Hi!
I have two dimensional dataset which has and I need to decide if a point
lies in some "confidence level". If a point has low confidence/density
it can be anomaly which I need to find.
For example:
#load library
library(sm)
#get some data
x.locs = c(74, 74.5, 75, 77,74.5)
y.locs = c(64, 63.5, 63, 61,61.5)
points = cbind(x.locs, y.locs)
#plot it
plot(points)
#get points density
abc=sm.density(points, eval.points=points, eval.grid=FALSE)
#make a matrix from points and estimation
fc=cbind(abc$eval.points,abc$estimate)
#select points upper and lower "confidence" level
fca=subset(fc,fc[,3]>0.08)
fcb=subset(fc,fc[,3]<=0.08)
#show how contours look like with sm "nature" contour function
dens.2 =sm.density(points,display="slice")
#finally plot contour- green points are ok, red are some anomalies
contour(dens.2$eval.points[,1],dens.2$eval.points[,2],dens.2$estimate)
points(fca,col="green")
points(fcb,col="red")
This is OK.
From some reason I'm not able to use sm.density display="slice"
with
estimation on points. This is first problem.
Second is that when I use it with my dataset which has around 46000
points the contours are too much rounded- it makes a circle, but data
has more ellipse shape aproximation. I have tried to work with h
parametr, but without success.
My best result looks:
https://github.com/matejuh/doschecker_wiki_images/raw/master/linear_regression/density/sm_density.png
Btw. if you have some advice how to write example upper better or you
have some suggestion that I can use another library I will appriciate it.
Thanks in advance
Matej Plch
