Rui,
Angelo,
I found it :=)
Many thanks
S.
Le dimanche 9 mai 2021 à 23:49:41 UTC+2, Angelo Canty <ca...@math.mcmaster.ca>
a écrit :
Have you looked in the pdf file (power.pdf) to which you instructed R to
send the plots?
On 2021-05-09 5:27 p.m., varin sacha via R-help wrote:
> Dear Rui,
>
> I thank you for your response but when I run the code with your few
> modifications, I still don't get the 8 graphs but I get the following answer :
>
> null device
> 1
>
> Here below my R code with your modifications. I don't know what I am still
> missing ?
>
> ##############
> set.seed(1)
> library(energy)
>
> # Here we define parameters which we use to simulate the data
> # The number of null datasets we use to estimate our rejection reject
> #regions for an alternative with level 0.05
> nsim=50
>
> # Number of alternative datasets we use to estimate our power
> nsim2=50
>
> # The number of different noise levels used
> num.noise <- 30
>
> # A constant to determine the amount of noise
> noise <- 3
>
> # Number of data points per simulation
> n=100
>
> # Vectors holding the null "correlations" (for pearson, for spearman, for
> kendall and dcor respectively) for each # of the nsim null datasets at a
> #given noise level
> val.cor=val.cors=val.cork=val.dcor=rep(NA,nsim)
>
> # Vectors holding the alternative "correlations" (for pearson, for #spearman,
> for kendall and dcor respectively) #for each of the nsim2 alternative
> datasets at a given noise level
> val.cor2=val.cors2=val.cork2=val.dcor2= rep(NA,nsim2)
>
> # Arrays holding the estimated power for each of the 4 "correlation" types,
> for each data type (linear, #parabolic, etc...) with each noise level
> power.cor=power.cors=power.cork=power.dcor= array(NA, c(8,num.noise))
>
> ## We loop through the noise level and functional form; each time we
> #estimate a null distribution based on #the marginals of the data, and then
> #use that null distribution to estimate power
> ## We use a uniformly distributed x, because in the original paper the
> #authors used the same
>
> for(l in 1:num.noise) {
>
> for(typ in 1:8) {
>
> ## This next loop simulates data under the null with the correct marginals (x
> is uniform, and y is a function of a #uniform with gaussian noise)
>
> for(ii in 1:nsim) {
> x=runif(n)
>
> #lin+noise
> if(typ==1) {
> y=x+ noise *(l/num.noise)* rnorm(n)
> }
>
> #parabolic+noise
> if(typ==2) {
> y=4*(x-.5)^2+ noise * (l/num.noise) * rnorm(n)
> }
>
> #cubic+noise
> if(typ==3) {
> y=128*(x-1/3)^3-48*(x-1/3)^3-12*(x-1/3)+10* noise * (l/num.noise) *rnorm(n)
> }
>
> #sin+noise
> if(typ==4) {
> y=sin(4*pi*x) + 2*noise * (l/num.noise) *rnorm(n)
> }
>
> #their sine + noise
> if(typ==5) {
> y=sin(16*pi*x) + noise * (l/num.noise) *rnorm(n)
> }
>
> #x^(1/4) + noise
> if(typ==6) {
> y=x^(1/4) + noise * (l/num.noise) *rnorm(n)
> }
>
> #circle
> if(typ==7) {
> y=(2*rbinom(n,1,0.5)-1) * (sqrt(1 - (2*x - 1)^2)) + noise/4*l/num.noise
> *rnorm(n)
> }
>
> #step function
> if(typ==8) {
> y = (x > 0.5) + noise*5*l/num.noise *rnorm(n)
> }
>
>
> # We resimulate x so that we have the null scenario
> x <- runif(n)
>
> # Calculate the 4 correlations
> val.cor[ii]=(cor(x,y))
> val.cors[ii]=(cor(x,y,method=c("spearman")))
> val.cork[ii]=(cor(x,y,method=c("kendal")))
> val.dcor[ii]=dcor(x,y)
> }
>
> ## Next we calculate our 4 rejection cutoffs
> cut.cor=quantile(val.cor,.95)
> cut.cors=quantile(val.cors,.95)
> cut.cork=quantile(val.cork,.95)
> cut.dcor=quantile(val.dcor,.95)
>
> ## Next we simulate the data again, this time under the alternative
>
> for(ii in 1:nsim2) {
> x=runif(n)
>
> #lin+noise
> if(typ==1) {
> y=x+ noise *(l/num.noise)* rnorm(n)
> }
>
> #parabolic+noise
> if(typ==2) {
> y=4*(x-.5)^2+ noise * (l/num.noise) * rnorm(n)
> }
>
> #cubic+noise
