Hi all, I have a simple graphing question that is not really a graphing question, but a question about repeating a task.
I’m fiddling with some of McElreath’s Statistical Rethinking, and there’s a graph illustrating extreme overfitting (a number of polynomial terms in x equal to the number of observations), a subject I know well having taught it to grad students for many years. The plot I want to reproduce has, in effect: m1 <- lm( y ~ x) m2 <- lm( y ~ x + x^2) …etc., through lm( y ~ x + x^2 + x^3 + x^4 + x^5 + x^6 ), followed by some plot() or lines() or ggplot2() call to render the data and fitted curves. Obviously I don’t want to run such regressions for any real purpose, but I think it might be useful to learn how to do such a thing in R without writing down each lm() call individually. It’s not obvious where I’d want to apply this, but I like learning how to repeat things in a compact way. So, something like: data( mtcars ) d <- mtcars v <- c( 1 , 2 , 3 , 4 , 5 , 6 ) m1 <- lm( mpg ~ hp , data = d ) and then somehow use for() with an index or some flavor of apply() with the vector v to repeat this process yielding m2 <- lm( mpg ~ hp + I( hp ^2 ) , data=d) m3 <- lm( mpg ~ hp + I( hp^2 ) + I(hp^3) , data=d ) … and the rest through m6 <- lm( mpg ~ hp + I(hp^2) + I(hp^3) + I(hp^4) + I(hp^5) + I(hp^6) , data=d ) But finding a way to index these values including not just each value but each value+1 , then value+1 and value+2, and so on escapes me. Obviously I don’t want to include index values below zero. === Richard Sherman rss....@gmail.com ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
