How to get data from adjusted quantile plot in R? -

i have data.frame 2 columns , using aq.plot mvoutlier package, identify potential outliers in 2-d dataset. problem i'm not happy "look" of plots generated , grab data plotting , make plot in other software.

for specific case plot generated by,

library('mvoutlier')  data = read.csv(fp, colclasses=c("null",na,na))  h = aq.plot(data) 

the data.frame, data looks this:

    pr          tas 1   5.133207    59.24362 2   20.173075   75.81661 3   24.819054   97.31020 4   35.893467   92.11203 5   27.752425   95.70120 6   25.765618   91.14163 7   20.895360   57.30519 8   8.921513    70.31467 9   36.031261   98.24573 10  27.166213   92.79554 11  8.889431    54.48514 12  59.564447   85.69632 13  43.818336   99.36451 14  43.408963   84.23207 15  22.653269   84.89939 16  21.480331   96.18303 17  22.827370   69.97202 18  23.252464   85.08739 19  14.618731   45.30504 20  40.795519   78.56758 21  37.310456   80.30799 22  31.099105   91.31675 23  33.107472   63.07043 24  9.611930    35.62702 

and generated plot looks this:

so question is, how can information plotted in subplot on top right? information mean x, y coordinates , number associated each point. great if there way x values @ 2 vertical lines drawn.

i see output h calling aq.plot() command gives boolean array stating points outliers (true) or not (false) there appear no access underlying components of plot.

any appreciated.

it's in code aq.plot. here specific code plot in upper right:

plot(s$x, (1:length(dist))/length(dist), col = 3, xlab = "ordered squared robust distance",          ylab = "cumulative probability", type = "n")     text(s$x, (1:length(dist))/length(dist), as.character(s$ix),          col = 3, cex = 0.8)     t <- seq(0, max(dist), = 0.01)     lines(t, pchisq(t, df = ncol(x)), col = 6)     abline(v = delta, col = 5)     text(x = delta, y = 0.4, paste(100 * (pchisq(delta, df = ncol(x))),          "% quantile", sep = ""), col = 5, pos = 2, srt = 90,          cex = 0.8)     xarw <- arw(x, covr$center, covr$cov, alpha = alpha)     if (xarw$cn < inf) {         abline(v = xarw$cn, col = 4)         text(x = xarw$cn, y = 0.4, "adjusted quantile", col = 4,              pos = 4, srt = 90, cex = 0.8)     } 

if through code of function aq.plot, see can x coordinates , associated observation way:

covr <- robustbase::covmcd(data, alpha = 1/2) dist <- mahalanobis(data, center = covr$center, cov = covr$cov) s <- sort(dist, index = true) s$x  #        22          4          6         10         21         18         15          5         14  # 0.1152036  0.2181437  0.3148553  0.3255492  0.3752751  0.4076276  0.4661830  0.5299942  0.7093746  #         9         20          3         16          2         13         17         23         12  # 0.7564636  0.7756129  0.8838616  1.0807574  1.3059546  1.4891242  1.8606975  2.9690980  3.9152682  #         8          7          1         11         19  # 4.0283820  5.0767176  7.4233298  7.9488595 10.3217389  

then y coordinates:

(1:length(dist))/length(dist) #[1] 0.04347826 0.08695652 0.13043478 0.17391304 0.21739130 0.26086957 0.30434783 0.34782609 #[9] 0.39130435 0.43478261 0.47826087 0.52173913 0.56521739 0.60869565 0.65217391 0.69565217 #[17] 0.73913043 0.78260870 0.82608696 0.86956522 0.91304348 0.95652174 1.00000000 

you can rebuild plot directly using following code altered above. reading through code , following along build plot should see find each piece of information. abline calls info on vertical lines, , you'll find values here qchisq(0.975, df = ncol(data)) , here arw(data, covr$center, covr$cov, alpha = 0.05)$cn

 plot(s$x, (1:length(dist))/length(dist), col = 3, xlab = "ordered squared robust distance",          ylab = "cumulative probability", type = "n")     text(s$x, (1:length(dist))/length(dist), as.character(s$ix),          col = 3, cex = 0.8)     t <- seq(0, max(dist), = 0.01)     lines(t, pchisq(t, df = ncol(data)), col = 6)     abline(v = qchisq(0.975, df = ncol(data)), col = 5)     text(x = qchisq(0.975, df = ncol(data)),           y = 0.4, paste(100 * (pchisq(qchisq(0.975, df = ncol(data)), df = ncol(data))),          "% quantile", sep = ""), col = 5, pos = 2, srt = 90,          cex = 0.8)     xarw <- arw(data, covr$center, covr$cov, alpha = 0.05)     if (xarw$cn < inf) {         abline(v = xarw$cn, col = 4)         text(x = xarw$cn, y = 0.4, "adjusted quantile", col = 4,              pos = 4, srt = 90, cex = 0.8)     }