similar to: chisq.test() as a goodness of fit test

Hello all, Would somebody be kind enough to show me how to do a KS test in R for a lognormal distribution with ESTIMATED parameters. The R function ks.test()says "the parameters specified must be prespecified and not estimated from the data" Is there a way to correct this when one uses estimated data? Regards, Kwabena. -------------------------------------------- Kwabena Adusei-Poku

Hi, from what you're writing: "The logaritmic transformation "shapiro.test(log10(y))" says: W=0.9773, p-value= 2.512e-05." it seems the log-values are not distributed normally and so original data are not distributed like a log-normal: the p-value is extremally small! Other tests for normality are available in package: nortest compare the log-transformation of your ecdf

Hi, pchisq -> distribution function dchisq -> density function pval is the area under the curve, to calculte it you use distribution function which is the integral of density function. See: http://www.itl.nist.gov/div898/handbook/eda/section3/eda362.htm http://mathworld.wolfram.com/DistributionFunction.html f(x) density function F(x) distribution function =Pr(X<x)= integral(f(x))

Hi all, I am a freshman of R,but I am interested in it! Those days,I am learning pages on NIST,with url http://www.itl.nist.gov/div898/handbook/eda/section3/probplot.htm, I am meeting a problem about probability plot and I don't know how to plot a data set with R. Could somebody tell me the answer,and a example is the best! I will look forward to your answer. Thank you very much.

Hola a todos: Estoy intentado realizar un modelo senoidal de unos datos de radiación solar con el fin de afrontar el relleno de la serie y aplicar la prueba de Thom para verificar su homogeneidad [0]. De momento me encuentro con los siguientes problemas: 1- ¿Existe la prueba de Thom en R? ¿O debo crearme mi propia función? 2- Para la realización del modelo senoidal estoy siguiendo los pasos

Hi, Can anyone please provide the formula used to compute ACF(nlme). I believe the one that is used in R is of the type mentioned on the website. Please correct me if I am wrong. The normalization of the numerator (Ch) has been done by 'N' where as I would like to do it by 'N-k'. Is there anyway in the present implementation of ACF to normalize it by 'N-k', where

Does anyone have code to plot bihistograms in R? See http://www.itl.nist.gov/div898/handbook/eda/section3/bihistog.htm for a description of a bihistogram. -- Hal Varian voice: 510-643-4757 SIMS, 102 South Hall fax: 510-642-5814 University of California hal at sims.berkeley.edu Berkeley, CA 94720-4600 http://www.sims.berkeley.edu/~hal

I'd like to generate some plots like you'd see on the old "normal probability graph paper", like the first plot in: <http://www.itl.nist.gov/div898/handbook/eda/section3/normprpl.htm> except the horizontal scale would have 1%, 5%, 25%, 50%, 75%, 95%, 99%, or similar quantiles, with associated tick/grid lines. [still hunting around for a good example...] something like

hi! I would like to ask help how to generate numbers from g-and-h distribution. This distribution is like normal distribution but span more of the kurtosis and skewness plane. Has R any package on how to generate them? Any help will be greatly appreciated. Thank you so much! Form, Filame Uyaco --------------------------------- [[alternative HTML version deleted]]

Hi, this is both a statistical and a R question... what would the best way / test to detect an outlier value among a series of 10 to 30 values ? for instance if we have the following dataset: 10,11,12,15,20,22,25,30,500 I d like to have a way to identify the last data as an outlier (only one direction). One way would be to calculate abs(mean - median) and if elevated (to what extent ?) delete the

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

Dear R-useRs, I've written a contribute (in Italian language) concering fitting distribution with R. I believe it could be usefull for someones. It's available on CRAN web-site: http://cran.r-project.org/doc/contrib/Ricci-distribuzioni.pdf Here's the abstract: This paper deals with distribution fitting using R environment for statistical computing. It treats briefly some

Hi, I found this document, but it concerns S+. If it could interest you'll see: http://faculty.washington.edu/ezivot/book/QuanCopula.pdf Cordially Vito You wrote: Dear R: Is there a function or a reference to simulate Gumbel copulas, please? Thanks in advance! Sincerely, Erin Hodgess mailto: hodgess at gator.uhd.edu R version 2.0.1 windows ===== Diventare costruttori di soluzioni

Hi, you can address to a single ts in a multivariate ts object by namets[,index]. See this example: > dati X Y 1 100 200 2 150 210 3 180 220 4 200 230 5 220 250 > serie<-ts(dati,start=1999) > serie Time Series: Start = 1999 End = 2003 Frequency = 1 X Y 1999 100 200 2000 150 210 2001 180 220 2002 200 230 2003 220 250 > serie[,1] ## first ts Time Series: Start =

NIST maintains a repository of Statistical Reference Datasets at http://www.itl.nist.gov/div898/strd/. I have been working through the datasets to compare R's results to their references with the hope that if all works well, this could become a validation package. All the linear regression datasets give results with some degree of accuracy except one. The NIST model includes 11 parameters,

Dear Users! I encountered with some problem in data reading while I challenged R (and me too) in a validation point of view. In this issue, I tried to utilize some reference datasets ( http://www.itl.nist.gov/div898/strd/index.html). And the result departed a bit from my expectations. This dataset dedicated to challenge cancellation and accumulation errors (case SmLs07), that's why this

I have run a t.test in R, and received these results: Two Sample t-test data: rsa and umple t = 0.9819, df = 10, p-value = 0.3493 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -76.1541 196.1541 sample estimates: mean of x mean of y 508.3333 448.3333 Can someone give me a detailed interpretation of the above results? Specifically,

Hi, I've a list containing parameters (intercepts & coefficients) of 12 regressions fitted > coeff [[1]] (Intercept) anno -427017.1740 217.0588 [[2]] (Intercept) anno -39625.82146 21.78025 ..... [[12]] (Intercept) anno 257605.0343 -129.7646 I want create a data frame with two columns (intercept and anno)using data in these list. Any help

Hi, see ?cor in base package to get correlation matrix for your data. Maybe it could be usefull getting principal components (give a look to: ? princomp (base)) to reduce the number of variables. Hoping I helped you. Best regards, Vito You wrote: Hi all: I have a question on how to go about creating a correlation matrix. I have a huge amount of data....21 variables for 3471 times. I want

Dear all, in last weeks you discussed about R vs SAS. I want to ask your opinion about a comparison between R and SPSS. I don't know this software, but some weeks ago I went to a presentation of this product. I found it really user-friendly with GUI (even if I'd prefer command line) and very usefull and simple to use in creation and managing tables, OLAP tecniques, pivot table. What you