[solved] -- As always, the solution is simple and I always feel foolish for not
having seen it right away.
Thank you, John, for showing that the frequency data can be "converted" to log
before the linear approximations are made.. once the linear approximations are
done with log data, the approximations can be plotted into the "log=x" plot and
everything is spot on :-)
Here is the updated code that does exactly what I had originally wanted:
freq <- c(2, 3, 5, 10, 50, 100, 200, 300, 500, 750, 1000, 1300, 1800, 2450,
2900, 3000, 4000, 5000, 6000, 7000, 8200, 9300, 10000, 11000, 18000, 26500,
33000, 40000);
mag <- c(1.9893038, 1.5088071, 1.1851947, 0.9444483, 0.7680123, 0.7458169,
0.7069638, 0.6393066, 0.6261539, 0.6263381, 0.7053774, 0.6900626, 0.6953527,
0.7843036, 0.9056359, 0.8867276, 0.8937421, 0.9492288, 0.9629118, 1.1972268,
1.0010515, 0.9945838, 1.0564356, 0.8733333, 1.1666667, 1.5366667, 1.4666667,
1.3166667);
flog <- log(freq);
plot(exp(flog),mag,type="b",log="x");
for(i in 1:2000){
xx <- runif(1,min(flog),max(flog));
yy <- approx(flog,mag,xout=xx, method = "linear");
points(exp(xx),yy$y,col=rgb(1,0,0));
}
Thank you!
-Rich
______________________________________________
[email protected] 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.
