similar to: Anderson-Darling GoF (re-sent)

Hi, I'm not a statistician so sorry for possible trivial questions ... I want to perform a GoF test on sample data against several distribution (like Extreme Value, Phase Type, Pareto, ...). Since I suspect a long-tailed behaviour on data I want to use Anderson-Darling (AD) GoF test because it's well known it's more sensible to tail data. Looking at R packages the only AD test is

Hi, I have the binned data (observed and generated from model)  that I would like to test using the anderson-darling goodness of fit test.  But I'm not sure which package in R to use. I tried ad.test(...) but it does not recognise the test by Vito Ricci in FITTING DISTRIBUTIONS WITH R   > ad.test(hist_hume_beec[,1],hist_hume_beec[,2]) Error: could not find function "ad.test"

Dear R gurus, I have a function "goftests" that receives the following arguments: * a vector "x" of data values; * a distribution name "dist"; * the dots list ("...") containing a list a parameters to pass to CDF function; and calls several goodness-of-fit tests on the given data values against the given distribution. That is: ##### BEGIN CODE SNIP #####

Dear R-users, I want to perform a One-Sample parametric bootstrapped Kolmogorov-Smirnov GoF test (note package "Matching" provides "ks.boot" which is a 2-sample non-parametric bootstrapped K-S version). So I wrote this code: ---[R Code] --- ks.test.bootnp <- function( x, dist, ..., alternative=c("two.sided", "less", "greater"), B = 1000 ) {

Hi, I would like to compute a goodness-of-fit statistic for one data series against a t-distribution, and obtain the quantiles of the distribution of the statistic with given degrees of freedom. I wonder if this is implemented in a package. I know that the critical values have to be computed for every distribution, and this requires numerical integration typically. I would prefer if I could get

Hi, I'm trying to run an anderson-darling test for normality on a given variable 'Y': ad.test(Y) I think I need the 'nortest' package, but since it does not appear in any of the Ubuntu repositories for 2.10.1, I am wondering if it goes by the name of something else now? Thanks -- View this message in context:

? Hallo, which function can i use to do (baseline) logistic regression + goodness of fit tests? so far i found: # logistic on binary data lrm combined with resid(model,'gof') # logistic on binary data glm with no gof-test # baseline logit on binary data

Hi Folks, Hopefully this question has enough R and not too much stats to be appropriate for this list. Based on,* Hosmer et al. 2013. Logistic regression for matched case-control studies. Applied Logistic Regression *(eqtn. 7.8)*, *I am assessing GOF of conditional (or matched) logistic regression models with the *standardized Pearson residuals*. The authors define ?large? as delta chi-squared

All, This may be a bit off topic so feel free to flame me ... my defence is that I am using R. I have data with case counts per family. I arrange the data in a simple table of frequency classes (e.g. how many families with 0 cases, how many with 1 case, &c.) and then GoF to Poisson and negative binomial. I treat each family as a natural sampling unit but families are of different size. I can

Hi all, I need to test gof of 3-dimensional t copula for my trivariate observed data set. So I used the command t.cop <- tCopula(c(0.785,0.283,0.613),dim=3,dispstr="un",df=6,df.fixed = TRUE) where c(0.785,0.283,0.613) is the correlation pattern of my data with 0.785 pearson correlation between variable 1-2, 0.283 correlation between 1-3 and 0.613 is the correlation between variable

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

dear experts, i am a novice and have been trying to use the anderson-darling test on a simple text file with one column of data. i have followed the example in the manual to read from a file into a vector(mm). i am able to see the summary stats with >summary(mm) however, when i try to use the ad.test package, it keeps coming up with the following error messages, > ad.test(mm) Error in

Hola, LLevo buscando desde hace tiempo como hacer el Goodness of fit test en R. Es decir, me explico, intento hacer una cosa parecida que se hace en Minitab, por ejemplo, yo tengo un conjunto de datos, y lo que quiero es sabes que tipo de distibución es, en minitab se hace un histograma para ver si se ajusta bien o no a la campana de Gauss, luego vemos si aproximar la distribución de la muestra

I don't know from the nortest package, but it should ***always*** be the case that you test hypotheses H_0: The data have a normal distribution. vs. H_a: The data do not have a normal distribution. So if you get a p-value < 0.05 you can say that ***there is evidence*** (at the 0.05 significance level) that the data are not from a normal distribution. If the nortest package does

Hi, I'm a newbie and i am unable to use lillie.test in nortest I have a message: "Couldn't find function "lillie.test" I am under windows2000 with R1.8.1 nortest is listed with .packages(TRUE) How to do to use lillie.test function? Laurent Houdusse Analyste Programmeur

Hi all, i have 2 cumulative (F(x)) distribution function that not defined in R. i want to make anderson darling goodness of fit test to first function (function 1) to check if it behaves as the other distributed function(function 2) how can i insert my 2 unknown by R function to ad.test()? Regards, Irit -- View this message in context:

Dear R-experts, I have some questions about boxplots with lattice. My data is similar as in the example below, I have two factors (Goodness of Fit and Algorithms) and data values but in each panels the scales are quite different, therefore the normal boxplots produced by set.seed(1) GOF <- factor(rep(c("GOF1","GOF2","GOF3"),each=40)) Alg <-

Hi R-Developers, I had a question about the random number generator used in the R StandAlone Math Library. The stand-alone library depends on the unif_rand() function for most simulated values, and this function is provided in the sunif.c file in the relevant directory. At present, this program implements the "Marsaglia-Multicarry" algorithm, which is described throughout the R

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Dear Statistics-Experts, Assume you have given a new and untested pseudo-random number generator (prng) and you want to test if it "works". The distribution function (cdf) from which the prng is supposed to sample is known. Further, you are given some finite (large) sample from the prng. If the sample is one-dimensional, we can apply the cdf to it and test the sample for being