search for: nbin

Peter, I thought this one was noted and fixed, but I could be wrong. R : Copyright 1999, The R Development Core Team Version 0.63.3 (March 6, 1999) .... [Previously saved workspace restored] > tabulate(1:10, 5) Process R:1 segmentation fault at Sat Apr 3 17:48:34 1999 -- (The following contact details become official on 1 May 1999, but the email

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 the rows o...

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 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);

...s occurring in columns 'b' to 'e' but considering the fact at the very same time that these distributions should be 'groupped by' the id numbers in column 'id'. It works fine, check it -> matrix(matrix(unlist(lapply(df[,(-(1))],function(x) tapply(x,df$id,tabulate,nbins=nlevels(factor(df[,2])))) [[1]])),ncol=3,nrow=3,byrow=TRUE) matrix(matrix(unlist(lapply(df[,(-(1))],function(x) tapply(x,df$id,tabulate,nbins=nlevels(factor(df[,3])))) [[2]])),ncol=3,nrow=3,byrow=TRUE) matrix(matrix(unlist(lapply(df[,(-(1))],function(x) tapply(x,df$id,tabulate,nbins=nlevels(factor...

...first delete xlab and then add marks myself, but it doesn't look like it's working. The old label can not be removed by setting xlab="" or xlab = NULL. Thanks for your help! Hua #------------------------ x = c(rep(0,10),rep(1,100),rep(2,50), rep(3,30)) library(MASS) truehist(x,nbins=4) truehist(x,nbins=4,xlab="")

...y=rnorm(3000),group=rep(factor(c("A","B","C")), 1000)) # this binscatter-function shows what I want, # I just would like to have it as a panel function binscatter<-function(x,y){ col<-rev(gray.colors(5)) breaks=c(1,5,10,100,500,100000) h2d<-hist2d(x=x,y=y,nbins=10,same.scale=T,show=F) image(h2d$x,h2d$y,h2d$counts,breaks=breaks,col=col,axes=T) } # for one group, this works fine A<-subset(ds,group=="A") binscatter(A$x,A$y) # simple xyplot does too (of course) xyplot(y~x|group,data=ds) # but my lattice-ified version of binscatter does not:...

...on. Is that possible to pass some non-default arguments in the function we want to apply ? For example: if "mat" is a matrix and I want to use the "tabulate" function on its row. The command apply(mat,1,tabulate) works but I have problem with this one apply(mat, 1, tabulate(nbins=4)). Any clue ? Thanks, Eric -- Eric Pellegrini, PhD Computer-Chemie-Centrum University of Erlangen-N??rnberg N??gelbachstra??e, 25 D-91052 Erlangen Germany

...ed) that will do such a thing: # # Function to generate a vector containing values from a truncated # negative binomial distribution (i.e., no zeros). Select desired # mean and variance, sample size, initial values for mean and theta, # and a threshold value for tests. # # format: out<-nbin(desired_mean, desired_variance, n, initial_mean, # theta, threshold_level) # # example: out<-nbin(2, 1, 100, 2, 2, 0.1) # # nbin<-function(mu.s,var.s,n,mu.i,theta,test) { library(MASS) mu<-0 var<-0 rand<-rep(0,n) while(abs(mu.s-mu)>=test &...

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'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

Visual inspection of the plot of a density() function vs a normal with the same mean and variance suggests the area under the density curve is bigger than under the normal curve. The two curves are very close over most of the domain. Assuming the normal curve does integrate to 1, this implies the area under density() is > 1. Is there any assurance that the density kernel smoother produces

Hello, I have created two histograms with: hist2d(gps2, nbins=200, col = c("white",heat.colors(16))) Both of them have the same range and the same number of bins. Now I would like to compare them bin by bin and plot the results. Could someone please tell me how to do that. I searched the man pages and the web, but couldn't find anything. Than...

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]]

The standard chisq.test() and fisher.test() functions, when applied to two distributions (to determine whether the same underlying distribution applies to both) requires one to pre-bin the distributions. Is there a library function (either built-in or in a package) that acts more like the ks.test() function, in that one can simply pass the two distributions and have it do the necessary binning as

...I need to convert my ff_matrix to a data.frame to discretize the whole matrix and calculate the mutual information. The calculated result should be saved as an ffdf-object or something similar. require(infotheo) disc <- as.ffdf(discretize(as.data.frame(as.ffdf(cc)), disc="equalwidth", nbins=5)) This won't work. After this step it somehow loses the path to the working directory. As soon as I try to discretize the next data.frame I get the following message: Error in if (dfile == getOption("fftempdir")) finalizer <- "delete" else finalizer <- "close&...

...,10000000000), log="xy", pch="." ) the output seems to only show a few of the points, and they are in odd places that don't seem to correlate at all with the colors. I got a warning that I should increase the gridsize, but even using comically large values (nbin=10000) didn't help. Does anybody know how I can do this? Thanks! -- View this message in context: http://r.789695.n4.nabble.com/density-plot-on-a-log-scale-tp4633347.html Sent from the R help mailing list archive at Nabble.com.

I have a large data matrix (4460X3500) and I want to sum row subsets in groups of 10 (bin). Is there an efficient way to do this in R without using loops. Looping takes forever to perform this task! For example suppose we have the matrix > matrix(1:12,6,2) [,1] [,2] [1,] 1 7 [2,] 2 8 [3,] 3 9 [4,] 4 10 [5,] 5 11 [6,] 6 12 my problem is to sdum for

Using R-2.12.1 and latticeExtra-0.6-14, I would like to understand why a rootogram displaying samples from the Poisson distribution looks like I expected it, whereas a rootogram using the normal distribution does not: library(latticeExtra) rootogram(~rpois(1000, lambda = 50), dfun = function(x) dpois(x, lambda = 50)) rootogram(~rnorm(1000), dfun = function(x) dnorm(x,mean(x),sd(x))) I

My R installation (Mac OS9.2.2) is working, but when I try to run the examples from Venables & Ripley (p. 6): R : Copyright 2001, The R Development Core Team Version 1.4.0 (2001-12-19) ... > library(MASS) > x<-rnorm(1000) > y<-rnorm(1000) > truehist(c(x,y+3),nbins=25) % Plots nicely > dd<-con2tr(kde2d(x,y)) % Doesn't complain > contourplot(z ~ x + y, data=dd,aspect=1) Error: couldn't find function "contourplot" > wireframe(z ~ x + y, data=dd,drape=T) Error: couldn't find function "wireframe" > -- ===...