similar to: maximum difference between two ECDF's

Dear wizaRds, I use the MASS::kde2d function to estimate density of the two first principal components. I do that to have a graphic visualisation of a "group structure" in my dataset. So far, no problem. But i would like to estimate that density using weights according to the COS?? values that tells me if my observation is well represented on the factorial plan 1-2. I would like to

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} =

Muy buenas, Tengo la siguiente duda/problema, He optimizado con éxito un problema de este tipo: \sum f(x_i) donde f es una curva exponencial (función no lineal) sujeto a: a_i < x_i < b_i y \sum f(x_i) < Presupuesto Vamos, es repartir un presupuesto forzando a que inviertas como poco a_i y como mucho b_i para cada i Esto lo hecho correctamente usando el paquete:

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Hi all; I recently have been used 'maxLik' function for maximizing G2StNV178 function with gradient function gradlik; for receiving this goal, I write the following program; but I have been seen an error  in calling gradient  function; The maxLik function can't enter gradlik function (definition of gradient function); I guess my mistake is in line ******** ,that the vector  ‘h’ is

Hi Firstly , happy Christmas to R-Help! Secondly, I wonder if anyone can help me with the following query: I am trying to reproduce some explicit probability calculations performed in APPL (a Maple extension for computational probability). For instance, in APPL, to compute the probability that the sum of 10 iid uniform variables [0,1] will be between 4 and 6, (i..e Pr( 4 < \sum_{i=1}^{10}X_i

I tried lrm in library(Design) but there is always some error message. Is this function really doing the weighted logistic regression as maximizing the following likelihood: \sum w_i*(y_i*\beta*x_i-log(1+exp(\beta*x_i))) Does anybody know a better way to fit this kind of model in R? FYI: one example of getting error message is like: > x=runif(10,0,3) > y=c(rep(0,5),rep(1,5)) >

Dear helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > Suppose I have the following data set: > > id x y > 023 1 2 > 023 2 5 > 023 4 6 > 023 5 7 > 412 2 5 > 412 3 4 > 412 4 6 > 412 7 9 > 220 5 7 > 220 4 8 > 220 9 8 > ...... > Now I want to compute the

Dear everyone, I'm coding the Horowitz-Spokoiny (2001) test [1], and I would be very grateful or some advice regarding the Kernel density (apologies beforehand if my terminology is not fully correct). I have looked into ksmooth and npreg, but with no success. Given a (n x p) matrix of covariates X, I need to construct the following matrix of Kernel densities or weights: w(x_i, x_j) =

I am more than 'a little disappointed' that you expect a detailed explanation of the problems with your 'bug' report, especially as you did not provide any explanation yourself as to your reasoning (nor did you provide any credentials nor references). Note that 1) Your report did not make clear that this was only relevant to prediction intervals, which are not commonly used.

Hi all, I have been trying to add the line: h = n - p0 + 1; just after h = n / 2 + 1; (line 131) in the source code (the original paper mention this is possible for p0&lt;n). with p0 declared as an int (actually i used the same declaration protocol as n everywhere in the code). The &quot;new&quot; source compiles, but when i give it reasonable values of p0, it runs unto

Peter, This is a highly structured text. Just for the discussion, I separate the building blocks, where (D) and (E) and (F) are new: BEGIN OF TEXT -------------------- (A) Non-?NULL? ?weights? can be used to indicate that different observations have different variances (with the values in ?weights? being inversely proportional to the variances); (B) or equivalently, when the elements of

I have a function written for Splus, when I run it in R I obtain get an error because the function has the elements "0.d0" and "2.d0". How can I change it to run in R? The function can be found in page 230 from http://www.stat.wisc.edu/~mchung/teaching/stat471/stat_computing.pdf Function is as follows: gauher <- function(n) {# Gauss-Hermite: returns x,w so that

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 all, I'm working with data to which I'm fitting three-parameter weibull distributions (shape, scale & shift). The data are of low sample sizes (between 10 and 80 observations), so I'm reluctant to check my fits using chi-square (also, I'd like to avoid bin choice issues). I'd use the Kolmogorov-Smirnov test, but of course this is invalid when the distribution

Hi. I'm trying to plot two ecdf's on the same graph using two different colors. I can plot using the same color, but it doesn't work when I change colors? Any suggestions? Thanks in advance for your help. x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46) y <- c(1.15, 0.88, 0.90, 0.74, 1.21) plot(ecdf(x)) # it works without col='blue', but doesn't

Hi all, I have a problem using rdirichlet{gtools}. For Dir( a1, a2, ..., a_n), its mode can be found at $( a_i -1)/ ( \sum_{i}a_i - n)$; The means are $a_i / (\sum_{i} a_i ) $; I tried to study the above properties using rdirichlet from gtools. The code are: ############## library(gtools) alpha = c(1,3,9) #totoal=13 mean.expect = c(1/13, 3/13, 9/13) mode.expect = c(0, 2/10, 8/10) #

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 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

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(