> if(typ==3) {
> y=128*(x-1/3)^3-48*(x-1/3)^3-12*(x-1/3)+10* noise * (l/num.noise) *rnorm(n)
> }
>
> #sin+noise
> if(typ==4) {
> y=sin(4*pi*x) + 2*noise * (l/num.noise) *rnorm(n)
> }
>
> #their sine + noise
> if(typ==5) {
> y=sin(16*pi*x) + noise * (l/num.noise) *rnorm(n)
> }
>
> #x^(1/4) + noise
> if(typ==6) {
> y=x^(1/4) + noise * (l/num.noise) *rnorm(n)
> }
>
> #circle
> if(typ==7) {
> y=(2*rbinom(n,1,0.5)-1) * (sqrt(1 - (2*x - 1)^2)) + noise/4*l/num.noise
> *rnorm(n)
> }
>
> #step function
> if(typ==8) {
> y = (x > 0.5) + noise*5*l/num.noise *rnorm(n)
> }
>
> ## We again calculate our 4 "correlations"
> val.cor2[ii]=(cor(x,y))
> val.cors2[ii]=(cor(x,y,method=c("spearman")))
> val.cork2[ii]=(cor(x,y,method=c("kendal")))
> val.dcor2[ii]=dcor(x,y)
> }
>
> ## Now we estimate the power as the number of alternative statistics
> #exceeding our estimated cutoffs
> power.cor[typ,l] <- sum(val.cor2 > cut.cor)/nsim2
> power.cors[typ,l] <- sum(val.cors2 > cut.cor)/nsim2
> power.cork[typ,l] <- sum(val.cork2 > cut.cor)/nsim2
> power.dcor[typ,l] <- sum(val.dcor2 > cut.dcor)/nsim2
> }
> }
>
> save.image()
>
> ## The rest of the code is for plotting the image
> pdf(file = "power.pdf")
> op <- par(mfrow = c(4,2), cex = 0.45)
> plot((1:30)/10, power.cor[1,], ylim = c(0,1), main = "Linear", xlab = "Noise
> Level", ylab = "Power", pch = 1, col = "black", type = 'b')
> points((1:30)/10, power.cors[1,], pch = 2, col = "green", type = 'b')
> points((1:30)/10, power.cork[1,], pch = 3, col = "blue", type = 'b')
> points((1:30)/10, power.dcor[1,], pch = 4, col = "red", type = 'b')
> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch =
> c(1,2,3), col = c("black","green","blue","red"))
>
> plot((1:30)/10, power.cor[2,], ylim = c(0,1), main = "Quadratic", xlab =
>"Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
> points((1:30)/10, power.cors[2,], pch = 2, col = "green", type = 'b')
> points((1:30)/10, power.cork[2,], pch = 3, col = "blue", type = 'b')
> points((1:30)/10, power.dcor[2,], pch = 4, col = "red", type = 'b')
> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch =
> c(1,2,3), col = c("black","green","blue","red"))
>
> plot((1:30)/10, power.cor[3,], ylim = c(0,1), main = "Cubic", xlab = "Noise
>Level", ylab = "Power", pch = 1, col = "black", type = 'b')
> points((1:30)/10, power.cors[3,], pch = 2, col = "green", type = 'b')
> points((1:30)/10, power.cork[3,], pch = 3, col = "blue", type = 'b')
> points((1:30)/10, power.dcor[3,], pch = 4, col = "red", type = 'b')
> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch =
> c(1,2,3), col = c("black","green","blue","red"))
>
> plot((1:30)/10, power.cor[5,], ylim = c(0,1), main = "Sine: period 1/8", xlab
> = "Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
> points((1:30)/10, power.cors[5,], pch = 2, col = "green", type = 'b')
> points((1:30)/10, power.cork[5,], pch = 3, col = "blue", type = 'b')
> points((1:30)/10, power.dcor[5,], pch = 4, col = "red", type = 'b')
> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch =
> c(1,2,3), col = c("black","green","blue","red"))
>
> plot((1:30)/10, power.cor[4,], ylim = c(0,1), main = "Sine: period 1/2", xlab
> = "Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
> points((1:30)/10, power.cors[4,], pch = 2, col = "green", type = 'b')
> points((1:30)/10, power.cork[4,], pch = 3, col = "blue", type = 'b')
> points((1:30)/10, power.dcor[4,], pch = 4, col = "red", type = 'b')
> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch =
> c(1,2,3), col = c("black","green","blue","red"))
>
> plot((1:30)/10, power.cor[6,], ylim = c(0,1), main = "X^(1/4)", xlab = "Noise
> Level", ylab = "Power", pch = 1, col = "black", type = 'b')
> points((1:30)/10, power.cors[6,], pch = 2, col = "green", type = 'b')
