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    <p>Another option, with <code>ldply</code>, <strong>but see hadley's comment below for a more efficient use of <code>ldply</code></strong>: </p> <pre><code> # prepare data x &lt;- seq(1:10) y &lt;- sin(x)^2 dat &lt;- data.frame(x,y) # create list of named models obviously these are not suited to the data here, just to make the workflow work... models &lt;- list(model1=lm(y~x, data = dat), model2=lm(y~I(1/x), data=dat), model3=lm(y ~ log(x), data = dat), model4=nls(y ~ I(1/x*a) + b*x, data = dat, start = list(a = 1, b = 1)), model5=nls(y ~ (a + b*log(x)), data=dat, start = setNames(coef(lm(y ~ log(x), data=dat)), c("a", "b"))), model6=nls(y ~ I(exp(1)^(a + b * x)), data=dat, start = list(a=0,b=0)), model7=nls(y ~ I(1/x*a)+b, data=dat, start = list(a=1,b=1)) ) library(plyr) library(AICcmodavg) # for small sample sizes # build table with model names, function, AIC and AICc data.frame(cbind(ldply(models, function(x) cbind(AICc = AICc(x), AIC = AIC(x))), model = sapply(1:length(models), function(x) deparse(formula(models[[x]]))) )) .id AICc AIC model 1 model1 15.89136 11.89136 y ~ x 2 model2 15.78480 11.78480 y ~ I(1/x) 3 model3 15.80406 11.80406 y ~ log(x) 4 model4 16.62157 12.62157 y ~ I(1/x * a) + b * x 5 model5 15.80406 11.80406 y ~ (a + b * log(x)) 6 model6 15.88937 11.88937 y ~ I(exp(1)^(a + b * x)) 7 model7 15.78480 11.78480 y ~ I(1/x * a) + b </code></pre> <p>It's not immediately obvious to me how to replace the <code>.id</code> with a column name in the <code>ldply</code> function, any tips?</p>
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    1. COOr just: `ldply(models, function(mod) data.frame(AICc = AICc(mod), AIC = AIC(mod), model = deparse(formula(mod))) })`
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    2. CO@hadley, thanks yes, that's much simpler (an open brace needed to make it work). Would you care to simplify this one from the school of dirty tricks: http://stackoverflow.com/a/15104211/1036500 ?
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