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

Mon May 10 17:17:38 CEST 2021

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.
> >
>
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>
> ______________________________________________
> 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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