Hi varin, Were you expecting image files? I don't see any plot device e.g. pdf() in your code.
Jim On Wed, May 12, 2021 at 6:34 PM varin sacha via R-help <r-help@r-project.org> wrote: > > Dear Experts, > > My R code was perfectly working since I decide to add a 5th correlation > coefficient : hoeffdings' D. > fter a google search, I guess I need somewhere in my R code "unlist" but I > don't know where ! > Here below my R code with 1 error message. At the end I get my 8 plots but > they are empty ! > Many thanks for your precious help ! > > ################# > set.seed(1) > library(energy) > library(independence) > library(TauStar) > > # 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, for hoeffding and dcor respectively) for each of the nsim null > datasets at a #given noise level > val.cor=val.cors=val.cork=val.dcor=val.hoe=rep(NA,nsim) > > # Vectors holding the alternative "correlations" (for pearson, for #spearman, > for kendall, for hoeffding and dcor respectively) for each of #the nsim2 > #alternative datasets at a given noise level > val.cor2=val.cors2=val.cork2=val.dcor2=val.hoe2= 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=power.hoe= 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 5 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) > val.hoe[ii]=(hoeffding.D.test(x,y,na.rm=TRUE,collisions=TRUE)) > } > > ## Next we calculate our 5 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) > cut.hoe=quantile(val.hoe,.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 5 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) > val.hoe2[ii]=(hoeffding.D.test(x,y,na.rm=TRUE,collisions=TRUE)) > } > > ## 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 > power.hoe[typ,l] <- sum(val.hoe2 > cut.hoe)/nsim2 > } > } > > ## The rest of the code is for plotting the image > 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') > points((1:30)/10, power.hoe[1,], pch = 5, col = "purple", type = 'b') > legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe" > ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple")) > > 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') > points((1:30)/10, power.hoe[2,], pch = 5, col = "purple", type = 'b') > legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe" > ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple")) > > 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') > points((1:30)/10, power.hoe[3,], pch = 5, col = "purple", type = 'b') > legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe" > ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple")) > > 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') > points((1:30)/10, power.hoe[5,], pch = 5, col = "purple", type = 'b') > legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe" > ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple")) > > 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') > points((1:30)/10, power.hoe[4,], pch = 5, col = "purple", type = 'b') > legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe" > ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple")) > > 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') > points((1:30)/10, power.hoe[6,], pch = 5, col = "purple", type = 'b') > legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe" > ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple")) > > 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') > points((1:30)/10, power.hoe[7,], pch = 5, col = "purple", type = 'b') > legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe" > ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple")) > > 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') > points((1:30)/10, power.hoe[8,], pch = 5, col = "purple", type = 'b') > legend("topright",c("cor pearson","cor spearman", "cor kendal","dcor","hoe" > ), pch = c(1,2,3,4,5), col = c("black","green","blue","red", "purple")) > ################# > > > > > > > > > > Le mardi 11 mai 2021 à 20:00:49 UTC+2, varin sacha via R-help > <r-help@r-project.org> a écrit : > > > > > > 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. > > ______________________________________________ > 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.