similar to: ks.test in ctest package (PR#934)

Hi, I couldn't figure out how to use the functions from the ctest library. I'm using the r-base package that comes with debian potato. library("ctest") told me that no such package existed. I checked the CRAN, but no such package was availiable, instead I was told that it would be part of the standard installation. But functions from ctest like shapiro-wilk don't work. The

I have written a program in C that two xy datasets, aligns these 2 datasets based on shared features, transforms them into equal sized histograms, transforms the histograms into cumulative distribution functions (via GSL) and finally performs a KS_test. I am wanting to validate my program's results and figure'd i would use R but i am kinda stuck at ithe histograms (I have 2 histogram

Thanks for sharing the questions and responses! Is it possible to appreciate how much the coefficients matter in one or the other model? Say, using Biau's example, using coxph, as.factor(grade2 == "high")TRUE gives hazard ratio 1.27 (rounded). As clinician I can grasp this HR as 27% relative increase. I can relate with other published results. With survreg the Weibull model gives a

This question is mainly aimed at Kurt Hornik as author of the ctest package, but I'm cc'ing it to r-help as I suspect there will be other valuable opinions out there. I have been attempting 2 sample Kolmogorov-Smirnov tests using the ks.test function from the ctest package (ctest v.0.9-15, R v.0.63.3 win32). I am comparing fish length-frequency distributions. My main reference for the

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

Full_Name: Ziheng Yang Version: 1.3.1 OS: Windows 98 Submission from: (NULL) (172.136.54.89) I noticed that qbeta is sometimes wrong and the error is not even due to the beta parameters being too extreme. I am calculating the quantiles corresponding to cdf = 0.05, 0.15, ..., 0.95. The value corresponding to cdf=0.25 is wrong while all other values are correct. qbeta(0.05, 0.143891, 0.05) =

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, '' ),

Hi, I need to write two inequalities depend on cumulative distribution (CDF) of beta binomial distribution where alpha and beta are unknown and need to find them. CDF of betabinomial(2,10,alpha,beta) <0.3<=CDF of betabinomial(3,10,alpha,beta) and CDF of betabinomial(5,10,alpha,beta) <0.8<=CDF of betabinomial(6,10,alpha,beta) How I can do that using r studio package? I tried to do

Hello R Helpers, Business - 64 bit windows 7, R 2.11.1 I am trying to match the character contents of one list, called 'exclude', to those of a second list, called 'dataset' dataset is a list of file names with folder locations, and looks like this when called: > dataset [1] "A/10-10-29a-13.cdf" "A/10-10-29a-14.cdf" "A/10-10-29a-15.cdf"

Suppose I have a set of binned data, counts exceeding a series of arbitrary thresholds, a total N, a minimum and maximum, those sorts of things. Is there a "standard" method for estimating arbitrary quantiles from this? My initial thought is that the counts and min/max give me solutions at various points along the empirical cdf. As the data are roughly log-normal, I thought maybe I

Hi r-users,   I hope somebody can help me to understand the error message.  Here is my code; ## Newton iteration newton_gam <- function(z) { n   <- length(z)   r   <- runif(n)   tol <- 1E-6   cdf <- vector(length=n, mode="numeric")   fprime <- vector(length=n, mode="numeric")   f   <- vector(length=n, mode="numeric")     for (i in 1:1000)   {

Hi R-users,   I have this code here: library(numDeriv)   fprime <- function(z) { alp  <- 2.0165;   rho  <- 0.868;   # simplified expressions   a      <- alp-0.5   c1     <- sqrt(pi)/(gamma(alp)*(1-rho)^alp)   c2     <- sqrt(rho)/(1-rho)   t1     <- exp(-z/(1-rho))   t2     <- (z/(2*c2))^a   bes1   <- besselI(z*c2,a)   t1bes1 <- t1*bes1   c1*t1bes1*t2 }   ## Newton

Folks, Normally, in a data frame, one observation counts as one observation of the distribution. Thus one can easily produce a CDF and (in Splus atleast) use cdf.compare to compare the CDF (BTW: what is the R equivalent of the SPlus cdf.compare() function, if any?) However, if each point should not count equally, how can I weight the points before comparing the distributions? I was thinking of

R version: 0.62.1 (June 15, 1998) I just very naively attempted to grab the 'bicreg' package for Bayesian model selection from the StatLib library, and get it running under R. I've hit a brick wall very quickly, and as an R novice I'm not sure where to go next. Here's what happened: > bicreg(as.matrix(hiv[,c(-17,-18)]),as.matrix(hiv[,18])) Error: invalid formula >

Hi all, Just a random shot in the dark. Does anyone have/know of a function for the CDF of a generalized error dist? -- Elliot Williams (ewilliams at ucsd.edu) Economics Department, UC San Diego -------------- next part -------------- An embedded message was scrubbed... From: Elliot Williams <ewilliams at ucsd.edu> Subject: [R] Generalized Error Distribution (Exponential Power) CDF?

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