如何在mutate中以编程方式使用多项式函数? [英] How can you use a polynomial function programmatically in mutate?

查看:165
本文介绍了如何在mutate中以编程方式使用多项式函数?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

我想使用mutate根据预先定义的具有二次/多项式函数的变量为我提供预测值.我可以使用这样的线性公式轻松地做到这一点:

I want to use mutate to give me predicted values based on pre-specified variables with a quadratic / polynomial function. I can easily do this with a linear formula like this:

library(tidyverse)

xvar <- "Sepal.Length"
yvar <- "Sepal.Width"


##linear fit
#what does formula text look like?
formula = !!sym(yvar) ~ !!sym(xvar) 
formula

models <- iris %>%
  nest(-Species) %>%
  mutate(
    # Perform loess (or lm) calculation on each  group
    m = map(data, lm,
            formula = !!sym(yvar) ~ !!sym(xvar) ),
    # Retrieve the fitted values from each model
    fitted = map(m, `[[`, "fitted.values")
  )

但是,尝试使用多项式公式建模会产生错误.我在做什么错了?

However, trying to model with a polynomial formula creates an error. What am I doing wrong?

##polynomial fit

#what does formula text look like?
formula = !!sym(yvar) ~ !!sym(xvar) + I(!!sym(xvar)^2)
formula

#Doesn't work
models <- iris %>%
  nest(-Species) %>%
  mutate(
    # Perform loess (or lm) calculation on each  group
    m = map(data, lm,
            formula = !!sym(yvar) ~ !!sym(xvar) + I(!!sym(xvar)^2)), 
            #formula = Sepal.Length ~ Sepal.Width + I(Sepal.Width^2)), #works
    # Retrieve the fitted values from each model
    fitted = map(m, `[[`, "fitted.values")
  )

#Error in sym(xvar)^2 : non-numeric argument to binary operator

推荐答案

您是否尝试过将方括号放在其他位置?例如sym(xvar ^ 2)还是(!!sym(xvar)) ^ 2?

Have you tried putting the brackets in different places? e.g. sym(xvar ^ 2) or (!!sym(xvar)) ^ 2?

错误消息告诉您sym(xvar)是非数字的,这是正确的.因此,您需要应用一元!运算符之前的二进制^一.

The error messages is telling you that sym(xvar) is non numeric, which is true. So you need to apply the unary !! operator before the binary ^ one.

操作员优先级:

https://stat.ethz .ch/R-manual/R-devel/library/base/html/Syntax.html

这篇关于如何在mutate中以编程方式使用多项式函数?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!

查看全文
登录 关闭
扫码关注1秒登录
发送“验证码”获取 | 15天全站免登陆