similar to: cross validation/GAM/package Daim

Hi I am trying to perform a bootstrap estimate of classification accuracy of a logistic regression using the 'Daim' package in r using the code at the bottom of this post, this all works great and I get the .632+ misclassification accuracy, specificity, sensitivity, AUC etc etc but what I would like is to access the list of AUC for each of the bootstrap samples as I need calculate the 95%

I swear, I didn't touch it! I can't fit GLM's anymore, and I can't make it talk english (for googling the error messages) anymore. > y <- c(1,1,0,1,0,1) > x <- c(2,7,3,5,2,4) > glm(y~x, binomial) Fehler in runif(length(pi)) : Element 1 ist leer; Der Teil der Argumentliste 'length' der berechnet wurde war: (pi) > Sys.setenv(LANG="EN")

Hi, ==================== Reproducible example: ==================== data(Animals, package="MASS") # interesting dataset # Run model lm1 <- lm(log10(body)~log10(brain), data=Animals) # Setup 2x2 graphics device par(mfrow=c(2,2)) # Plot diagnostics, label the two most "extreme" points based on magnitude of residuals plot(lm1, id.n=2) ==============================

Hi, Got my issues sorted. Error message solved: I heard from the guy who developed MuMIn and his suggestion worked. "As for the error you get, it seems you are running an old version of MuMIn. Please update the package first." I did (I was only 1 version behind in both R and in MuMIn) and error disappeared! Running quasipoisson GAM in MuMIn: As for my questions on GAM and " to

Hi, I have a GAM quasipoisson that I'd like to run through MuMIn package - dredge - gettop.models - model.avg However, I'm having no luck with script from an example in MuMIn help file. In MuMIn help they advise "include only models with smooth OR linear term (but not both) for each variable". Their example is: # Example with gam models (based on

Hi, all I have a question on mgcv and ns. Now I want to compare the results from glm, gam and ns. Take a simple model y~x for example. glm1 = glm(y~x, data=data1) gam1 = gam(y~s(x), data=data1) ns1 = glm(y~ns(x),data=data1) In order to confirm the result from glm1 is consistent to those from gam1 and ns1, I want to define degree=1 in mgcv and ns. I am wondering if there is somebody can give me

Hi, (1.) How is it possible to get automatic a "lower triangle of correlation matrix" ? h.cor <- cor(dat,use="pairwise.complete.obs") zz <- lower.tri(h.cor,diag=T) ### that's not what i wish and "wrong" ? results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)])) results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)]),5) Must i take the lowest

Hi, I am using mgcv:gam and have developed a model with 5 smoothed predictors and one factor. gam1 <- gam(log.sp~ s(Spr.precip,bs="ts") + s(Win.precip,bs="ts") + s( Spr.Tmin,bs="ts") + s(P.sum.Tmin,bs="ts") + s( Win.Tmax,bs="ts") +factor(site),data=dat3) The total deviance explained = 70.4%. I would like to extract the variance explained

Hello, I'm trying to illustrate the relationships between various trait and environment data gathered from a number of sites. I've created a GAM to do this: gam1=gam(trait~s(env1)+s(env2)+te(env1,env2)) and I know how to create a 3D plot using vis.gam. I want to be able to show points on the 3D plot indicating the sites that the data came from. I can do this on a 2D plot when there is one

Hello folks. I'm new to the list and I have questions about Samba. I have been able to configure Samba 3.x on Solaris 9 with AD authentication for the users. I'm able to mount the shares onto Windows XP clients and able to read the files. Now, if I use a text editor like notepad or GVIM to save an existing file, it saves it. When I try to use Word, Eclipse, or Crimson Editor, it

Hi, I am looking at the effects of two explanatory variables on chlorophyll. The data are an annual time-series (so are autocorrelated) and the relationships are non-linear. I want to account for autocorrelation in my model. The model I am trying to use is this: Library(mgcv) gam1 <-gam(Chl~s(wintersecchi)+s(SST),family=gaussian, na.action=na.omit, correlation=corAR1(form =~

Dear r-Community, Step1: I would like to calculate a NMDS (package vegan, function metaMDS) with species data. Step2: Then I want to plot environmental variables over it, using function envfit. The Problem: One of these environmental variables is cos(EXPOSURE). But for flat releves there is no exposure. The value is missing and I can't call it 0 as 0 stands for east and west. Therefore I

Dear r-helpers! I want to classify my vegetation data with hierachical cluster analysis. My Dataset consist of Abundance-Values (Braun-Blanquet ordinal scale; ranked) for each plant species and relev?. I found a lot of r-packages dealing with cluster analysis, but none of them is able to calculate a distance measure for ranked data. Podani recommends the use of Goodman and Kruskals' Gamma for

Hi all, I am attempting to learn my way through the sem package by constructing a simple structural model for some of my data on bird diversity, abundance, and primary productivity. I have constructed a covariance matrix between these variables as per the following: >S_matrix = matrix(c( >+ 0.003083259, 0, 0, >+ 0.143870284, 89.7648490, 0, >+ 0.276950919,

I propose the following enhancements and changes to plot.lm(), the most important of which is the addition of a Residuals vs Leverage plot. (1) A residual versus leverage plot has been added, available by specifying which = 5, and not included as one of the default plots. Contours of Cook's distance are included, by default at values of 0.5 and 1.0. The labeled points, if any, are those

Thanks & Regards Hitesh Lad Jr. System Administrator Suma Soft Pvt. Ltd., Pune. ----- Original Message ----- From: <samba-request@lists.samba.org> To: <samba@lists.samba.org> Sent: Thursday, February 08, 2007 8:49 AM Subject: samba Digest, Vol 50, Issue 11 > Send samba mailing list submissions to > samba@lists.samba.org > > To subscribe or unsubscribe via the World

Hi! I want to construct a path analysis model that can account for measurement error in totally aggregated parcels, which refer to parcels where all of the items in a scale are summed or averaged. If I am not mistaken, Bollen (1989) advocates the following formula for computing the error variance of each parcel: (1−α(parcel))×variance(parcel), such that α refers to Cronbach's alpha, which

I wonder if someone could explain why, when I perform confirmatory factor-analysis model using polychoric correlations why I do not get an estimated confidence interval for the RMSEA. My experience with these type models is that I would obtain a confidence interval estimate. I did not get any warning messages with the output. RESULTS: Model Chisquare = 1374 Df = 185 Pr(>Chisq) = 0

Dear R users, Version 2.1-1 of the sem package, for structural equation modeling, is now on CRAN. Unlike version 2.0-0, which was a major overhaul of the package, version 2.1-1 just sprinkles some syntactic sugar on it, introducing the specifyEquations() and cfa() functions; specifyEquations() supports model specification in equation (rather than path) format, and cfa() facilitates compact

Dear R users, Version 2.1-1 of the sem package, for structural equation modeling, is now on CRAN. Unlike version 2.0-0, which was a major overhaul of the package, version 2.1-1 just sprinkles some syntactic sugar on it, introducing the specifyEquations() and cfa() functions; specifyEquations() supports model specification in equation (rather than path) format, and cfa() facilitates compact