Dear all,
I created a bivariate normal distribution:
set.seed(138813)
n-100
x-rnorm(n); y-rnorm(n)
and plotted a scatterplot of it:
plot(x,y)
Now I'd like to add the 2D-standard deviation.
I found a thread regarding plotting arbitrary confidence boundaries from
Pascal Hänggi
OK, the following seems to work
still do not understand exactly why...
library(MASS)
# parameters:
n-100
# generate samples:
set.seed(138813)
#seed - .Random.seed
x-rnorm(n); y-rnorm(n)
# estimate non-parameteric density surface via kernel smoothing
den-kde2d(x, y, n=n)
# store z values of
Look at the ellipse package (and the ellipse function in the package)
for a simple way of showing a confidence region for bivariate data on
a plot (a 68% confidence interval is about 1 SD if you just want to
show 1 SD).
On Sat, Mar 3, 2012 at 7:54 AM, drflxms drfl...@googlemail.com wrote:
Dear
Thanx a lot Greg for the hint and letting me not alone with this!
Tried ellipse and it works well. But I am looking for something more
precise. The ellipse fits the real border to the nearest possible
ellipse. I want the real contour, if possible.
Meanwhile I found an interesting function named
On Mar 3, 2012, at 17:01 , drflxms wrote:
# this is the critical block, which I still do not comprehend in detail
z - array()
for (i in 1:n){
z.x - max(which(den$x x[i]))
z.y - max(which(den$y y[i]))
z[i] - den$z[z.x, z.y]
}
As far as I can tell, the point is to
On Mar 3, 2012, at 12:34 PM, drflxms wrote:
Thanx a lot Greg for the hint and letting me not alone with this!
Tried ellipse and it works well. But I am looking for something more
precise. The ellipse fits the real border to the nearest possible
ellipse. I want the real contour, if possible.
Thank you very much for your thoughts!
Exactly what you mention is, what I am thinking about during the last
hours: What is the relation between the den$z distribution and the z
distribution.
That's why I asked for ecdf(distribution)(value)-percentile earlier
this day (thank you again for your
Wow, David,
thank you for these sources, which I just screened. bagplot looks most
promising to me. I found it in the package ‘aplpack’ as well as in the R
Graph Gallery
http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=112
Ellipses are not exactly what I am heading for. I am looking
The key part of the ellipse function is:
matrix(c(t * scale[1] * cos(a + d/2) + centre[1], t * scale[2] *
cos(a - d/2) + centre[2]), npoints, 2, dimnames = list(NULL,
names))
Where (if I did not miss anything) the variable 't' is derived from a
chisquare distribution and the
On Mar 3, 2012, at 20:25 , drflxms wrote:
Once you go into two dimensions, SD loses all meaning, and adding
nonparametric density estimation into the mix doesn't help, so just stop
thinking in those terms!
This makes me really think a lot! Is plotting the 0,68 confidence
interval in 2D as
To further explain. If you want contours of a bivariate normal, then
you want ellipses. The density for a bivariate normal (with 0
correlation to keep things simple, but the theory will extend to
correlated cases) is proportional to exp( -1/2 ( x1^2/v1 + x2^2/v2 )
so a contour of the
Greg, extremely cool thoughts! Thank you for delving into it this deep.
As I mentioned, I am a neurologist with unfortunately poor statistical
training. You are professional statisticians. So I'd like to apologize
for any unprofessional nomenclature and strange thoughts beforehand.
As my
Puhh, I am really happy to read that the idea was not completely
sensless. This would have heavily damaged my anyway labile view of the
world of statistics ;)
In any case I need to get to know more about bivariate normal
distributions! Any literature recommendations?
Felix
Am 03.03.12 22:13,
Please allow one (hopefully ;) last question:
Do you think the code I adopted from Hänggi is valid in selecting a
contour which encloses i.e. 68% of the scatter-plot data? - I am still
not completely shore... still looking for the reason of the different
result of Hänggis and Foresters code.
On 3/3/2012 12:34 PM, drflxms wrote:
Thanx a lot Greg for the hint and letting me not alone with this!
Tried ellipse and it works well. But I am looking for something more
precise. The ellipse fits the real border to the nearest possible
ellipse. I want the real contour, if possible.
The
I do agree with the recommendation to consider car::dataEllipse() for
plotting a best fit for the theoretical bivariate Normal situation.
However, the bagplot function from Has Peter Wold: http://www.wiwi.uni-bielefeld.de/~wolf/#software
is more likely to line up and be suitable for
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