Dear all,
Many thanks for your responses.
Best
S.
Le lundi 10 mai 2021 à 17:18:59 UTC+2, Bill Dunlap <williamwdun...@gmail.com> a
écrit :
Also, normalizePath("power.pdf").
On Sun, May 9, 2021 at 5:13 PM Bert Gunter <bgunter.4...@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@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 <
>> ruipbarra...@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@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@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@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@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.
