如何在R中建模多项式回归? [英] How to model polynomial regression in R?

问题描述

我有一个包含70个变量的数据集，我想尝试对其进行多项式回归.如果列数是三/四，我可以手动编写类似这样的代码-

I've a dataset with 70 variables, and I want to try polynomial regression on it. If the number of columns were three/four I could just hand code something like this --

 model <- lm(y ~ poly(var1,3) + poly(var2,3) + poly(var4,4)

如果我们有70个变量，我们将如何处理?我们应该手动输入所有变量的名称还是有一种更简单的方法?

How would we go about this, if we have 70 variables? Should we type in manually names of all the variables or is there a easier method?

推荐答案

如果所有变量都被系统命名，则可以粘贴公式:

You could paste the formula, if all variables are named systematically:

form <- as.formula(paste("y~", paste0("poly(var", 1:10, ")", collapse="+")))

或(对于3级多项式):

or (for polynomial of 3rd degree):

form <- as.formula(paste("y~", paste0("poly(var", 1:10, ", degree=3)", collapse="+")))

此外，如果数据集中df中只有因变量y和相关协变量(具有非系统名称)，则可以尝试

Also, if you have only the dependent variable y and covariates of interest (that have non-systematic names) in your dataset df, you can try

ind.y <- grep("y", colnames(df))
form <- as.formula(paste("y~", paste0("poly(", colnames(df[, -ind.y]), ", degree=3)", collapse="+")))

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