?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 at
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 ? 22:44:22 UTC+2, Rui Barradas <
> ruipbarradas at sapo.pt> a ?crit :
>
>
>
>
>
> 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
>
> ?s 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 at 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 at 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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