> points((1:30)/10, power.cork[6,], pch = 3, col = "blue", type = 'b')
> points((1:30)/10, power.dcor[6,], pch = 4, col = "red", type = 'b')
> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch =
> c(1,2,3), col = c("black","green","blue","red"))
>
> plot((1:30)/10, power.cor[7,], ylim = c(0,1), main = "Circle", xlab = "Noise
> Level", ylab = "Power", pch = 1, col = "black", type = 'b')
> points((1:30)/10, power.cors[7,], pch = 2, col = "green", type = 'b')
> points((1:30)/10, power.cork[7,], pch = 3, col = "blue", type = 'b')
> points((1:30)/10, power.dcor[7,], pch = 4, col = "red", type = 'b')
> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch =
> c(1,2,3), col = c("black","green","blue","red"))
>
> plot((1:30)/10, power.cor[8,], ylim = c(0,1), main = "Step function", xlab =
> "Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
> points((1:30)/10, power.cors[8,], pch = 2, col = "green", type = 'b')
> points((1:30)/10, power.cork[8,], pch = 3, col = "blue", type = 'b')
> points((1:30)/10, power.dcor[8,], pch = 4, col = "red", type = 'b')
> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch =
> c(1,2,3), col = c("black","green","blue","red"))
> par(op)
> dev.off()
> #################
>
>
>
>
>
>
>
> Le dimanche 9 mai 2021 à 22:44:22 UTC+2, Rui Barradas <ruipbarra...@sapo.pt>
> a écrit :
>
>
>
>
>
> Hello,
>
> You are not closing the pdf device.
> The only changes I have made to your code are right at the beginning of
> the plotting instructions and at the end of the code.
>
>
> ## The rest of the code is for plotting the image
> pdf(file = "power.pdf")
> op <- par(mfrow = c(4,2), cex = 0.45)
>
> [...]
>
> par(op)
> dev.off()
> #################
>
> The comments only line is your last code line.
> The result is attached.
>
> Hope this helps,
>
> Rui Barradas
>
> Às 19:39 de 09/05/21, varin sacha via R-help escreveu:
>> Dear R-experts,
>>
>> I am trying to get the 8 graphs like the ones in this paper :
>> https://statweb.stanford.edu/~tibs/reshef/comment.pdf
>> My R code does not show any error message neither warnings but I d'on't get
>> what I would like to get (I mean the 8 graphs), so I am missing something.
>> What's it ? Many thanks for your precious help.
>>
>> #################
>> set.seed(1)
>> library(energy)
>>
>> # Here we define parameters which we use to simulate the data
>> # The number of null datasets we use to estimate our rejection reject
>> #regions for an alternative with level 0.05
>> nsim=50
>>
>> # Number of alternative datasets we use to estimate our power
>> nsim2=50
>>
>> # The number of different noise levels used
>> num.noise <- 30
>>
>> # A constant to determine the amount of noise
>> noise <- 3
>>
>> # Number of data points per simulation
>> n=100
>>
>> # Vectors holding the null "correlations" (for pearson, for spearman, for
>> kendall and dcor respectively) for each # of the nsim null datasets at a
>> #given noise level
>> val.cor=val.cors=val.cork=val.dcor=rep(NA,nsim)
>>
>> # Vectors holding the alternative "correlations" (for pearson, for
>> #spearman, for kendall and dcor respectively) #for each of the nsim2
>> alternative datasets at a given noise level
>> val.cor2=val.cors2=val.cork2=val.dcor2= rep(NA,nsim2)
>>
>>
>> # Arrays holding the estimated power for each of the 4 "correlation" types,
>> for each data type (linear, #parabolic, etc...) with each noise level
>> power.cor=power.cors=power.cork=power.dcor= array(NA, c(8,num.noise))
>>
>> ## We loop through the noise level and functional form; each time we
>> #estimate a null distribution based on #the marginals of the data, and then
>> #use that null distribution to estimate power
>> ## We use a uniformly distributed x, because in the original paper the
>> #authors used the same
