similar to: Extracting values from a ecdf (empirical cumulative distribution function) curve

https://cran.r-project.org/web/views/Optimization.html (Cran's optimization task view -- as always, you should search before posting) In general, nonlinear optimization with nonlinear constraints is hard, and the strategy used here (multiplying by a*b < 1000) may not work -- it introduces a discontinuity into the objective function, so gradient based methods may in particular be

I ran the following script. I satisfied the constraint by making a*b a single parameter, which isn't always possible. I also ran nlxb() from nlsr package, and this gives singular values of the Jacobian. In the unconstrained case, the svs are pretty awful, and I wouldn't trust the results as a model, though the minimum is probably OK. The constrained result has a much larger sum of squares.

Full_Name: Martin Frith Version: R-2.0.0 OS: linux-gnu Submission from: (NULL) (134.160.83.73) I have large vectors containing 100,000 to 20,000,000 numbers. However, they only contain a few hundred *distinct* numbers (e.g. positive integers < 200). When I do ecdf(v), it either runs out of memory, or it succeeds, but when I plot the ecdf with postscript, the output is unnecessarily bloated

I am using nlsLM {minpack.lm} to find the values of parameters a and b of function myfun which give the best fit for the data set, mydata. mydata=data.frame(x=c(0,5,9,13,17,20),y = c(0,11,20,29,38,45)) myfun=function(a,b,r,t){ prd=a*b*(1-exp(-b*r*t)) return(prd)} and using nlsLM myfit=nlsLM(y~myfun(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05), lower = c(1000,0),

I am not as expert as John, but I thought it worth pointing out that the variable substitution technique gives up one set of constraints for another (b=0 in this case). I also find that plots help me see what is going on, so here is my reproducible example (note inclusion of library calls for completeness). Note that NONE of the optimizers mentioned so far appear to be finding the true best

Hi! I've noticed the ecdf() R code (R ver. 2.5.0) contains two call to sort: --- [R-code] --- ecdf <- function(x) x <- sort(x) n <- length(x) if (n < 1) stop("'x' must have 1 or more non-missing values") vals <- sort(unique(x)) rval <- approxfun(vals, cumsum(tabulate(match(x, vals)))/n, method

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

> On Jun 18, 2017, at 6:24 AM, Manoranjan Muthusamy <ranjanmano167 at gmail.com> wrote: > > I am using nlsLM {minpack.lm} to find the values of parameters a and b of > function myfun which give the best fit for the data set, mydata. > > mydata=data.frame(x=c(0,5,9,13,17,20),y = c(0,11,20,29,38,45)) > > myfun=function(a,b,r,t){ > prd=a*b*(1-exp(-b*r*t)) >

David, thanks for the response. In your response the quantile function (if I see correctly) runs on the columns versus I need to run it on the rows, which is an easy fix, but that is not exactly what I had in mind... essentially we can remove t() from my original code to make "res" look like this: res<-apply(z, 1, quantile, probs=c(0.3)) but after all maybe I did not explain

Hi, all ecdf function (Empirical Cumulative Distribution Function) in "stats" package counts from smaller values to larger values. However, I want to draw it by counting from larger value to smaller values and I couldn't find options for this purpose. How can I draw ecdf or ecdf like graph by counting from larger values to smaller values. Thank you in advance. Hyunchul Kim

I've seen a number of problems like this over the years. The fact that the singular values of the Jacobian have a ration larger than the usual convergence tolerances can mean the codes stop well before the best fit. That is the "numerical analyst" view. David and Jeff have given geometric and statistical arguments. All views are useful, but it takes some time to sort them all out and

It would depend on which one of the 9 quantile definitions you are using. The discontinuous ones aren't invertible, and the continuous ones won't be either, if there are ties in the data. This said, it should just be a matter of setting up the inverse of a piecewise linear function. To set ideas, try x <- rnorm(5) curve(quantile(x,p), xname="p") The breakpoints for the

Platforms : SunOS and Linux, both using gcc 2.95.[23] R versions : 1.2.3 and "R-devel" I came accross this when trying to debug a somewhat sub-optimal behavior of ecdf() from package stepfun. What happens is that approxfun(*, ties = <function>) does not always return the same result for identical arguments. I tried "min", "max", and "mean" all of

Dear Experts, The following is my "Iterator". When I try to write a new function with itel, I got error. This is what I have: > supDist<-function(x,y) return(max(abs(x-y))) > > myIterator <- function(xinit,f,data=NULL,eps=1e-6,itmax=5,verbose=FALSE) { + xold<-xinit + itel<-0 + repeat { + xnew<-f(xold,data) + if (verbose) { + cat( +

Hi everyone, I've been having difficulty writing wrapper functions for some functions where those same functions include other functions with eval() calls where the environment is specified. A very simple example using function lmer from lme4: lmerWrapper <- function(formula, data, family = gaussian, method = c("REML", "ML", "PQL", "Laplace",

Folks, I'm doing fine with using orthogonal polynomials in a regression context: # We will deal with noisy data from the d.g.p. y = sin(x) + e x <- seq(0, 3.141592654, length.out=20) y <- sin(x) + 0.1*rnorm(10) d <- lm(y ~ poly(x, 4)) plot(x, y, type="l"); lines(x, d$fitted.values, col="blue") # Fits great! all.equal(as.numeric(d$coefficients[1] + m

Hey there, I apologize if this is an irritatingly simple question ... I'm a new user. I can't understand why R flips the sign of all data values when reading in external text files (tab delimited or csv) with the read.delim or read.csv functions. The signs of data values also seem to be flipped after assigning a new data.frame from within R (xnew <-- edit(data.frame()). What am

Dear all, I have several character strings with a high number of different levels. unique(x) gives me values in the range of 100-200. This creates problems as I would like to use them as predictors in a coxph model. I therefore would like to convert each of these strings to a new string (x_new). x_new should be equal to x for the top n categories (i.e. the top n levels with the highest

Hi, I am trying to write a function to automate multiple graph generation. My data looks like: Table of numeric values with the following headers: timeM1 statusM1 xM1 timeM2 statusM2 xM2 timeM3 statusM3 xM3 1 2 3 4 5 6 Where M1,M2, M3 hve no similarity except they have a max string length of 7. Examples are mcw0045, adl0003, lei0101. Now, what I want to do is Function(M1, M2,

I would like you consider that the function ecdf could be extended in the following way to handle weights when computing Empirical distribution Functions. There exist particular cases that supports this kind of extension, see for example: Rao, C. R., 1997. Statistic and True. Putting chance to work. World Scientific Publishing. Cox, D. R., 1969. Some Sampling Problems in Technology. New