search for: cressi

Has anyone implemented a t-test with the effective sample size correction proposed by Dale and Fortin, Ecoscience 9(2):162-167, 2002, using a discussion by Cressie, 1993, page 15? thanks, Denis

...of my data appears to be linearly correlated for the first 5000 meters and not correlated beyond that. I have been having problems using krige.conv() to get a decent kriged map using the linear model. The code I am using from my data is as follows: >modeltest=variofit(variotest, weights=?cressie?, cov.model=?linear?, ini.cov.pars=c(80,1)) The output parameters are tausq = 9.855, sigmasq = 0.0087, phi=1.0 >krig=krige.conv(data, krige=krige.control(type.krig=?ok?, obj.model=modeltest), locations=pred.grid) At this point, krig$predict values have little to no variability (1.897 +/-...

You are talking about random point patterns, since the glow-worms appear as ``stars'' (= points). See the package ``spatial'' (which comes with R) and try simulating a pattern using Strauss(). Or install the package ``spatstat'' from CRAN --- in this package there is a variety of ways to simulate ``regular'' random point patterns --- rMaternI, rMaternII, rSSI,

...,2] <- lon[ll] # x-axis ppt2[,3] <- ptemp[ll] # ppt pptd <- as.geodata(ppt2) bin1 <- variog(pptd) # plot(bin1) # fig1 bin2 <- variog(pptd,estimator.type="modulus")# plot(bin2) # fig2 ini1 <- max(bin1$v) ols <- variofit(bin1, fix.nugget = F,weights="cressie",ini.cov.pars=c(ini1,4)) kc <- krige.conv(pptd, loc=pgrid,krige=krige.control(type.krige="OK",trend.d="2nd",trend.l="2nd",cov.pars=ols[2]$cov.pars)) pvalxx <- which(kc$predict < 0) kc$predict[pvalxx] <- 0 pptpred <- kc$predict }else{...

...>ok<-krige.conv(cadiz.geo,data=amostragem.cadiz$dia1.std,locations=as.matrix(cadiz.polygrid[,c(1,2)]),krige=krige.control.ok) krige.conv: model with constant mean krige.conv: Kriging performed using global neighbourhood where my.variogram.model is gaussian fitted through variofit with "cressie" weigths. I'm getting the same predicted values for both models, 12% of them being negative. Is it because simple and ordinary kriging predictions are not including the overall mean? If this is the case what mean should I use? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-....

...diz.geo,data=amostragem.cadiz$dia1.std,locations=as.matrix(cadiz.polygrid[,c(1,2)]),krige=krige.control.ok) > krige.conv: model with constant mean > krige.conv: Kriging performed using global neighbourhood > > where my.variogram.model is gaussian fitted through variofit with > "cressie" weigths. > > I'm getting the same predicted values for both models, 12% of them > being negative. Is it because simple and ordinary kriging predictions > are not including the overall mean? If this is the case what mean should > I use? Thank you for your help Juan -.-.-...

_____ De : Pousset [mailto:maud.pousset@noos.fr] Envoyé : mardi 4 juillet 2006 18:38 À : 'r-help@stat.math.ethz.ch' Objet : Latent Class Analysis Hello everybody, I am working on latent class analysis and have already used the ‘R’ function « lca » (in the e1071 package). I ‘ve got interesting results but I can’t simply find out the methodology used by this routine : 1) What

Hi, I am new in R and trying to implement an algorithm which makes ordinary kriging by using gstat library and the predict method. I use the predict method as following: First, I create an object g: g <- gstat(id="tec", formula=TEC ~ 1, data=data) ## Create gstat object called g And then I use this object in the predict. p <- predict.gstat(g, model=mod, newdata=predGrid,

dear list I am student M.S. statistics in department statistics . I am working in the function "nls" in the [R 2.3.1] with 246 data and want to fit the "exp" model to vectors( v and u ) but I have a problem to use it u 5.000000e-13 2.179057e+03 6.537171e+03 1.089529e+04 1.525340e+04 1.961151e+04 2.396963e+04 2.832774e+04 3.268586e+04 3.704397e+04 4.140209e+04

I hope this question is sufficiently different from the other requests for book recommendations that it's not repetitious. If not, I apologize in advance. I'm curious what standard reference books working statisticians, or biostatisticians, have within easy reach of their desk. I'm a computer systems administrator, and have a two-foot bookshelf directory under my monitor that contains