>>
>> for(l in 1:num.noise) {
>>
>> for(typ in 1:8) {
>>
>> ## This next loop simulates data under the null with the correct marginals
>> (x is uniform, and y is a function of a #uniform with gaussian noise)
>>
>> for(ii in 1:nsim) {
>> x=runif(n)
>>
>> #lin+noise
>> if(typ==1) {
>> y=x+ noise *(l/num.noise)* rnorm(n)
>> }
>>
>> #parabolic+noise
>> if(typ==2) {
>> y=4*(x-.5)^2+ noise * (l/num.noise) * rnorm(n)
>> }
>>
>> #cubic+noise
>> if(typ==3) {
>> y=128*(x-1/3)^3-48*(x-1/3)^3-12*(x-1/3)+10* noise * (l/num.noise) *rnorm(n)
>> }
>>
>> #sin+noise
>> if(typ==4) {
>> y=sin(4*pi*x) + 2*noise * (l/num.noise) *rnorm(n)
>> }
>>
>> #their sine + noise
>> if(typ==5) {
>> y=sin(16*pi*x) + noise * (l/num.noise) *rnorm(n)
>> }
>>
>> #x^(1/4) + noise
>> if(typ==6) {
>> y=x^(1/4) + noise * (l/num.noise) *rnorm(n)
>> }
>>
>> #circle
>> if(typ==7) {
>> y=(2*rbinom(n,1,0.5)-1) * (sqrt(1 - (2*x - 1)^2)) + noise/4*l/num.noise
>> *rnorm(n)
>> }
>>
>> #step function
>> if(typ==8) {
>> y = (x > 0.5) + noise*5*l/num.noise *rnorm(n)
>> }
>>
>> # We resimulate x so that we have the null scenario
>> x <- runif(n)
>>
>> # Calculate the 4 correlations
>> val.cor[ii]=(cor(x,y))
>> val.cors[ii]=(cor(x,y,method=c("spearman")))
>> val.cork[ii]=(cor(x,y,method=c("kendal")))
>> val.dcor[ii]=dcor(x,y)
>> }
>>
>> ## Next we calculate our 4 rejection cutoffs
>> cut.cor=quantile(val.cor,.95)
>> cut.cors=quantile(val.cors,.95)
>> cut.cork=quantile(val.cork,.95)
>> cut.dcor=quantile(val.dcor,.95)
>>
>> ## Next we simulate the data again, this time under the alternative
>>
>> for(ii in 1:nsim2) {
>> x=runif(n)
>>
>> #lin+noise
>> if(typ==1) {
>> y=x+ noise *(l/num.noise)* rnorm(n)
>> }
>>
>> #parabolic+noise
>> if(typ==2) {
>> y=4*(x-.5)^2+ noise * (l/num.noise) * rnorm(n)
>> }
>>
>> #cubic+noise
>> if(typ==3) {
>> y=128*(x-1/3)^3-48*(x-1/3)^3-12*(x-1/3)+10* noise * (l/num.noise) *rnorm(n)
>> }
>>
>> #sin+noise
>> if(typ==4) {
>> y=sin(4*pi*x) + 2*noise * (l/num.noise) *rnorm(n)
>> }
>>
>> #their sine + noise
>> if(typ==5) {
>> y=sin(16*pi*x) + noise * (l/num.noise) *rnorm(n)
>> }
>>
>> #x^(1/4) + noise
>> if(typ==6) {
>> y=x^(1/4) + noise * (l/num.noise) *rnorm(n)
>> }
>>
>> #circle
>> if(typ==7) {
>> y=(2*rbinom(n,1,0.5)-1) * (sqrt(1 - (2*x - 1)^2)) + noise/4*l/num.noise
>> *rnorm(n)
>> }
>>
>> #step function
>> if(typ==8) {
>> y = (x > 0.5) + noise*5*l/num.noise *rnorm(n)
>> }
>>
>> ## We again calculate our 4 "correlations"
>> val.cor2[ii]=(cor(x,y))
>> val.cors2[ii]=(cor(x,y,method=c("spearman")))
>> val.cork2[ii]=(cor(x,y,method=c("kendal")))
>> val.dcor2[ii]=dcor(x,y)
>> }
>>
>> ## Now we estimate the power as the number of alternative statistics
>> #exceeding our estimated cutoffs
>> power.cor[typ,l] <- sum(val.cor2 > cut.cor)/nsim2
>> power.cors[typ,l] <- sum(val.cors2 > cut.cor)/nsim2
>> power.cork[typ,l] <- sum(val.cork2 > cut.cor)/nsim2
>> power.dcor[typ,l] <- sum(val.dcor2 > cut.dcor)/nsim2
>> }
>> }
>>
>> save.image()
>>
>> ## The rest of the code is for plotting the image
>> pdf("power.pdf")
>> par(mfrow = c(4,2), cex = 0.45)
>> plot((1:30)/10, power.cor[1,], ylim = c(0,1), main = "Linear", xlab = "Noise
>> Level", ylab = "Power", pch = 1, col = "black", type = 'b')
>> points((1:30)/10, power.cors[1,], pch = 2, col = "green", type = 'b')
>> points((1:30)/10, power.cork[1,], pch = 3, col = "blue", type = 'b')
>> points((1:30)/10, power.dcor[1,], pch = 4, col = "red", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch
>> = c(1,2,3), col = c("black","green","blue","red"))
>>
>> plot((1:30)/10, power.cor[2,], ylim = c(0,1), main = "Quadratic", xlab =
>> "Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
>> points((1:30)/10, power.cors[2,], pch = 2, col = "green", type = 'b')
