R help - Oct 2020 - 2 D density plot interpretation and manipulating the data

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.anamarija at
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.4567 at
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.anamarija at
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 <spurdle.a at
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 at 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.

Hi Abby,

Thanks for getting back to me, yes I believe I did that by doing this:

SNP$density <- get_density(SNP$mean, SNP$var)
> summary(SNP$density)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
      0     383     696     738    1170    1789

where get_density() is function from here:
https://slowkow.com/notes/ggplot2-color-by-density/

and keep only entries with density > 400

a=SNP[SNP$density>400,]

and plot it again:

p <- ggplot(a, mapping = aes(x = mean, y = var))
p <- p +  geom_density_2d() + geom_point() + my.theme +
ggtitle("SNPS_red")

and probably I can increase that threshold...

Any idea how do I interpret data points that are left contained within
the ellipses?

On Fri, Oct 9, 2020 at 6:09 PM Abby Spurdle <spurdle.a at gmail.com>
wrote:
>
> 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.anamarija at
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.4567 at
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.anamarija
at 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 <spurdle.a at
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 at 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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> SNP$density <- get_density(SNP$mean, SNP$var)
> > summary(SNP$density)
>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
>       0     383     696     738    1170    1789
This doesn't look accurate.
The density values shouldn't all be integers.
And I wouldn't expect the smallest density to be zero, if using a
Gaussian kernel.

Are these values rounded or formatted?

(Recombined Excerpts)
> and keep only entries with density > 400
> a=SNP[SNP$density>400,]
> Any idea how do I interpret data points that are left contained within
the ellipses?

Reiterating, they're contour lines, but they should *not* be ellipses.

You could work out the proportion of "densities" > 400.

    d <- SNP$density
    p.remain <- length (d [d > 400]) / length (d)
    p.remain

Or a more succinct version:

    p.remain <- sum (SNP$density > 400) / nrow (SNP)

Then you can say that you've plotted data with the highest <p.remain>
densities.

I?m wondering if you do any searching of the Web or use the help facilities
before asking questions? When I posed the question to Google?s search facilities
I immediately was directed to, unsurprisingly, the help page text in a webpage
format:

https://ggplot2.tidyverse.org/reference/geom_density_2d.html

In many situations I find it very difficult to pry code out of ggplot functions,
but that help page says it?s a routine found in the MASS package which is very
well documented.
? 
David. 

Sent from my iPhone
> On Oct 9, 2020, at 4:23 PM, Ana Marija <sokovic.anamarija at
gmail.com> wrote:
> 
> ?Hi Abby,
> 
> Thanks for getting back to me, yes I believe I did that by doing this:
> 
> SNP$density <- get_density(SNP$mean, SNP$var)
>> summary(SNP$density)
>   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
>      0     383     696     738    1170    1789
> 
> where get_density() is function from here:
> https://slowkow.com/notes/ggplot2-color-by-density/
> 
> and keep only entries with density > 400
> 
> a=SNP[SNP$density>400,]
> 
> and plot it again:
> 
> p <- ggplot(a, mapping = aes(x = mean, y = var))
> p <- p +  geom_density_2d() + geom_point() + my.theme +
ggtitle("SNPS_red")
> 
> and probably I can increase that threshold...
> 
> Any idea how do I interpret data points that are left contained within
> the ellipses?
> 
>> On Fri, Oct 9, 2020 at 6:09 PM Abby Spurdle <spurdle.a at
gmail.com> wrote:
>> 
>> 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.anamarija at
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.4567 at
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.anamarija
at 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 <spurdle.a
at 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 at 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.
> <snps_red.pdf>
> ______________________________________________
> R-help at 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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