similar to: rate instead of scale in ?ks.test

When fitting a logistic regression model using weights I get the following warning > data.model.w <- glm(ABN ~ TR, family=binomial(logit), weights=WEIGHT) Warning message: non-integer #successes in a binomial glm! in: eval(expr, envir, enclos) Details follow *** I have a binary dependent variable of abnormality ABN = T, F, T, T, F, F, F... and a continous predictor TR = 1.962752

I'd like to update MASS from version 7.2-11 to version 7-2.19. I am running R 2.0.1 on ubuntu, that installs MASS with the package r-cran-vr. I have tried doing update.packages(), and I get a list of packages that I could update, but none is called MASS. I have tried the following too, right after launching emacs, but to no avail. <QUOTE> > update.packages("MASS") trying

Frank E Harrell Jr wrote: > Eric Rescorla <ekr at rtfm.com> wrote: > >> (2) I'd like to compute goodness-of-fit statistics for my fit >> (Hosmer-Lemeshow, Pearson, etc.). I didn't see a package that >> did this. Have I missed one? > > Hosmer-Lemeshow has low power and relies on arbitrary binning of > predicted probabilities. The Hosmer-Le Cessie

Dear R-List members, I want to check if a set of measurements follows better a gamma or a lognormal distribution (see data below). Using shapiro.test I can test for normality (shapiro.test(log (Lt)). To test for gamma (and normal) distribution I would use ks.test but I need to specify its shape and scale. How should I calculate these values in R? I tried > Lt.fit <- glm(Lt ~ 1,

(Sorry for obvious mistakes, as I am quite a newby with no Statistics background). My question is going to be what is the gain of logistic regression over odds ratios when none of the input variables is continuous. My experiment: Outcome: ordinal scale, ``quality'' (QUA=1,2,3) Predictors: ``segment'' (SEG) and ``stress'' (STR). SEG is nominal scale with 24

Hi, I have two sets of data, an observed data and generated data. The generated data is obtained from the model where the parameters is estimated from the observed data. So I'm not sure which to use either one-sample test ks.test(x+2, "pgamma", 3, 2) # two-sided, exact or two-sample test ks.test(x, x2, alternative="l") If I use the one-sample test I need to

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 everybody, while performing ks.test for a standard exponential distribution on samples of dimension 2500, generated everytime as new, i had this strange behaviour: >data<-rexp(2500,0.4) >ks.test(data,"pexp",0.4) One-sample Kolmogorov-Smirnov test data: data D = 0.0147, p-value = 0.6549 alternative hypothesis: two.sided >data<-rexp(2500,0.4)

In message <Pine.GSO.4.31.0107010731110.7616-100000@auk.stats>, Prof Brian D Ripley <ripley@stats.ox.ac.uk> writes > >You do realize that the Kolmogorov tests (and the Kolmogorov-Smirnov >extension) assume continuous distributions, so the distribution theory >is not valid in this case? > >S-PLUS does stop you doing this: > >> ks.gof(o,

Full_Name: Andrew Grant McDowell Version: R 1.1.1 (but source in 1.3.0 looks fishy as well) OS: Windows 2K Professional (Consumer) Submission from: (NULL) (194.222.243.209) In article <xeQ_6.1949$xd.353840@typhoon.snet.net>, johnt@tman.dnsalias.com writes >Can someone help? In R, I am generating a vector of 1000 samples from >Bin (1000, 0.25). I then do a Kolmogorov Smirnov test

On Sun, 1 Jul 2001 mcdowella@mcdowella.demon.co.uk wrote: > Full_Name: Andrew Grant McDowell > Version: R 1.1.1 (but source in 1.3.0 looks fishy as well) > OS: Windows 2K Professional (Consumer) > Submission from: (NULL) (194.222.243.209) Please upgrade: we've found a number of Win2k bugs and worked around them since then, let alone teh bug fixes and improvements in R .... >

Hello, While doing test of normality under R and SAS, in order to prove the efficiency of R to my company, I notice that Anderson Darling, Cramer Van Mises and Shapiro-Wilk tests results are quite the same under the two environnements, but the Kolmogorov-smirnov p-value really is different. Here is what I do: > ks.test(w,pnorm,mean(w),sd(w)) One-sample Kolmogorov-Smirnov test data: w D

Hello, I am new on Ubuntu. I would like to use R, but I tried Kate and Scite. The first one keeps trying to use KDE applications,while the other does not understand the language. I have searched for another editor for hours, in vain. Which editor should work with Ubuntu? I am looking forward to your answer, thanks, Camille

Hi, I would like to count the columns of a data.frame. I know how to count the rows, but not the columns. Can someone tell me how to do it? My best regards, Giacomo Moro --------------------------------- [[alternative HTML version deleted]]

> x <- runif(100) > y <- runif(100) > ks.test(x,y) Two-sample Kolmogorov-Smirnov test data: x and y D = 0.11, p-value = 0.5806 alternative hypothesis: two-sided ok I expected that, but: > ks.test(runif(100), "runif") One-sample Kolmogorov-Smirnov test data: runif(100) D = 0.9106, p-value < 2.2e-16 alternative hypothesis: two-sided How

Buenas Tengo un Data table de la siguiente manera: datos<-data.table(grupo=rep(c("a","b"),5),x=c(1:10),y=rnorm(10,2,1)) Lo que quiero es añadir una fila por cada grupo y en esa nueva fila, al valor de la x ponerle el valor anterior de la y Lo que hago es añadir una nueva fila por grupo, con: datos[,.SD[1:(.N+1)],by=grupo] Y para añadir el valor anterior uso la función

Hi there! I have two little different data. One is a computer test on people, the other is a paper and pencil test. two boxplots show me that the data is almost the same. So now I'd like to know if I could handle all data as one, by testing with ks.test: ==== > ks.test(el$angststoer, fl$angststoer) Two-sample Kolmogorov-Smirnov test data: el$angststoer and fl$angststoer D =

The note to 1004 says "fixed for 1.3.1" Uh. No. It ain't. The problem was more serious than guessed as even the simplest testing would show. For example, Example 5.4 in Hollander and Wolfe (Nonparametric Statistical, Methods, 2nd ed., Wiley, 1999, pp. 180-181) R Version 1.3.1 (SuSE Linux 7.1) > X <-

Dear R users, I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances. My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of

Hi! how can I do the Kolmogorov Smirnov test for discrepancy between the estimated and empirical tails? Regards Anuradha [[alternative HTML version deleted]]