[R] empty plots !

Wed May 12 10:33:27 CEST 2021

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.