[R] empty plots !
Rui Barradas
ru|pb@rr@d@@ @end|ng |rom @@po@pt
Wed May 12 14:03:47 CEST 2021
Hello,
All power.* are filled with NA, please revise the code that produces
those matrices, there's nothing wrong with the plotting code.
Also, suggested in an answer to your previous e-mail *with* code that
you should save
old_par <- par(mfrow = c(4,2), cex = 0.45)
[...]
par(old_par) # at the end, put the graphics device pars back.
Hope this helps,
Rui Barradas
Às 09:33 de 12/05/21, varin sacha via R-help escreveu:
> 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 using 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 <williamwdunlap using gmail.com> a écrit :
>
>
>
>
>
> Also, normalizePath("power.pdf").
>
> On Sun, May 9, 2021 at 5:13 PM Bert Gunter <bgunter.4567 using 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 using 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 <
>>> ruipbarradas using 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 using 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 using 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]]
>
>>
>>
>> ______________________________________________
>> R-help using 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 using 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 using 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.
>
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