>> points((1:30)/10, power.cork[2,], pch = 3, col = "blue", type = 'b')
>> points((1:30)/10, power.dcor[2,], pch = 4, col = "red", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch
>> = c(1,2,3), col = c("black","green","blue","red"))
>>
>> plot((1:30)/10, power.cor[3,], ylim = c(0,1), main = "Cubic", xlab = "Noise
>> Level", ylab = "Power", pch = 1, col = "black", type = 'b')
>> points((1:30)/10, power.cors[3,], pch = 2, col = "green", type = 'b')
>> points((1:30)/10, power.cork[3,], pch = 3, col = "blue", type = 'b')
>> points((1:30)/10, power.dcor[3,], pch = 4, col = "red", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch
>> = c(1,2,3), col = c("black","green","blue","red"))
>>
>> plot((1:30)/10, power.cor[5,], ylim = c(0,1), main = "Sine: period 1/8",
>> xlab = "Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
>> points((1:30)/10, power.cors[5,], pch = 2, col = "green", type = 'b')
>> points((1:30)/10, power.cork[5,], pch = 3, col = "blue", type = 'b')
>> points((1:30)/10, power.dcor[5,], pch = 4, col = "red", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch
>> = c(1,2,3), col = c("black","green","blue","red"))
>>
>> plot((1:30)/10, power.cor[4,], ylim = c(0,1), main = "Sine: period 1/2",
>> xlab = "Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
>> points((1:30)/10, power.cors[4,], pch = 2, col = "green", type = 'b')
>> points((1:30)/10, power.cork[4,], pch = 3, col = "blue", type = 'b')
>> points((1:30)/10, power.dcor[4,], pch = 4, col = "red", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch
>> = c(1,2,3), col = c("black","green","blue","red"))
>>
>> plot((1:30)/10, power.cor[6,], ylim = c(0,1), main = "X^(1/4)", xlab =
>> "Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
>> points((1:30)/10, power.cors[6,], pch = 2, col = "green", type = 'b')
>> points((1:30)/10, power.cork[6,], pch = 3, col = "blue", type = 'b')
>> points((1:30)/10, power.dcor[6,], pch = 4, col = "red", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch
>> = c(1,2,3), col = c("black","green","blue","red"))
>>
>> plot((1:30)/10, power.cor[7,], ylim = c(0,1), main = "Circle", xlab = "Noise
>> Level", ylab = "Power", pch = 1, col = "black", type = 'b')
>> points((1:30)/10, power.cors[7,], pch = 2, col = "green", type = 'b')
>> points((1:30)/10, power.cork[7,], pch = 3, col = "blue", type = 'b')
>> points((1:30)/10, power.dcor[7,], pch = 4, col = "red", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch
>> = c(1,2,3), col = c("black","green","blue","red"))
>>
>> plot((1:30)/10, power.cor[8,], ylim = c(0,1), main = "Step function", xlab =
>> "Noise Level", ylab = "Power", pch = 1, col = "black", type = 'b')
>> points((1:30)/10, power.cors[8,], pch = 2, col = "green", type = 'b')
>> points((1:30)/10, power.cork[8,], pch = 3, col = "blue", type = 'b')
>> points((1:30)/10, power.dcor[8,], pch = 4, col = "red", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor"), pch
>> = c(1,2,3), col = c("black","green","blue","red"))
>> #################
>>
>> ______________________________________________
>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> 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.
> ______________________________________________
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.
>
--
------------------------------------------------------------------
| Angelo J. Canty Email: can...@mcmaster.ca |
| Mathematics and Statistics Phone: (905) 525-9140 x 27079 |
| McMaster University Fax : (905) 522-0935 |
| 1280 Main St. W. |
| Hamilton ON L8S 4K1 |
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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.
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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.
