similar to: density() integrates to 1?

I'm working with a large dataset - large enough that when I do a scatter plot the points all blur together, so I want to plot their density by color - a heat map or something like that. I've used smoothScatter for tasks like this, but the problem is that my current dataset really only looks good on a log-log scale. When I do the following command smoothScatter( data,

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

for example, I have two sets, x and y. I want to draw their histograms using different colors in a graph. I didn't find how to do this by reading ?hist Thanks very much. [[alternative HTML version deleted]]

Hi, I'm using the densCols function for a scatterplot and cannot figure out 1) how to extract the computed densities, and 2) how to create a legend based that represents the upper and lower ranges of the densities. For example: movers.den <- densCols(move$x, move$y) table(movers.den) #08306B #083775 #083B7C #083D7E #3989C1 #3F8FC4 28 22 101 25

Dear group, I need help on two problems: 1. I am trying to plot density plots for each individual in 8 occasions. I can do this by subject wiht the code below: par(mfrow=c(4,2)) plot(density(all8scenarios$SIMCONC[all8scenarios$ID==1&all8scenarios$WSEQ==0])) plot(density(all8scenarios$SIMCONC[all8scenarios$ID==1&all8scenarios$WSEQ==1]))

Dear r-help, I would like to use the 'ppm' function of the 'spatstat' package to fit a Strauss inhibition model. I understand that I can specify a parametric model for the "background" trend, but how would I specify a trend which is estimated using a Kernel density smoother? In particular, I would like to use the 'kde' function of the 'ks' package to

It seems that if an S4 class has no slots it can't be instantiated because it is assumed to be virtual. Is there a way around this other than adding a do-nothing slot? A singleton would be OK, though is not essential. Problem: EmptyFitResult <- setClass("EmptyFitResult", representation=representation()) # also tried it without the second argument. same result. > e <-

Hello, I have a few questions, regarding the smoothScatter function. I have a scatter plot with more than 500,000 data points for two samples. So, I am wanting to display the density in colors to convince people that my good correlation coefficient is not due to an "influential point effect" and plus, I also want to make my scatter plot look pretty. Anyway ... I have been able to

Hi all (again), many thanks for the answer to the optimization problem. All is fine now. The problem now is with kernel estimators in sm. package. I do all the work and the graphics good, but I need the density function data for each point, and I don't know how to get it. The only thing I get is the table at the end of the following sequence: >

Hi to All, I am building a package in R and whenever I run command "R CMD build OAR" in the terminal, I get the following error: * checking for file ?OAR/DESCRIPTION? ... OK * preparing ?OAR?: * checking DESCRIPTION meta-information ... ERROR Malformed Depends or Suggests or Imports or Enhances field. Offending entries: R (>=3.0.2) Entries must be names of packages optionally

Hi! Do not understand following behavior. > summary(test$dif) Min. 1st Qu. Median Mean 3rd Qu. Max. 0.7389 0.9713 0.9850 0.9818 1.0000 1.0000 length(test$dif) [1] 85879 tmp <- hist(test$dif,breaks=100,freq=FALSE) The density on the Y axis in the plot are in the range 0-200. Thought that the density should be in the range 0-1 (something like

Hi, I don't understand what hist(x, freq=FALSE) does. At first I thought it would be just the relative frequencies instead of the absolute frequencies, by just computing "frequencies / n" in every category. But with a small dataset the y-values (densities) don't sum to one. Is there a way to get the histogram doing that? Or what is the idea of this density-computation?

Estimate region of highest probabilty density Dear R-community I have data consisting of x and y. To each pair (x,y) a z value (weight) is assigned. With kde2d I can estimate the densities on a regular grid and based on this make a contour plot (not considering the z-values). According to an earlier post in the list I adjusted the kde2d to kde2d.weighted (see code below) to estimate the

Hi all, I'm looking to add a "density" smoother on top of a hist when Freq=T. In order to do this I can use the relation between count and density, but I would like to know if there is a way for me to predict it upfront. Here is an example: set.seed(242) z = rnorm(30) hist_z <- hist(z) hist_z$counts / hist_z$density # the relation is 15 # why is this 15 ?? # So I can now do:

tabulate has .C("R_tabulate", as.integer(bin), as.integer(length(bin)), as.integer(nbins), ans = integer(nbins), PACKAGE="base")$ans The implementation of R_tabulate has if(x[i] != R_NaInt && x[i] > 0 && x[i] <= *nbin) and so copes with (silently drops) NA. Perhaps the .C could have NAOK=TRUE? This is useful in apply'ing tabulate to

Dear R-Developers, please find attached a set of drafted functions for flat file import and export, partially extending existing functions, partially completely written as new code. I thought you might be interested in those functions and the accompanying utilities for padding and trimming. Main features are - supports several formats, i.e. fixed width and CSV (with one exception) - supports

Hi, I posted a message earlier entitled "How to fit a line through the "Mountain crest" ..." I figured loess is probably the best way, but it seems that the problem is the robustness of the fit. Below I paste an example to illustrate the problem: tmp=rnorm(2000) X.background = 5+tmp; Y.background = 5+ (10*tmp+rnorm(2000)) X.specific = 3.5+3*runif(1000);

Hi, I'm trying to setup some complicated binning with statistics and could use a little help. I've found the bin2 function from the ash package, but it doesn't do everything I need. My intention is to copy some of their code and then modify as needed. I have a vector of two columns: head(data) r1 r2 [1,] 0.03516559 0.03102128 [2,] 0.02162539 0.14847034

Dear R users, I'm wanting to sample from the negative binomial distribution using the rnegbin function from the MASS library to create artificial samples for the purpose of doing some power calculations. However, I would like to work with samples that come from a negative binomial distribution that includes only values greater than or equal to 1 (a truncated negative binomial), and I

Hi the function density() does normally integrate to one - I've checked it and it works and I also read the previous threads. But I realised that it does not integrate to one if I use from, to or cut. My scenario: I simulated densities of a plants originating from an sseed source at distance zero. Therefore the density of the plants will be highest close to zero. Is there anything I can do