similar to: Distribution fitting with binned data

Hi All, I apologise if this question has been answered before, but my background is a little different from most people using R, and the language we use seems to be different! I am trying to analyse some nuclear physics data, which consists of an ensemble of "energy" readings in a detector that, when binned, form a number of Gaussian shaped peaks superimposed on a varying background

Greetings, all I am having difficulty getting the fitdistr() function to return without an error on my data. Specifically, what I'm trying to do is get a parameter estimation for fracture intensity data in a well / borehole. Lower bound is 0 (no fractures in the selected data interval), and upper bound is ~ 10 - 50, depending on what scale you are conducting the analysis on. I read in the

Hi, I'm a newbie in using R and I would like to have a few clues as to how I could compute and plot a distribution of daily rainfall intensity in different categories. I have daily values (mm/day) for several years and I need to show the frequency of 0-1, 1-2.5, 2.5-5, 5-10, 10-20, 20+ mm/day. Can this be done easily? Thanks, Etienne

Dear R-useRs, I've written a contribute (in Italian language) concering fitting distribution with R. I believe it could be usefull for someones. It's available on CRAN web-site: http://cran.r-project.org/doc/contrib/Ricci-distribuzioni.pdf Here's the abstract: This paper deals with distribution fitting using R environment for statistical computing. It treats briefly some

Hi, I'm trying to create a plot showing the density distribution of some shipping data. I like the look of violin plots, but my data is not continuous but rather binned and I want to make sure its binned nature (not smooth) is apparent in the final plot. So for example, I have the number of individuals per vessel, but rather than having the actual number of individuals I have data in the

How to use ggplot to do the binned quantile plots(one type of scatter plot)? Hi all, I have done scatter plot: plot(x, y). Now I wanted to do binned quantile plots... can ggplot2 help me? For example, we bin x data into 10 bins. For each bin, we draw the 10 deciles of the corresponding y data in that bin as points/dots. And then accross all bins, we would like to connect the corresponding

Dear All, A simple question: packages like fitdistr should be ideal to analyze samples of data taken from a univariate distribution, but what if rather than the raw data of the observations you are given directly and only a histogram? I was thinking about generating artificially a set of data corresponding to the counts binned in the histogram, but this sounds too cumbersome. Another question is

Hi R-Users, I read some texts related to KS-tests. Most of those authors stated, that KS-Tests are not suitable for binned data, but some of them refer to 'other' authors who are claiming that KS-Tests are okay for binned data. I searched for sources and can't find examples which approve that it is okay to use KS-Tests for binned data - do you have any links to articles or

Dear all, I have a binned data that looks like this: > dat (-1,9] (9,19] (19,29] (29,39] (39,49] (49,59] (59,69] (69,79] 10063374 79 16 4 3 4 4 3 (79,89] (89,99] 6 2 I tried to plot a histogram overlayed with curve. With the following snippet: library(lattice) pdf("myfile.pdf") hist(dat)

Hi, Standard correlations (Pearson's, Spearman's, Kendall's Tau) do not accurately reflect how closely the model (GAM) fits the data. I was told that the accuracy of the correlation can be improved using a root mean square deviation (RMSD) calculation on binned data. For example, let 'o' be the real, observed data and 'm' be the model data. I believe I can calculate

Forum, I'm a grad student in Civil Eng, took some Stats classes that required students learn R, and I have since taken to R and use it for as much as I can. Back in my lab/office, many of my fellow grad students still use proprietary software at the behest of advisers who are familiar with the recommended software (Statistica, @Risk (Excel Add-on), etc). I have spent a lot of time learning

Hey Guys, i have the sample variances for 1000 samples, and i want to fit it to a chi-squared distribution. after making a loop for the simulation initially, i have the following code to compute the variances samples = replicate(n, rnorm(m, 0, 1), simplify=FALSE) variances = sapply(samples, var) summary(variances) can someone please help me fit this to a chi-squared distribution with n degrees

Dear all, I would like to know how can I create a binned column in a data.frame. The output that I would like is something like this: Start Binned_Start 1 0-5 2 0-5 6 5-10 8 5-10 13 10-15 ... Best regards João Fadista Ph.d. student UNIVERSITY OF AARHUS Faculty of Agricultural Sciences Dept. of Genetics and Biotechnology Blichers

Suppose I have a set of binned data, counts exceeding a series of arbitrary thresholds, a total N, a minimum and maximum, those sorts of things. Is there a "standard" method for estimating arbitrary quantiles from this? My initial thought is that the counts and min/max give me solutions at various points along the empirical cdf. As the data are roughly log-normal, I thought maybe I

Hi, I am attempting to tabulate binned data. The '1' represents the appearance of the focal mouse pup, and '2' represents the disappearance of the focal mouse pup. The code written below is intended to calculate the total time spent appeared out of 3600s. For Sample 1, both the hand calculation and R code yield the same result, 50. A problem seems to occur when '1' is the

Hey everyone, I've been reading an old scientific paper (well, not that old, about 15 years) and I want to verify the authors' statistical results. The paper is fairly unclear about what exactly they did, and none of the relatively simple commands I'm familiar with are producing results similar to theirs. The data is dose-response, recorded as binomial data: structure(list(X1 =

Hi, I have the binned data (observed and generated from model)  that I would like to test using the anderson-darling goodness of fit test.  But I'm not sure which package in R to use. I tried ad.test(...) but it does not recognise the test by Vito Ricci in FITTING DISTRIBUTIONS WITH R   > ad.test(hist_hume_beec[,1],hist_hume_beec[,2]) Error: could not find function "ad.test"

How do you fit a Weibull distribution in R?

Some background: I have some data on structural dependencies in a base of code artifacts. The dependency structure is reflected in terms of relative node degrees, with each node representing some code unit (just as an example). This gives me real data of the following form (sorry for the longish posting): dat1 <- c(0.00245098039215686, 0, 0, 0, 0, 0, 0, 0, 0.0563725490196078, 0, 0, 0,

Dear All, I am researching financial market microstructure and have approx 4 x 10^7 multivariate 2D data samples which I have counted into a 250 x 390 bin matrix (frequency counted 2D histogram) in order to more efficiently manage the volume of data. I now wish to construct a smooth kernel density estimate (Gaussian kernel function) using this binned data. Does anyone in the R community know