similar to: smoothing functions

Hi, I am getting some weird results here and I think I am missing something. I am trying to program a function that for a set of random variables drawn from uniform distributions plots that distribution of the second order statistic of the ordered variables. (ie I have n uniform distributions on [0, w_i] for w_i different w_j and i=1..n. I want to plot the distribution of the second order

Hi, I'm a recent R convert so I haven't quite figured out the details yet... How do I select one variable by another one? Ie if I want to draw the histogram of variable X only for those individuals that also have a value Y in a certain range? In STATA I would give something like: histogram X if ((Y>=A & Y<=B)) (The data is for individuals and each individual has a number of

Hi, I have estimated a step function and need to take the derivatives of this function at all points in the range. Does anyone know of any clever ways to do this? (I have already tried to fit a polynomial through the points in order to obtain a smooth representation and then take derivatives of this. Also tried to smooth it, and used an SG differentiator. Results are rather poor so far, in

Hi everyone, (This is related to my posting on chi-squared from a day ago. I have tried simulating this but I am still unable to calculate it analytically.) Let y be an n times 1 vector of random normal variables mean zero variance 1 and x be an n times k vector of random normal variables mean zero variance 1. x and y are independent. Then P is the projection matrix P=x*inv(x'*x)*x'

Deall all, I'm just learning R, but unfortunately I need to urgently do a rather more complex task so I need some help. I have just learnt the very basics a few days ago and am not ready yet to deal with panels and kernel densities, so a soft guidance would be most appreciated. I have a (very) large panel data set (400,000 individuals x50 time periods) and need to display the evolution of

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))

Dear all, I want to use kernel estimator to smooth some step funtions such as the empirical distribution and hazard function in survival analysis. Is there a function or package to deal with it in R? How do I use it? Thanks for your help. Jimmy

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

Hello, I have been trying to take the derivative of a quadratic B-spline obtained by using the COBS library. What I would like to do is similar to what one can do by using fit<-smooth.spline(cdf) xx<-seq(-10,10,.1) predict(fit, xx, deriv = 1) The goal is to fit the spline to data that is approximating a cumulative distribution function (e.g. in my example, cdf is a 2-column matrix with x

Dear all, I have define a discrete distribution P(y_i=x_i)=p_i, which I want to plot a CDF plot. However, I can not find a function in R to draw it for me after searching R and R-archive. I only find the one for the sample CDF instead my theoretical one. I find stepfun can do it for me, however, I want to plot some different CDF with same support x in one plot. I can not manage how to do it with

Dear R-Users, I have the CDF of a discrete probability distribution. I now observe a change in this CDF at one point. I would like to find a new CDF such that it has the shortest Kullback-Leibler Distance to the original CDF and respects my new observation. Is there an existing package in R which will let me do this ? Google searches based on entropy revealed nothing. Kind regards, Tolga

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

Hi R-users,   I have this code below and I understand the error message but do not know how to correct it.  My question is how do I get rid of “with absolute error < 7.5e-06” attach to value of cdf so that I can carry out the calculation.   integrand <- function(z) { alp  <- 2.0165   rho  <- 0.868   # simplified expressions   a      <- alp-0.5   c1     <-

Hello! I have a problem with integrate() in my function nctspa(). Integrate produces an error message "evaluation of function gave a result of wrong length". I don't know what that means. Could anyone suggest me what is wrong with my function? These are the examples of function calls that work OK: nctspa(a=1:10,n=5) nctspa(a=1:10, n=5, mu=2, theta=3, renorm=0) This does not work:

My machine has 8GB memory. I had quit all other programs that might take a lot of memory when I try the script (before I post the first message in this thread). The cdf file is of only 741 MB. It is strange to me to see the error. On Mon, Dec 28, 2009 at 2:38 AM, Wolfgang Huber <whuber at embl.de> wrote: > Dear Peng Yu > > how big is the RAM of your computer? You could try with

Hello R-Users, I'm new to R so I apologize in advance for any big mistake I might be doing. I'm trying to fit a set of samples with some probabilistic curve, and I have an important question to ask; in particular I have some data, from which I calculate manually the CDF, and then I import them into R and try to fit: I have the x values (my original samples) and the y values

Please give me some help, many thanks. I graphed a expected CDF line of a binomial distribution on a graph, And I have some observed points (observed CDF) from 4 groups fall on the smooth CDF line. I cannot really get the legend I want legend ('topleft', c('a, 'b', 'c', 'd', 'expected CDF'), col=c(1,2,3,4), pch=c(0,1,2, 3, '' ),

Does backgroundrb support SSL like drb? I was unable to find any sample configurations including SSL. Any help would be appreciated, thanks. -jim salinas -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://rubyforge.org/pipermail/backgroundrb-devel/attachments/20080521/9588825a/attachment.html>

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.

Hi, I am attempting to graph a Kaplan Meier estimate for some claims using the survfit function. However, I was wondering if it is possible to plot a cdf of the kaplan meier rather than the survival function. Here is some of my code: library(survival) Surv(claimj,censorj==0) survfit(Surv(claimj,censorj==0)~1) surv.all<-survfit(Surv(claimj,censorj==0)~1) summary(surv.all) plot(surv.all)