similar to: How to calculate area between ECDF and CDF?

> Almost any method of fitting a density estimate would work on > integrating (numerically) the result. it is a nice idea concerning the monotony property, which will be obtained automatically, but I am going to use results of approximation analytically > In particular, look at package polspline, where > p(old)logspline does the integration for you. thank you, I am going to

Hello all, I would like to plot the emperical CDF, normal CDF and pareto CDF in the same graph and I amusing the following codes. "z" is a vector and I just need the part when z between 1.6 and 3. plot(ecdf(z), do.points=FALSE, verticals=TRUE, xlim=c(1.6,3),ylim=c(1-sum(z>1.6)/length(z), 1)) x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4373,-0.2398), col="red") y

Dear R Users, I am trying to suppress the information printed by the ecdf function during an assignment. Various alternatives have failed me so far: > a=summary(ecdf(rnorm(100)))["1st Qu."] Empirical CDF: 100 unique values with summary > invisible(a=summary(ecdf(rnorm(100)))["1st Qu."]) Empirical CDF: 100 unique values with summary > (function()

Hi r-users, I would like to overlap 2 ecdf plots.  I tried this below and it gives me two plots of ecdf but just both just in black. par(mar=c(4,4,2,1.2),oma=c(0,0,0,0),xaxs="i", yaxs="i") plot(ecdf(datobs)) lines(ecdf(gam_sum_gen)) Then I try to add colors etc and also the legend but fail. par(mar=c(4,4,2,1.2),oma=c(0,0,0,0),xaxs="i", yaxs="i")

Dear UseRs, maybe is a silly question: how can I get Empirical CDF values from an object created with ecdf()?? Using print I obtain: Empirical CDF Call: ecdf(t) x[1:57] = 4.1, 4.4, 4.5, ..., 491.3, 671.27 Thanks in advance. Regards, Vito Diventare costruttori di soluzioni Became solutions' constructors "The business of the statistician is to catalyze the scientific

Dear R users, I'm using Ecdf (Hmisc library) to plot four cdf in a same graphic. In this graphic I also plot the 0.99 quantile for these cdf. I successfully plot cdfs using different types of line to distinguish them, but I can't determine the type of lines showing 0.99 quantile. Is there a way to assign different line types for quantile lines in Ecdf plot? Best regards, -- Mateus da

Good day everyone, I want to assess the error when fitting a Gram-Charlier CDF to some data 'ws', that is, I want to calculate: Err = |ecdf(ws) - GCh_ser(ws)| The problem is, I cannot get the F(x) values from the ecdf. 'Summary(ecdf())' returns some of the x-axis values, but how do you get the F(x) values? Thank you for any help you can provide. Regards, Augusto

Good Afternoon, I'm trying to create a cdf plot, with the following code. It works well, but I have little doubt, if you can help solve. When I create the plot, like the graph line would still not appear with point #cdf x<-table(Dataset$Apcode) View(s) hist(s) *plot(ecdf(x))* x<-1 37607 2 26625 3 5856 4 25992 5 30585 6 16064 7 9850 .. ... .. 186 52 -- View this message in

Hi r-users,   I have 2 sets of data and I would like to superimpose this cumulative density in one graph.   I know how to put the 2 graphs in one same graph but my problem is the data are different.   > z[1:20]  [1]  2.02347388  3.19514379  0.05188875  1.41333812  3.50249892  4.34272676  6.65639581  5.01533819  4.18207505 [10]  2.87615551  2.28202152  0.49431541  0.06570579  5.68352438

Dear Prof. Brian Ripley, first of all thank you for your answer, I do appreciate how do you manage to keep successfully all your activities and answer posts in this forum! > An empirical CDF is a step function: it does not have a > derivative at the jump points, and has a zero > derivative everywhere else. of course! Let me add few words concerning my simple motivation. 1.

Hi, I'm bit confused about ecdf (read the help files but still not sure about this). I have an analytical expression for the pdf, but want to get the empirical cdf. How do I use this analytical expression with ecdf? If this helps make it concrete, the pdf is: f(u) = \sum_{t = 1}^T 1/n_t \sum_{i = 1}^{n_t} 1/w K((u - u_{it})/w) where K = kernel density estimator, w = weights, and u_{it} =

Hello! I want to plot a P-P plot. So I've implemented this function: ppplot <- function(x,dist,...) { pdf <- get(paste("p",dist,sep=""),mode="function"); x <- sort(x); plot( pdf(x,...), ecdf(x)(x)); } I have two questions: 1. Is it right to draw as reference line the following: xx <- pdf(x,...); yy <- ecdf(x)(x); l <- lm(

Dear Group, I am using the ecdf function as follows: cawa.cdp <- ecdf(cawaocc$LEFF80) summary(cawa.cdp) Empirical CDF: 223 unique values with summary Min. 1st Qu. Median Mean 3rd Qu. Max. 0.07918 1.35700 1.68600 1.61000 1.91200 2.70000 I can see by the summary that the y-value for the 3rd quartile is 1.912. How can I obtain the x-value for a specified y-value (e.g., 0.8)?

Hi, I would like to overlap the cdf curve for observed and generated data  Here is my code: plot(cdf,main ="CDF of the sum for winter season-Hume",cex.axis=1.2,xlab="Rainfall (mm)", xaxs="i",yaxs="i",col=c("black","red"), lty=c(1,1),ylab="Cumulative probability", xlim=c(0,800),lwd=1) lines(ecdf(datobs))

Hi r-users,   I would like to compare the cdf between historical and predicted. My x.obs and x.pre are the frequency data in classes of 0-300. I tried: plot(ecdf(x.obs),ecdf(x.pre),type="l",col="red")   and it gives me: Error in plot.stepfun(x, ..., ylab = ylab, verticals = verticals, pch = pch) :   argument 4 matches multiple formal arguments   Thank you so much for any

Hi all, I would like to plot part of the emperical CDF. Suppose the variable is x, I just need the part when x>1,therefore, I am using the following codes. tail <- x>1 plot(ecdf(x[tail]), do.points=FALSE, verticals=TRUE) The "x" value starts from 1, but the yaxs still begins from 0, not the corresponding value when "x" is 1. How can I make it match? Could anyone

Hi all, is there any package in R capable of smooth approximation of CDF basing on given sample? (Thus, I am not speaking about ecdf) In particular, I expect very much that the approximation should subject to the property: f(x0)<=f(x1) for x0<x1, where x0 and x1 belong to range of the sample given. Polynomial approximation could be OK for me as well. P.S.

Dear List: I am trying to find the area between two ECDFs. I am examining the gap in performance between two groups, males and females on a student achievement test in math, which is a continuous metric. I start by creating a subset of the dataframe male<-subset(datafile, female="Male") female<-subset(datafile, female="Female") I then plot the two CDFs via

This question might seem silly, because I felt that it MUST be in the mailing list archives or help files somewhere, but I simply couldn't find it. I want to make some simple CDF (cumulative distribution function) plots to check whether distributions are Gaussian / normal. But in order to check how "normal" the distribution is, I really need the y-axis to be Gaussian as well

Hi all, Trying to understand the logistic regression performed by glm (i.e. when family='binomial'), and I'm curious to know how it treats perfect success. That is, lets say I have the following summary data x=c(1,2,3,4,5,6) y=c(0,.04,.26,.76,.94,1) w=c(100,100,100,100,100,100) where x is y is the probability of success at each value of x, calculated across w observations.