[R] No error message but don't get the 8 graphs

[varin sacha]

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 <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"))

> 
> #################
> 
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