similar to: probability plot correlation coefficient

hi, I wrote a set of R functions for estimating what is the probability function that best fits a set of data. I wrote them based in this response: /http://tolstoy.newcastle.edu.au/R/help/03b/1714.html/ I extracted the relevant segment of the link above: //> PPCC <- function(shape, scale, x) { # only for weibull / + x <- sort(x) + pp <- ppoints(x) + cor( qweibull(pp, shape=shape,

Hello, I am trying to generate random survival times by inverting the function,  S(t)= exp(b*F(t)), where b is constant and F(t) is some cumulative distribution function, let say that F(t) is cdf of normal distribution or any others distributions.   as we know that S(t) has uniform distribution on  (0,1) so we can write that U= exp(b*F(t)), where U is uniform (0,1). Now to generat the time t, we

Stock returns and other financial data have often found to be heavy-tailed. Even Cauchy distributions (without even a first absolute moment) have been entertained as models. Your qq function subtracts numbers on the scale of a normal (0,1) distribution from the input data. When the input data are scaled so that they are insignificant compared to 1, say, then you get essentially the

Hi, I want to create upper and lower 95% confidence intervals for a p-p plot of an empirical distribution with a theoretical gamma distribution. This is my code: x<-rgamma(100,shape=2, rate=1) # empirical data fitdistr(x,"gamma") # fit a gamma distribution dist<-pgamma(x,shape=1.9884256 ,rate=0.8765314 ) # fitted distribution, using the loglikelihood estimated parameters

When I look at ppoints I see: ppoints<-function (x) { n <- length(x) if (n == 1) n <- x (1:n - 0.5)/n } However Venables & Ripley (2nd ed, p 165) say ppoints() should return (i-1/2)/n for n>=11; (i-3/8)/(n+1/4) for n<=10. The version below should work as described: ppoints<-function (x) { n <- length(x) if (n <= 10) (1:n - 0.375)/(n + 0.25) else (1:n - 0.5)/n

Hello, Our professor asked us to do probability plotting using weibull paper, exponential paper, normal, log-normal paper, etc. I know I can create Q-Q plot for normal dist. and see if all te points are on one line. How do I go about other distributions? I tried generating different samples and use the general qq function. However, I could not do it since I don't know the population

Dear r-bugs, Whilst playing with ppoints I discovered that when one uses it directly, occasional NA's in a vector also become data fractions: ppoints(c(1,2,NA,4)) Would it be a good idea to add a warning message as in: ppoints <- function (n, a = ifelse(n <= 10, 3/8, 1/2)) { if(any(is.na(n))) warning("'n' contains NA's") if(length(n) > 1) n <-

Hello, I'm Giovanni from ROMA.. I can't find a solution for the error: "error using packet 1 the y field is not specified and it has not a default value" (this is my traslation from italian language) The code is: pc<- qqmath(~valori, distribution=function(p) qweibull(p,beta,alpha), prepanel = prepanel.qqmathline, panel = function(x, y) {

Hello, I'm Giovanni from ROMA.. I can't find a solution for the error: "error using packet 1 the y field is not specified and it has not a default value" (this is my traslation from italian language) The code is: pc<- qqmath(~valori, distribution=function(p) qweibull(p,beta,alpha), prepanel = prepanel.qqmathline, panel = function(x, y) {

I think qqline does not do exactly what it is advertised to do ("`qqline' adds a line to a normal quantile-quantile plot which passes through the first and third quartiles."). Consider the graph: tmp <- qnorm(ppoints(10)) qqnorm(tmp) qqline(tmp) The line (which I expected go through all the points), has a slightly shallower slope than does the points plotted by qqnorm. I think

Hi, I am trying to write a function with the following codes and I would like it to return the values for "alpha beta para parab " seperately. Then I would like to use this funstion for "variable" with factor "a" and "b". But the result turns out to be a matrix with element like "Numeric,2" ... I guess they are just the values for

Do you know of a reference that discusses alternative choices for plotting positions for a normal probability plot? The documentation for qqnorm says it calls ppoints, which returns qnorm((1:m-a)/(m+1-2*a)) with "a" = ifelse(n<=10, 3/8, 1/2)? The help pages for qqnorm and ppoints just refer to Becker, Chambers and Wilks (1988) The New S Language (Wadsworth & Brooks/Cole),

Dear R community. I am using the identify() function to identify outliers in my dataset. This is the code I am using: #################################################################### # Function to allow identifying points in the QQ plot (by mouseclicking) qqInteractive <- function(..., IDENTIFY = TRUE) { qqplot(...) -> X abline(a=0,b=1) if(IDENTIFY) return(identify(X))

>>>>> On Tue, 31 Aug 1999 14:57, Werner Stahel <stahel@stat.math.ethz.ch> said: WSt> Here is a suggestion. It seems that qqplots, comparing a sample WSt> to a distribution other than the normal, are not explicitly WSt> available in S or R. I found (in S-plus / Trellis it is, see below) WSt> qqplot(y, rt(300, df = 5)) WSt> as an

Hi, This is essentially a reposting of http://tolstoy.newcastle.edu.au/R/devel/05/11/3305.html which had no responses, and the behaviour reported there persists in r-devel as of yesterday. (1) sort() with non-null partial > x = rnorm(100000) > keep = as.integer(ppoints(10000) * 100000) > system.time(sort(x)) [1] 0.05 0.00 0.04 0.00 0.00 > system.time(sort(x, partial = keep)) [1]

Hello, I have a doubt about the plotting position of the theoretical quantile for the qqnorm command in R. Let F be the theoretical distribution of Y, we observed a sample of size n, y1,y2, ..., yn. We then sort it and comspare these empirical quantiles to the expected ones from F. For the plotting poition, there are several options: 1. i/(n+1) 2. (i-.375)/(n+.25) 3. (i- .3175)/ (n + .365) etc.

Hi, does the shapiro wilk test in R-1.3.0 work correctly? Maybe it does, but can anybody tell me why the following sample doesn't give "W = 1" and "p-value = 1": R> x<-1:9/10;x [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 R> shapiro.test(qnorm(x)) Shapiro-Wilk normality test data: qnorm(x) W = 0.9925, p-value = 0.9986 I can't imagine a sample being

(1) ...need to speed up a monte-carlo sampling...any suggestions about how I can get R to use all 8 cores of a mac pro would be most useful and very appreciated... (2) spent the last few hours trying to get pnmath to compile under os- x 10.5.4... using gcc version 4.2.1 (Apple Inc. build 5553) as downloaded from CRAN, xcode 3.0... ...xcode 3.1 installed over top of above after

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik