Why go to so much trouble? Why not fit a single full model and use it? Even better why not use a quadratic penalty on the full model to get optimum cross-validation? Frank
nofunsally wrote: > > Hello, > I'd like to sum the weights of each independent variable across linear > models that have been evaluated using AIC. > > For example: > >> library(MuMIn) >> data(Cement) >> lm1 <- lm(y ~ ., data = Cement) >> dd <- dredge(lm1, beta = TRUE, eval = TRUE, rank = "AICc") >> get.models(dd, subset = delta <4) > > There are 5 models with a Delta AIC Score of less than 4. I would > like to sum the weights for each of the independent variables across > the five models. > > How can I do that? > > Thanks, > Mike > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Sum-weights-of-independent-variables-across-models-AIC-tp3666306p3668513.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.