similar to: probabilty plot

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

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, 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

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

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]]

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, 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

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,

Hello. I'm a new user of R, and I have a question regarding the use of aov and lm-functions. I'm doing a fractional factorial experiment at our production site, and I need to familiarize myself with the analysis before I conduct the experiment. I've been working my way through the examples provided at http://www.itl.nist.gov/div898/handbook/pri/section4/pri472.htm

R plotting experts: I have a bivariate dataset composed of 300 (x,y) continuous datapoints. 297 of these points are located within the y range of [0,10], while 2 are located at 20 and one at 55. No coding errors, real outliers. When plotting these data with a scatterplot, I obviously have a problem. If I plot the full dataset with ylim = c(0,55), then I cannot see the structure in the data in

Hello, I am trying to test the precision of R on datasets from The Statistical Reference Datasets Project http://www.itl.nist.gov/div898/strd/index.html and I don't manage to understand how R is storing its results. For example, I calculate a mean on the michelso dataset (100 values) and find: > m=mean(michel) > m V1 299.8524 > print(m,digits=15) V1 299.8524

The function wtd.var(x,w) in Hmisc calculates the weighted variance of x where w are the weights. It appears to me that wtd.var(x,w) = var(x) if all of the weights are equal, but this does not appear to be the case. Can someone point out to me where I am going wrong here? Thanks. Tom La Bone [[alternative HTML version deleted]]

Hi, I'm interested in experimental design and data analysis on mixtures, like cake recipes where the sum of the components is fixed; e.g. <http://www.itl.nist.gov/div898/handbook/pri/section5/pri54.htm>. I can't believe that R doesn't have facilities to design and analyse such experiments, but I haven't been able to find them (I have looked quite hard!). Can anyone point

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hello all - As I progress in R I am trying to automate functions I would have normally farmed out to Excel, SPSS or Statistica. Single factor anova is one of them. For example, a dataset from NIST StRD (http://www.itl.nist.gov/div898/strd/anova/AtmWtAg.html) has two groups: 1 2 107.8681568 107.8681079 107.8681465 107.8681344

Dear Professor I have read your questions in the website on Doubly non-central F-distribution. I am looking for source code for evaluating this function now. I have matlab code but it's only accurate up to the 4th decimal point. Dataplot is more accurate, but it is not user friendly as you only can evaluate one function at a time. So I would like to know do you have found any R code for

Dear R users, I would appreciate for some advise how to generate a Tukey Mean-Difference Plot with the tmd function part of the lattice library. I have two test results (log transformed) which showing a correlation on a scatterplot. However the correlation line is parallel displaced depending on a clinical condition. I thought to a Tukey Mean Difference Plot would show me the difference

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