... 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 <i...@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
> i...@aghmed.fsnet.co.uk
> http://www.aghmed.fsnet.co.uk/home.html
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
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R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
