You could assign a density value to each point.
Maybe you've done that already...?
Then trim the lowest n (number of) data points
Or trim the lowest p (proportion of) data points.
e.g.
Remove the data points with the 20 lowest density values.
Or remove the data points with the lowest 5% of density values.
I'll let you decide whether that is a good idea or a bad idea.
And if it's a good idea, then how much to trim.
On Sat, Oct 10, 2020 at 5:47 AM Ana Marija <sokovic.anamar...@gmail.com> wrote:
>
> Hi Bert,
>
> Another confrontational response from you...
>
> You might have noticed that I use the word "outlier" carefully in this
> post and only in relation to the plotted ellipses. I do not know the
> underlying algorithm of geom_density_2d() and therefore I am having an
> issue of how to interpret the plot. I was hoping someone here knows
> that and can help me.
>
> Ana
>
> On Fri, Oct 9, 2020 at 11:31 AM Bert Gunter <bgunter.4...@gmail.com> wrote:
> >
> > I recommend that you consult with a local statistical expert. Much of what
> > you say (outliers?!?) seems to make little sense, and your statistical
> > knowledge seems minimal. Perhaps more to the point, none of your questions
> > can be properly answered without subject matter context, which this list is
> > not designed to provide. That's why I believe you need local expertise.
> >
> > 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 Fri, Oct 9, 2020 at 8:25 AM Ana Marija <sokovic.anamar...@gmail.com>
> > wrote:
> >>
> >> Hi Abby,
> >>
> >> thank you for getting back to me and for this useful information.
> >>
> >> I'm trying to detect the outliers in my distribution based of mean and
> >> variance. Can I see that from the plot I provided? Would outliers be
> >> outside of ellipses? If so how do I extract those from my data frame,
> >> based on which parameter?
> >>
> >> So I am trying to connect outliers based on what the plot is showing:
> >> s <- ggplot(SNP, mapping = aes(x = mean, y = var))
> >> s <- s + geom_density_2d() + geom_point() + my.theme + ggtitle("SNPs")
> >>
> >> versus what is in the data:
> >>
> >> > head(SNP)
> >> mean var sd
> >> FQC.10090295 0.0327 0.002678 0.0517
> >> FQC.10119363 0.0220 0.000978 0.0313
> >> FQC.10132112 0.0275 0.002088 0.0457
> >> FQC.10201128 0.0169 0.000289 0.0170
> >> FQC.10208432 0.0443 0.004081 0.0639
> >> FQC.10218466 0.0116 0.000131 0.0115
> >> ...
> >>
> >> the distribution is not normal, it is right-skewed.
> >>
> >> Cheers,
> >> Ana
> >>
> >> On Fri, Oct 9, 2020 at 2:13 AM Abby Spurdle <spurdl...@gmail.com> wrote:
> >> >
> >> > > My understanding is that this represents bivariate normal
> >> > > approximation of the data which uses the kernel density function to
> >> > > test for inclusion within a level set. (please correct me)
> >> >
> >> > You can fit a bivariate normal distribution by computing five parameters.
> >> > Two means, two standard deviations (or two variances) and one
> >> > correlation (or covariance) coefficient.
> >> > The bivariate normal *has* elliptical contours.
> >> >
> >> > A kernel density estimate is usually regarded as an estimate of an
> >> > unknown density function.
> >> > Often they use a normal (or Gaussian) kernel, but I wouldn't describe
> >> > them as normal approximations.
> >> > In general, bivariate kernel density estimates do *not* have
> >> > elliptical contours.
> >> > But in saying that, if the data is close to normality, then contours
> >> > will be close to elliptical.
> >> >
> >> > Kernel density estimates do not test for inclusion, as such.
> >> > (But technically, there are some exceptions to that).
> >> >
> >> > I'm not sure what you're trying to achieve here.
> >>
> >> ______________________________________________
> >> 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.
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