如何从lm()返回预测值，残差和R方? [英] How to return predicted values, residuals, R square from lm()?

问题描述

这段代码将返回系数:intercept，slop1，slop2

this piece of code will return coefficients :intercept , slop1 , slop2

set.seed(1)
n=10

y=rnorm(n)
x1=rnorm(n)
x2=rnorm(n)

lm.ft=function(y,x1,x2)
  return(lm(y~x1+x2)$coef)

res=list();
for(i in 1:n){
  x1.bar=x1-x1[i]
  x2.bar=x2-x2[i]
  res[[i]]=lm.ft(y,x1.bar,x2.bar)
}

如果我输入:

   > res[[1]]

我得到:

      (Intercept)          x1          x2 
     -0.44803887  0.06398476 -0.62798646 

我们如何返回预测值，残差，R方，.. etc等?

How can we return predicted values,residuals,R square, ..etc?

我需要一些常规信息以从摘要中提取所需的内容吗?

I need something general to extract whatever I need from the summary?

推荐答案

这里发生了几件事.

首先，最好将变量组合到data.frame中:

First, you are better off combining your variables into a data.frame:

df  <- data.frame(y=rnorm(10), x1=rnorm(10), x2 = rnorm(10))
fit <- lm(y~x1+x2, data=df)

如果这样做，那么使用模型对新数据集进行预测要容易得多.

If you do this, then using you model for prediction with a new dataset is is much easier.

第二，可以从模型本身访问一些拟合统计信息，而某些可以从summary(fit)访问.

Second, some of the statistics of the fit are accessible from the model itself, and some are accessible from summary(fit).

coef  <- coefficients(fit)       # coefficients
resid <- residuals(fit)          # residuals
pred  <- predict(fit)            # fitted values
rsq   <- summary(fit)$r.squared  # R-sq for the fit
se    <- summary(fit)$sigma      # se of the fit

要获取系数的统计信息，您需要使用摘要:

To get the statistics of the coefficients, you need to use summary:

stat.coef  <- summary(fit)$coefficients
coef    <- stat.coef[,1]    # 1st column: coefficients (same as above)
se.coef <- stat.coef[,2]    # 2nd column: se for each coef
t.coef  <- stat.coef[,3]    # 3rd column: t-value for each coef
p.coef  <- stat.coef[,4]    # 4th column: p-value for each coefficient

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