similar to: drawing ellipses in R

Is there a way to plot elliptical shapes? symbols() only provides circles... Thanks! Kamila

Dear list, I have a series of data points which I want to approximate with exactly two linear functions. I would like to choose the intervals so that the total deviation from my fitted lines is minimal. How do I best do this? Thanks! Kamila The information transmitted in this electronic communication...{{dropped}}

Hi all! I'm afraid I programmed something totally non-sensical and inefficient, but I can't figure out how to do it better. I have a list of ~ 40 000 characters. I want to take each element at a time, map it to a large data frame with hit=which(data.frame$column==elementFromList), then compute some statistic on data.frame[hit,] and return a result that consists of either 1) a list

Hi everyone, disturbingly, a PCA yields different results when I run the analysis under Windows and Linux. I open the same workspace (which is on an independent partition) and perform the exact same function (by using the history file). Interestingly, PC1 is the same, but PC2 comes out totally different. Thanks! Kamila Windows: platform i386-pc-mingw32 arch i386 os

Hi! I don't understand this: layout(matrix(c(1:10),5,2),heights=c(1,rep(2,4))) plot(1,1) error in plot.new() : plot region too large Why?????? Thanks! Kamila

Hi all, is the Matrix package no longer available for download via install.packages? When I try to install it (from any mirror), I get the following error message: install.packages("Matrix",lib="/home/kn52/R-2.5.0/library") Warning message: package 'Matrix' is not available in install.packages("Matrix",lib="/home/kn52/R-2.5.0/library") Other

Is there an option to draw concentration ellipses in biplots ? It seems really nice to summarize large number of points of each group. Cheers../ Murli

> 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

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

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

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))

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) In order to exclude the outlier to these ellipses/contours is it advisable to do something like this: SNP$density <- get_density(SNP$mean, SNP$var) > summary(SNP$density) Min. 1st Qu. Median Mean 3rd

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 <-

Dear All, I have a data set with the following structure: [A], [a], [B], [b] where [A] and [B] are measurements and [a] and [b] are the associated uncertainties. I produce [B]/[A] vs. [A] plots in R and would like to show uncertainties as error ellipses (rather than error bars). Would this be relatively easy to do in R? I would appreciate any help on this Thanks a lot Tibi

Hello again, First I want to thank all the people who answered my question about line width in graphs. I promise I will learn the 'par' help page by heart for the end of the month ! I now want to trace some ellipses to emphasize groups of data. I found how to trace circles with 'symbols()', but no ellipse. I'm planning on writing my own function based on

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

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

I can take the results of a simulation with one random variable and generate an empirical interval that contains 95% of the observations, e.g., x <- rnorm(10000) quantile(x,probs=c(0.025,0.975)) Is there an R function that can take the results from two random variables and generate an empirical ellipse that contains 95% of the observations, e.g., x <- rnorm(10000) y <- rnorm(10000) ?

Hello all, In the course of trying to implement the CDF of an InverseGammaDistribution, I have run across the need for an igamma() function. Several others have needed this function but the answers I have found so far are not totally clear to me. I'm writing for three reasons: 1) to present a small error in the docs 2) to clarify the approach we are expected to take 3) to request,for the

Hi, n a package, I have a data object with attributes, and I want to dynamically create a convenience function to access those attributes. This way, instead of using attr(x, "number"), I would like to use number(x). Because I don't know in advance which attributes the data object may have, I've used the following algorithm: x <- structure(pi, number=exp(1)) a <-