如何创建由多个残差组成的图？

Question

如何根据以下（这里的y_hat和e是什么）制作残差图？

这也是残差图的一种形式吗？

beeflm=lm(PBE ~ CBE + PPO + CPO + PFO +DINC + CFO+RDINC+RFP+YEAR, data = beef)
summary(beeflm)
qqnorm(residuals(beeflm))
#plot(beeflm) #in manuals I have seen they use this but it gives me multiple plot

或者这个是正确的吗？

plot(beeflm$residuals,beeflm$fitted.values)

我通过 cmets 知道 plot(beeflm,which=1) 是正确的，但根据所述问题我应该使用 matplot 但我收到以下错误：

matplot(beeflm,which=1,
+         main = "Beef: residual plot",
+         ylab = expression(e[i]), # only 1st is taken
+         xlab = expression(hat(y[i])))

Error in xy.coords(x, y, xlabel, ylabel, log = log) : 
  (list) object cannot be coerced to type 'double'

当我使用plot 时，我收到以下错误：

plot(beeflm,which=1,main="Beef: residual plot",ylab = expression(e[i]),xlab = expression(hat(y[i])))
Error in plot.default(yh, r, xlab = l.fit, ylab = "Residuals", main = main,  : 
  formal argument "xlab" matched by multiple actual arguments

你也知道下面是什么意思吗？有什么例子可以说明这一点（或外部链接）？

牛肉数据如下：

这是牛肉的data.frame：

   YEAR  PBE  CBE  PPO  CPO  PFO DINC  CFO RDINC RFP
1  1925 59.7 58.6 60.5 65.8 65.8 51.4 90.9  68.5 877
2  1926 59.7 59.4 63.3 63.3 68.0 52.6 92.1  69.6 899
3  1927 63.0 53.7 59.9 66.8 65.5 52.1 90.9  70.2 883
4  1928 71.0 48.1 56.3 69.9 64.8 52.7 90.9  71.9 884
5  1929 71.0 49.0 55.0 68.7 65.6 55.1 91.1  75.2 895
6  1930 74.2 48.2 59.6 66.1 62.4 48.8 90.7  68.3 874
7  1931 72.1 47.9 57.0 67.4 51.4 41.5 90.0  64.0 791
8  1932 79.0 46.0 49.5 69.7 42.8 31.4 87.8  53.9 733
9  1933 73.1 50.8 47.3 68.7 41.6 29.4 88.0  53.2 752
10 1934 70.2 55.2 56.6 62.2 46.4 33.2 89.1  58.0 811
11 1935 82.2 52.2 73.9 47.7 49.7 37.0 87.3  63.2 847
12 1936 68.4 57.3 64.4 54.4 50.1 41.8 90.5  70.5 845
13 1937 73.0 54.4 62.2 55.0 52.1 44.5 90.4  72.5 849
14 1938 70.2 53.6 59.9 57.4 48.4 40.8 90.6  67.8 803
15 1939 67.8 53.9 51.0 63.9 47.1 43.5 93.8  73.2 793
16 1940 63.4 54.2 41.5 72.4 47.8 46.5 95.5  77.6 798
17 1941 56.0 60.0 43.9 67.4 52.2 56.3 97.5  89.5 830

Answer 1

使用plot(beeflm, which=1) 获取残差和拟合值之间的图。

require(graphics)

## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## Page 9: Plant Weight Data.
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
plot(lm.D9, which=1)

---

已编辑

您可以使用matplot，如下所示：

matplot(
  x = lm.D9$fitted.values
, y = lm.D9$resid
)

Answer 2

使用mtcars 数据的示例：

fit <- lm(mpg ~ ., data=mtcars)
plot(x=fitted(fit), y=residuals(fit))

和

par(mfrow=c(3,4)) # or 'layout(matrix(1:12, nrow=3, byrow=TRUE))'
for (coeff in colnames(mtcars)[-1])
  plot(x=mtcars[, coeff], residuals(fit),  xlab=coeff, ylab=expression(e[i]))