search for: historgramming

CID DISTRICT SCHOOL YRofSTUDY DATE.ENTRY SEX AGE EGGS.M EGGS.MII EGGS.H POBADN0130 PO Badongo 0 18.1.08 2 11 0 -1 73 POBADN0117 PO Badongo 0 18.1.08 1 10 0 -1 59 POBADN0160 PO Badongo 0 18.1.08 1 11 0 -1 56 POBADN0112 PO Badongo 0 18.1.08 2 10 0 -1 47 POBADN0053 PO Badongo 0 18.1.08 1 8 0 -1 42 POBADN0141 PO Badongo 0 18.1.08 2 11 0 -1 36 POBADN0056 PO Badongo 0 18.1.08 1 8 0 -1 26 POBADN0127 PO

This may be a begining question. If so, please bear with me. If I have some data that based on the historgram and other plots it "looks" like a beta distribution. Is there a function or functions within R to help me determine the model parameters for such a distirbution? Similarily for other "common" distirbutions, Poisson(lambda), Chi-Square(degrees of freedom, chi-square

...39;t show how to do this. How does one implement, for example, the example in the text in which tax accountants are indexed by both state and sex? Is there a way to use tapply() to put the vector into bins defined by the levels instead of having to exactly match the levels? It seems some sort of historgramming is what's needed, but hist() does plots, not just the categorization into bins. -- Art Salwin salwin at mitretek.org (202) 863-2985 (202) 863-2988 (fax) -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~horn...

Hello, I'm trying to plot a historgram with a density plot superimposed: hist(x, seq(-1, 1, by = 0.1), prob = T, col = "blue") lines(density(x, bw = 0.1)) rug(x) I don't want to add rug(x) but the histogram will not be plotted unless rug(x) is there. It does not work if the "hist" line alone is present. Can you help? Thanks, Kim [[alternative HTML version

Dear: I have two series data, which are: p1-obs p1-exp 13 13 20 23.296 22 18.139 19 14.972 10 12.576 16 10.658 8 9.0794 4 7.7625 3 6.6536 5 5.7141 7 4.9146 4 4.2321 0 3.648 4 3.1471 4 2.7168 2 2.3468 4 2.0283 2 1.7537 2 1.517 5 1.3127 1 1.1362

Dear r user, please find my attached sample of the dataset i? am using to create a crosstable and eventually plot a histogram from the output. I am using? the cut2 function to create bins, about 7 of them using the code after reading the data: cluster <- cut2(cross_val$value, g=7) I get the warning: Warning message: In min(xx[xx > upper]) : no non-missing arguments to min; returning Inf

Hi, I wanted to plot the histogram of a vector and then, plot the density function of subsets of the vector on the histogram. So I use truehist in MASS package and lines(density) as follows: length(b) = 1000 truehist(b) lines(density(b[1:100])) however the density plot of the first 100 points exceeds the max of y axis (see attached). how is it possible to make a graphics so that the density plot

The 5-patch series to follow this email extends KVM-hypervisor and Linux guest running on KVM-hypervisor to support pv-ticket spinlocks, based on Xen's implementation. One hypercall is introduced in KVM hypervisor,that allows a vcpu to kick another vcpu out of halt state. The blocking of vcpu is done using halt() in (lock_spinning) slowpath. Changes in V4: - reabsed to 3.2.0 pre. - use APIC

The 5-patch series to follow this email extends KVM-hypervisor and Linux guest running on KVM-hypervisor to support pv-ticket spinlocks, based on Xen's implementation. One hypercall is introduced in KVM hypervisor,that allows a vcpu to kick another vcpu out of halt state. The blocking of vcpu is done using halt() in (lock_spinning) slowpath. Changes in V4: - reabsed to 3.2.0 pre. - use APIC

Hi everyone, first of all, I would like to say that I am a newbie in R, so I apologize in advance if my questions seem to be too easy for you. Well, I'm looking for periodicity in histograms. I have histograms of certain phenomenons and I'm asking whether a periodicity exists in these data. So, I make a periodogram with the function spec.pgram. For instance, if I have a histogram h, I

Dear all, We are trying to fit a non linear model to dispersal data. It seems that sometimes when the fit of the model of the data is not very good we start getting singular gradient errors. However if we modify slightly the data this won't occurr. We have also tried changing the initial parameter values and the algorithm for fitting in nls but didn't help. So we ended up programming a