Displaying 8 results from an estimated 8 matches for "geom_density_2d".
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geom_density2d
2020 Oct 09
3
2 D density plot interpretation and manipulating the data
...amarija 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 loc...
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...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 ass...
2020 Oct 08
2
2 D density plot interpretation and manipulating the data
...02678 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
...
and I am creating plot like this:
s <- ggplot(SNP, mapping = aes(x = mean, y = var))
s <- s + geom_density_2d() + geom_point() + my.theme + ggtitle("SNPs")
s
I am getting plot in attach.
My question is how do I:
1.interpret the inclusion versus exclusion within the ellipses-contours?
2. how do I extract from my data frame the points which are outside of ellipses?
Thanks
Ana
-------------- ne...
2020 Oct 09
2
2 D density plot interpretation and manipulating the data
...e 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.1020112...
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
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 yo...
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...Max.
0 383 696 738 1170 1789
where get_density() is function from here:
https://slowkow.com/notes/ggplot2-color-by-density/
and then do something like this:
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")
On Thu, Oct 8, 2020 at 3:52 PM Ana Marija <sokovic.anamarija at gmail.com> wrote:
>
> Hello,
>
> I have a data frame like this:
>
> > head(SNP)
> mean var sd
> FQC.10090295 0.0327 0...
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...iance. 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 0.044...
2020 Oct 09
2
2 D density plot interpretation and manipulating the data
> 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