21 7 22 30 17 3 10 12 19 11 18 16 23
>>> 25 18 3 29 1 4 8 6 9 30 2 14 11 16
>>> [75] 23 13 10 12 7 19 17 5 21 28 24 20 15 27 26 22 14 5 7 6 17 3 1
>>> 29 25 23 19 11 21 18 4 30 20 8 2 12 9
>>> [112] 16 10 15 27 26 13 24 28 22 19 7 17 1 12 8 18 16 14 22 3 28 27 25
>>> 10 6 4 15 30 9 11 5 20 26 24 29 21 2
>>> [149] 23 13 2 16 10 25 18 15 26 22 12 19 30 17 23 8 3 7 20 14 13 28 9
>>> 21 11 29 6 5 4 24 27 1
>>> Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
>>> 26 27 28 29 30
>>>
>>> $Nmatdf[[5]]
>>> [1] 12 18 8 22 9 21 2 1 29 13 30 25 17 6 16 5 26 7 3 14 23 15 28
>>> 27 10 24 20 11 19 4 20 30 14 27 25 4 6
>>> [38] 28 23 8 9 29 26 19 24 7 5 1 11 22 21 2 10 18 12 15 3 17 13 16
>>> 16 22 6 9 21 5 14 2 30 10 3 25 27 15
>>> [75] 28 7 17 20 11 8 19 29 12 26 24 13 1 4 18 23 4 16 10 25 5 13 18
>>> 19 22 7 28 30 23 21 11 2 14 9 20 24 8
>>> [112] 17 1 15 29 6 12 27 3 26 14 8 26 6 20 9 15 23 3 22 7 30 25 24
>>> 1 10 19 21 4 11 2 18 17 13 28 29 27 16
>>> [149] 12 5 19 2 4 5 15 21 17 7 25 8 6 16 20 29 10 18 1 12 26 28 27
>>> 11 14 23 22 9 3 13 30 24
>>> Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
>>> 26 27 28 29 30
>>>
>>>
>> Michael Dewey
>> info at aghmed.fsnet.co.uk
>> http://www.aghmed.fsnet.co.uk/home.html
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
>
More information about the R-help
mailing list