similar to: pairs

R plotting experts: I have a bivariate dataset composed of 300 (x,y) continuous datapoints. 297 of these points are located within the y range of [0,10], while 2 are located at 20 and one at 55. No coding errors, real outliers. When plotting these data with a scatterplot, I obviously have a problem. If I plot the full dataset with ylim = c(0,55), then I cannot see the structure in the data in

Dear R I recently did a scatterplot matrix using the following command pairs(sleep[c("SlowSleep", "ParaSleep", "logbw", "logbrw", "loglife", "loggest")],col=1+as.integer(ParaSleep > 5.5 | SlowSleep > 15.7)) this highlighted outlying points for some of the x,y plots that I needed to identify. Unfortunately this highlights all the x,y

 Dear Madame or Sir,I am having a problem in combining density-smoothed scatterplot matrices with a plot of kernel destiny estimations of each dimension plotted on the respective field of the diagonal.I have tried following approach using the package "sm" for the kernel density estimation, as well as "MASS" respectively:pairs(myTable[, 1:4],panel=function(x,y, ...){

Hello, This sounds like a problem to which many solutions should exist, but I did not manage to find one. Basically, given a list of datapoints, I'd like to keep those within the X% percentile highest density. That would be equivalent to retain only points within a given line of a contour plot. Thanks to anybody who could let me know which function I could use! Best, Emmanuel

Hi, I want to draw a scatter plot with 1M and more points and save it as pdf. This makes the pdf file large. So i tried to save the file first as png and than convert it to pdf. This looks OK if printed but if viewed e.g. with acrobat as document figure the quality is bad. Anyone knows a way to reduce the size but keep the quality? /E -- Dipl. bio-chem. Witold Eryk Wolski MPI-Moleculare

I'm working with a lot of data right now, but I'm new to R, and not very good with it, hence my request for help. What type of graph could I use to straighten out things like... http://r.789695.n4.nabble.com/file/n3711389/Untitled.png ...this? I want to see general frequencies. Should I use something like a 3D histogram, or is there an easier way like, say, shading? I'm sure these

Hi all, I'd like to do cross-validation on lm and get the resulting lift curve/table (or, alternatively, the estimates on 100% of my data with which I can get lift). If such a thing doesn't exist, could it be derived using cv.lm, or would we need to start from scratch? Thanks! -- Eric Siegel, Ph.D. President Prediction Impact, Inc. Predictive Analytics World Conference More info:

I am getting an error message from scatterplot: > library(car) > scatterplot(Prestige$income~Prestige$type) Error in Summary.factor(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, : range not meaningful for factors In addition: Warning message: In Ops.factor(x[floor(d)], x[ceiling(d)]) : + not meaningful for factors > The command does output the kind of graph that I want (boxplots).

Hi, This code: n=40 x=17 phat=x/n SE=sqrt(phat*(1-phat)/n) zstar=qnorm(0.995) E=zstar*SE phat+c(-E,E) Gives this result: [1] 0.2236668 0.6263332 The TI Graphing calculator gives the same result. Whereas this test: prop.test(x,n,conf.level=0.99,correct=FALSE) Give this result: 0.2489036 0.6224374 I'm wondering why there is a difference. D. -- View this message in context:

Hi all, I have been looking for means of add a contour around some points in a scatterplot as a means of representing the center of density for of the data. I'm imagining something like a 95% confidence estimate drawn around the data. So far I have found some code for drawing polygons around the data. These look nice, but in some cases the polygons are strongly influenced by outlying points.

Hi, I've two fitted models, one binomial model with presence-absence data that predicts probability of presence and one gaussian model (normal or log-normal abundances). I would like to evaluate these models not on their capability of adjustment but on their capability of prediction by calculating the (log)likelihood between predicted and observed values for each type of model. I found

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

> 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

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

Dear R-users, I have two outliers related questions. I. I have a vector consisting of 69 values. mean = 0.00086 SD = 0.02152 The shape of EDA graphics (boxplots, density plots) is heavily distorted due to outliers. How to define the interval for outliers exception? Is <2SD - mean + 2SD> interval a correct approach? Or should I define 95% (or 99%) limit of agreement for data interval,

An embedded and charset-unspecified text was scrubbed... Name: inte tillg?nglig Url: https://stat.ethz.ch/pipermail/r-help/attachments/20070103/d0f3260e/attachment.pl

Greetings R users, I am an R graphics newbie trying to produce a custom trellis plot using pairs() with R 2.7.2. I have spatial data on which I run a geographically weighted regression (gwr, using the -spgwr- package). I want to check the gwr coefficients for multicollinearity and spatial association, following Wheeler and Tiefelsdorf (2005), and I would like to summarize the results of this

Dear experts, I am a beginner of R. I'm looking for experts to guide me how to do programming in R in order to randomly replace 5 observations in X explanatory variable with outliers drawn from U(15,20) in sample size n=100. The replacement subject to y < 15. The ultimate goal of my study is to compare the std of y with and without the presence of outliers based on average of 1000

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, 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 <-