?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.
