[R-sig-eco] Identification of outliers using NMDS goodness of fit statistic
R-sig-ecology peeps, I have been helping a friend run an NMDS and we started talking about the the goodness of fit statistic (GOF) to identify outliers. It got me thinking about this metric a bit more. I have always used exploratory analysis procedures, like normal qqplot, histograms, box plots, ect., to identify outlier in my data. So I have two questions regarding GOF: 1) can the NMDS goodness of fit statistic be used to identify outliers? And 2) is there a recommended threshold of the NMDS goodness of fit statistic that would identify outliers? Thanks, - MVS = Matthew Van Scoyoc Graduate Research Assistant, Ecology Wildland Resources Department & Ecology Center Quinney College of Natural Resources Utah State University Logan, UT = Think SNOW! -- View this message in context: http://r-sig-ecology.471788.n2.nabble.com/Identification-of-outliers-using-NMDS-goodness-of-fit-statistic-tp7578632.html Sent from the r-sig-ecology mailing list archive at Nabble.com. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Bayesian analysis with MCMC
Dear all I have a model with four paramters, I want to estimate the parameter uncertainty, so Bayesian analysis with MCMC method is applied. But every sigle mcmc chain seems give quite different parameter marginal distributions. In order to get the true parameter marginal distributions, I do like this: (1) I take 100 MCMC chain, and each chain has 1 iterations. (2) caculate the different parameter marginal distributions according to the frequence of paramter in step 1 sampling. The result seems reasonable. but is it right? Looking forward for your reply, Thanks in advance Han Ming [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] joint distribution
An explicit formula for a posterior distribution is not something to expect from an MCMC procedure. But the next best thing to an explicit formula for a posterior distribution is a zillion samples from that distribution (which is what you have). What you can do is display smooth representations of the individual marginal distributions (and bivariate marginal distributions) using the density function or functions in the KernSmooth and ks packages. Jim Jim Baldwin Station Statistician Pacific Southwest Research Station USDA Forest Service -Original Message- From: r-sig-ecology-boun...@r-project.org [mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of ? Sent: Friday, January 17, 2014 5:57 AM To: r-sig-ecology Subject: [R-sig-eco] joint distribution Dear All I get samples from MCMC sampling to a posterior distribution. there is four variables, how could I get a joint distribution for this four variable from the samples? Thanks in advance~! Han Ming [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology This electronic message contains information generated by the USDA solely for the intended recipients. Any unauthorized interception of this message or the use or disclosure of the information it contains may violate the law and subject the violator to civil or criminal penalties. If you believe you have received this message in error, please notify the sender and delete the email immediately. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] joint distribution
Dear All I get samples from MCMC sampling to a posterior distribution. there is four variables, how could I get a joint distribution for this four variable from the samples? Thanks in advance~! Han Ming [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
