search for: 10090295

Displaying 8 results from an estimated 8 matches for "10090295".

2 D density plot interpretation and manipulating the data
2020 Oct 08
2
2 D density plot interpretation and manipulating the data
Hello, I have a data frame like this: > 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 ... and I am creating plot like this: s <- ggplot(SNP, mapping = aes(x = mean, y = var)) s...
2 D density plot interpretation and manipulating the data
2020 Oct 09
2
2 D density plot interpretation and manipulating the data
...rs 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....
2 D density plot interpretation and manipulating the data
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...ter? 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:...
2 D density plot interpretation and manipulating the data
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
2 D density plot interpretation and manipulating the data
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...wing: >> 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...
2 D density plot interpretation and manipulating the data
2020 Oct 09
3
2 D density plot interpretation and manipulating the data
...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 > >> ... > &gt...
2 D density plot interpretation and manipulating the data
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...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 0.002678 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 > ... > > and I am creating plot like this: > > s <-...
2 D density plot interpretation and manipulating the data
2020 Oct 09
0
2 D density plot interpretation and manipulating the data
...gt; > >> 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 &...