Hi Jim,
No, I just want my R code to run correctly. I don’t want a pdf.file or other
off-screen files.
There is 1 error message and I guess it is due to that error message I don’t
get the 8 plots.
Envoyé de mon iPhone
> Le 12 mai 2021 à 13:03, Jim Lemon <drjimle...@gmail.com> a écrit :
>
> Hi varin,
> Were you expecting image files? I don't see any plot device e.g. pdf()
> in your code.
>
> Jim
>
>> On Wed, May 12, 2021 at 6:34 PM varin sacha via R-help
>> <r-help@r-project.org> wrote:
>>
>> Dear Experts,
>>
>> My R code was perfectly working since I decide to add a 5th correlation
>> coefficient : hoeffdings' D.
>> fter a google search, I guess I need somewhere in my R code "unlist" but I
>> don't know where !
>> Here below my R code with 1 error message. At the end I get my 8 plots but
>> they are empty !
>> Many thanks for your precious help !
>>
>> #################
>> set.seed(1)
>> library(energy)
>> library(independence)
>> library(TauStar)
>>
>> # 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, for hoeffding and dcor respectively) for each of the nsim null
>> datasets at a #given noise level
>> val.cor=val.cors=val.cork=val.dcor=val.hoe=rep(NA,nsim)
>>
>> # Vectors holding the alternative "correlations" (for pearson, for
>> #spearman, for kendall, for hoeffding and dcor respectively) for each of
>> #the nsim2 #alternative datasets at a given noise level
>> val.cor2=val.cors2=val.cork2=val.dcor2=val.hoe2= 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=power.hoe= 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 5 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)
>> val.hoe[ii]=(hoeffding.D.test(x,y,na.rm=TRUE,collisions=TRUE))
>> }
>>
>> ## Next we calculate our 5 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)
>> cut.hoe=quantile(val.hoe,.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 5 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)
>> val.hoe2[ii]=(hoeffding.D.test(x,y,na.rm=TRUE,collisions=TRUE))
>> }
>>
>> ## 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
>> power.hoe[typ,l] <- sum(val.hoe2 > cut.hoe)/nsim2
>> }
>> }
>>
>> ## The rest of the code is for plotting the image
>> 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')
>> points((1:30)/10, power.hoe[1,], pch = 5, col = "purple", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe"
>> ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple"))
>>
>> 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')
>> points((1:30)/10, power.hoe[2,], pch = 5, col = "purple", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe"
>> ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple"))
>>
>> 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')
>> points((1:30)/10, power.hoe[3,], pch = 5, col = "purple", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe"
>> ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple"))
>>
>> 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')
>> points((1:30)/10, power.hoe[5,], pch = 5, col = "purple", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe"
>> ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple"))
>>
>> 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')
>> points((1:30)/10, power.hoe[4,], pch = 5, col = "purple", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe"
>> ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple"))
>>
>> 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')
>> points((1:30)/10, power.hoe[6,], pch = 5, col = "purple", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe"
>> ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple"))
>>
>> 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')
>> points((1:30)/10, power.hoe[7,], pch = 5, col = "purple", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe"
>> ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple"))
>>
>> 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')
>> points((1:30)/10, power.hoe[8,], pch = 5, col = "purple", type = 'b')
>> legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe"
>> ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple"))
>> #################
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> Le mardi 11 mai 2021 à 20:00:49 UTC+2, varin sacha via R-help
>> <r-help@r-project.org> a écrit :
>>
>>
>>
>>
>>
>> Dear all,
>>
>> Many thanks for your responses.
>>
>> Best
>> S.
>>
>>
>>
>>
>>
>>
>>
>> Le lundi 10 mai 2021 à 17:18:59 UTC+2, Bill Dunlap
>> <williamwdun...@gmail.com> a écrit :
>>
>>
>>
>>
>>
>> Also, normalizePath("power.pdf").
>>
>>> On Sun, May 9, 2021 at 5:13 PM Bert Gunter <bgunter.4...@gmail.com> wrote:
>>> ?getwd
>>>
>>> Bert Gunter
>>>
>>> "The trouble with having an open mind is that people keep coming along and
>>> sticking things into it."
>>> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>>>
>>>
>>> On Sun, May 9, 2021 at 2:59 PM varin sacha via R-help <r-help@r-project.org>
>>> wrote:
>>>
>>>> Rui,
>>>>
>>>> The created pdf.file is off-screen device. Indeed after dev.off() I should
>>>> view the pdf file on my computer. But I don't find it. Where do I find the
>>>> pdf.file ?
>>>>
>>>> Regards,
>>>>
>>>>
>>>>
>>>> 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.
>>>>
>>>
>>> [[alternative HTML version deleted]]
>>
>>>
>>>
>>> ______________________________________________
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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.
>>>
>>
>> ______________________________________________
>> 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
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>> and provide commented, minimal, self-contained, reproducible code.
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