similar to: Goodness of fit tests

Greetings, We wanted to announce v. 1.4.0 of the R package 'KScorrect', which carries out the Lilliefors correction to the Kolmogorov-Smirnoff (K-S) test for use in (one-sample) goodness-of-fit tests. Aside from several minor changes, the biggest change is that the Monte Carlo algorithm now supports parallel implementation, using the platform-independent 'doParallel' and

Greetings, We wanted to announce v. 1.4.0 of the R package 'KScorrect', which carries out the Lilliefors correction to the Kolmogorov-Smirnoff (K-S) test for use in (one-sample) goodness-of-fit tests. Aside from several minor changes, the biggest change is that the Monte Carlo algorithm now supports parallel implementation, using the platform-independent 'doParallel' and

Hi, I'm using the power.law.fit function from the igraph package to fit a power law distribution to some data. This function returns the power law exponent as it's only result. I would like to have some sort of goodness-of-fit and/or error estimate of the exponent returned. This paper: http://www.edpsciences.org/articles/epjb/pdf/2004/18/b04111.pdf suggests using the

Hi, Does anyone know an instruction to perform a test (or to construct a confidence interval) for the variance of a normal population (distribution). The test statistic is (n-1)*S´^2/sigma^2 and has chi square (n-1) distribution. Thank you, Alexandra [[alternative HTML version deleted]]

> -----Original Message----- > From: Peter Dalgaard > Sent: Thursday, May 06, 2004 4:32 PM > To: Janete Borges > Cc: r-help at stat.math.ethz.ch > Subject: Re: [R] help on ks.test > > "Janete Borges" <janeteborges at gmx.net> writes: > > > Dear All > > > > I need to test the goodness-of-fit of a (Negative) Exponential >

Dear R-Users, How can I use chisq.test() as a goodness of fit test? Reading man-page I?ve some doubts that kind of test is available with this statement. Am I wrong? X2=sum((O-E)^2)/E) O=empirical frequencies E=expected freq. calculated with the model (such as normal distribution) See: http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm for X2 used as a goodness of fit test. Any

I'm trying to do a Hosmer-Lemeshow 'goodness of fit' test on my logistic regression model. I found some code here: http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html The R code is above is a little complicated for me but I'm having trouble with my answer: Hosmer-Lemeshow: p=0.6163585 le Cessie and Houwelingen test (Design library): p=0.2843620

Hello, I've read the other posts with regard to "chisq.test" and "goodness of fit" and am still missing something. 1. I create a simple vector of randomly generated lognormal values with mean=0 and sd=1; >d1 <- rlnorm(100,meanlog=0,sdlog=1); 2. I also create a vector of probabilities that are expected for a lognormal distribution. I suspect this is the culprit. >pr

Dear R-users, is there a function for the Chi-square Goodness of Fit Test in R (compare function chisq.gof in S/S-PLUS). Thanks for help. Matthias Seitzinger -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

I am running chi-square test of goodness of fit, the observation data was well groupped to observation freq. I assume the data obey the normal distribution N(? ?^2), while ? and ?^2 are unknown. So I use their estimation. Consequently, I calculated out the probabilities and input in vector p >x<-c(6,13,14,27,25,19,10,6) #observation freq>p<-c(0.0505, 0.0874, 0.1533, 0.2088, 0.2088,

Hello, I have a question concerning the R-function chisq.test. For example, I have some count data which can be categorized as follows class1: 15 observations class2: 0 observations class3: 3 observations class4: 4 observations I would like to test the hypothesis whether the population probabilities are all equal (=> Test for discrete uniform distribution) If you have a small sample size

R Help on 'chisq.test' states that "if 'simulate.p.value' is 'TRUE', the p-value is computed by Monte Carlo simulation with 'B' replicates. In the contingency table case this is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are positive... In the

I want to calculate chi square test of goodness of fit to test, Sample coming from Poisson distribution. please copy this script in R & run the script The R script is as follows ########################## start ######################################### No_of_Frouds<- c(4,1,6,9,9,10,2,4,8,2,3,0,1,2,3,1,3,4,5,4,4,4,9,5,4,3,11,8,12,3,10,0,7) N <- length(No_of_Frouds) # Estimation of

Dear r list,   I am using glm.nb in the MASS package to fit negative binomial models to data on manta ray abundance, and AICctab in the bbmle package to compare model IC.  However, I need to test for the goodness of fit of the full model, and have not been able to find a Pearson's Chi Squared statistic in any of the output.  Am I missing it somewhere?  Is there a way to run the test using

I am trying to test goodness of fit for my legalistic regression using several options as shown below.  Hosmer-Lemeshow test (whose function I borrowed from a previous post), Hosmer–le Cessie omnibus lack of fit test (also borrowed from a previous post), Pearson chi-square test, and deviance test.  All the tests, except the deviance tests, produced p-values well above 0.05.  Would anyone please

Dear R users, I am a master student in Mathematics and I am writing my thesis in statistics. I need to use R and unfortunately I do not have any experience with a computer program. Could you please help me about chi squared goodness of fit test with R? In R-help website I saw a message about how to do that but I do not know how to cut the data into bins and calculate the expected numbers in each

Hi, is there any method for goodness of fit testing of an (as general as possible) univariate distribution with parameters estimated, for normal, exponential, gamma distributions, say (e.g. the corrected p-values for the Kolmogorov-Smirnov or Chi-squared with corresponding ML estimation method)? It seems that neither ks.test nor chisq.test handle estimated parameters. I am aware of function

I simulate a uniform data and then transformed into Burr(1,3,1) data, which is of pdf: f(x)=[3*(x^2)] / [(1+x^3)^2], x>0 How can I perform a goodness-of-fit test (k-s, anderson-darling,chisq,cramer-von mises,...) on it (should highly accept) to get test-statistics & p-values? Thanks! Sincerely, Shelton Jin -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

TOPIC My question regards the philosophy behind how R implements corrections to chi-square statistical tests. At least in recent versions (I'm using 2.13.1 (2011-07-08) on OSX 10.6.8.), the chisq.test function applies the Yates continuity correction for 2 by 2 contingency tables. But when used as a goodness of fit test (GoF, aka likelihood ratio test), chisq.test does not appear to implement

Dear R-help,   Chi Square Test for Goodness of Fit     Problem Faced :   I have got a discrete data as given below (R script)   No_of_Frauds <-c 1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,2,1,2,2,2,1,1,2,1,1,1,1,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,1,2,1,1,1,1,1,1,1,3,2,1,1,1,2,1,1,2,1,1,1,1,1,2,1,3,1,2,1,2,14,2,1,1,38,3,3,2,44,1,4,1,4,1,2,2,1,3)   I am trying to fit