similar to: Add ellipse to plot

Hello, I have been following the thread dated Monday, October 9, 2006 when Kamila Naxerova asked a question about plotting elliptical shapes. Can you explain the equations for X and Y. I believe they used the parametric form of x and y (x=r cos(theta), y=r sin(theta). I don't know what r is here ? Can you explain 1)the origin of these equations and 2) what is r? Sincerely, Mary A. Marion

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

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) ?

Buenas tardes, Estoy intentando mejorar un gráfico que construyo pero de manera "bruta" y quiere encontrar una manera más eficiente. El gráfico en cuestión tiene más partes pero para simplificar la parte que me interesa es esta: [image: Imágenes integradas 5] Esto es una elipse de confianza de unos puntos, la elipse la consigo ayudándome de dataEllipse (paquete car), para llegar a

> 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

Hi, I know there is a package 'elipse' available but I'm not sure how to use it for my specific implementation. What I would like to do is fit an elipse to two lines of identity (at right angles to each other), as indicated in the following figure: http://imageshack.us/photo/my-images/30/poincareplotwip.png/ Conceptually, I'm really struggling to even come up with a way of

Buenas tardes   Estoy intentando representar graficamente la elipse de confianza de dos pendiente en un modelo sin intercepto La formula que introduzo esplot(ellipse(recta,c(1,1)),type="l"), pero elipse me sale una recta   Gracias [[alternative HTML version deleted]]

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

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 <-

Hi all, By default a call to xyplot from the Lattice package when using 2 factors [eg xyplot( dv~iv | XY * AB ) ] yields the following shingle structure: |_A_|_A_|_B_|_B_| |_X_|_Y_|_X_|_Y_| However, I'm wondering if it is possible to merge the upper shingle within levels of that factor, as in: |___A___|___B___| |_X_|_Y_|_X_|_Y_| Mike -- Mike Lawrence Graduate Student, Department of

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

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

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

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

Thanks for help! Are you happy with your Eaton Elipse ECO 1600? Does it work well with ondelay? I will need some UPS that supports the ondelay parameter. Bartosz śr., 10 kwi 2019 o 13:06 Roger Price <roger at rogerprice.org> napisał(a): > On Wed, 10 Apr 2019, Bartosz wrote: > > > I don't see ups.delay.start anywhere. Here is the output of upsrw: > > > > upsrw

I'm trying to use R to determine the quality of a cubic Bezier curve approximation of an elliptical arc. I know the four control points and I want to compute (x,y) coordinates of many points on the curve. I can't find anything in either the base distribution or CRAN that does this; all the spline-related packages seem to be about *fitting* piecewise Bezier curves to a data set.