similar to: Comparing skewness

I have a question about the D'Agostino skewness test and the Anscombe-Glynn kurtosis test. agostino.test(x, alternative = c("two.sided", "less", "greater")) anscombe.test(x, alternative = c("two.sided", "less", "greater")) The option "alternative" in those two functions seems to be the null hypothesis. In the output, the

Greetings, I have generated several ROC curves and would like to compare the AUCs. The data are cross sectional and the outcomes are binary. I am testing which of several models provide the best discrimination. Would it be most appropriate to report AUC with 95% CI's? I have been looking in to the "net reclassification improvement" (see below for reference) but thus far I can only

Hi, Does anyone know if the D'agostino test is available with R ? Alex

Hola Carlos. Muchas gracias. No es exactamente lo que estoy buscando (sería genial ver que alguien tiene un paquete con la prueba de Nam-D'Agostino) pero puedo aprovechar algo de código. Encontré alguna referencia al test de Gronnesby&Borgan en el paquete stcoxgof de Stata, pero estoy torpe para encontrar algo hecho en R (y me extraña que no haya nada) Un saludo, Miguel. De: Carlos

Hi, I am trying to calculate net reclassification improvement (NRI) and Inegrated Discrimination Improvement (IDI) for a survival dataset to compare 2 risk models. It seems that the improveProb() in Hmisc package does this only for binary outcome, while rcorrp.cens() does take survival object, but doesn't output NRI or IDI. Can anyone suggest any other packages that can calculate NRI and IDI

Hola. Una duda.... Alguien sabe si hay algo hecho en R para comprobar si un modelo de supervivencia está bien calibrado? (usando las pruebas de Nam-D'Agostino o de Gronnesby & Borgan). Thnks. Un Saludo, _____________________________ Miguel Ángel Rodríguez Muíños Dirección Xeral de Innovación e Xestión da Saúde Pública Consellería de Sanidade Xunta de Galicia http://dxsp.sergas.es

Hello, I am interested in looking at the dataset used by Stock and Watson in their "Macroeconomic Forecasting Using Diffusion Indexes (J. of Business and Econ. Statistics, April 2002, pp158-161) or the set used by D'Agostino and Giannone "Comparing Alternative Predictors [...]"(October 2006) in R. Does anyone know if the R-code to retrieve these series from FRED (as

Dear useRs, I was searching CRAN for implementation of kurtosis and skewness tests, and found that there is some kind of lack on it. So, I have written three functions: 1. Anscombe-Glynn test for kurtosis 2. Bonett-Seier test based on Geary's kurtosis (which is not widely known, but I was inspired by original paper describing it, found coincidentally in Elsevier database) 3.

Dear Fellows, I would like to know how to uncompress a gz file at the R console. I could not find out any help from the R-help archive. Thanks for your great help. Best Regards, Wonsang You ----- -- Wonsang You Special Lab Non-Invasive Brain Imaging Leibniz Institute for Neurobiology http://www.ifn-magdeburg.de -- View this message in context:

Dear fellows, I am a novice in R. I would like to combine a matrix and a vector. Assume that we have the matrix a and the vector b with same length of column. a<-matrix(seq(1:10),nrow=2,ncol=5,byrow=TRUE) a= 1 2 3 4 5 6 7 8 9 10 b<-t(c(11,12,13,14,15)) b= 11 12 13 14 15 Then, I want to combine a and b as follows. c= 1 2 3 4 5 6 7 8 9

Hi, Is there an implementation of the D'agostino K2 omnibus test in an R package. I've found the D test in the fBasics package, but this doesn't give the same answer as my algorithm. I've already coded a version of the K2 and need to check the results. I have a set of spectra with gaussian distributed noise and I want a statistical test to separate those with no signal (so the

>From http://bugs.r-project.org/cgi-bin/R: If you are not sure whether you have observed a bug or not, it is a good idea to ask on the mailing list R-Help by sending an e-mail to r-help at stat.math.ethz.ch rather than submitting a bug report. I'm wondering whether to submit a bug report on this: ============================================================== >

A lo mejor si escribes a los que mantienen la Task-View de R (Survival Analysius) te pueden dar la clave...: CRAN Task View: Survival Analysis*Maintainer:*Arthur Allignol and Aurelien Latouche*Contact:*arthur.allignol at fdm.uni-freiburg.de*Version:*2013-08-09 Y en Stata, como referencia adicional (que puede conducir a algo equivalente en R): http://www.jstatsoft.org/v53/i12/paper Saludos,

Dear all I am trying to compare the performances of several methods using the AUC0.1 and not the whole AUC. (meaning I wanted to compare to AUC's whose x axis only goes to 0.1 not 1) I came to know about the Mcneil Hanley test from Bernardo Rangel Tura and I referred to the original paper for the calculation of "r" which is an argument of the function cROC. I can only find the

Dear Colleagues, I would like to provide a CITATION file for my package nat.nblast [1]. I have the correct citation in BibTeX format [2]. How can I convert this BibTeX to the format needed by R for a package CITATION file (I have a lot of other packages needing citations ...). I think what I need is the opposite of RefManageR::toBiblatex [3]. This seems like it should be a common need, so I

Hi, I would like to compare differences in AUC from 2 different models, glm and gam for predicting presence / absence. I know that in theory the model with a higher AUC is better, but what I am interested in is if statistically the increase in AUC from the glm model to the gam model is significant. I also read quite extensive discussions on the list about ROC and AUC but I still didn't find

Dear R user fellows, I would like to ask you about the package 'rgenoud' which is a genetic optimization tool. I ran the function 'genoud' with two variables to be minimized by the following command. result<-genoud(fn,nvars=2,starting.values=c(0.5,0), pop.size=1000, max.generations=10, wait.generations=3) Then, I had the following error message. Error in

Hi, R-Helpers, I would like to ask about multiple graphs in one figure. I tried to execute the following codes. xlim <- c(1,100) ylim <- c(1,4) plot(NA, xlim=xlim, ylim=ylim) > x <- c(1:100) for(j in seq(1,10,by=1)) { y <- j*x^2+log(j) lines(x, y) } In the above codes, I had to arbitrarily set up the coordinate range of the figure in advance before

Dear R Helpers, I have searched for any R package or code for simulating multivariate fractional Brownian motion (mFBM) or multivariate fractional Gaussian noise (mFGN) when a covariance matrix are given. Unfortunately, I could not find such a package or code. Can you suggest any solution for multivariate FBM and FGN simulation? Thank you for your help. Best Regards, Ryan ----- Wonsang You

Dear R-Helpers, I want to try the 2D (two-dimensional) convolution in R. For example, let us we have the following kernel and data. kernel <- (1,2,3,2,1) data <- array(1:100, dim=c(10,10)) I know the function 'convolve' only for one-dimensional convolution, but it is just for a 1D sequence. Is there any function for 2D convolution? For theory, please refer to the following link: