绘制线性模型预测的95%置信区间时的错误图

时间:2016-10-04 09:15:16

标签: r plot regression linear-regression lm

我需要一点帮助,用它的置信区间绘制预测。请考虑以下示例

library(Hmisc)
data("mtcars") 

mfit = lm(mpg ~ vs + disp + cyl, data = mtcars)

#disp and cyl at their mean
newcar = data.frame(vs = c(0,1), disp = 230, cyl = 6.188)

pmodel <- predict(mfit, newcar, se.fit=TRUE)

当所有其他变量保持不变(均值/模式)时,我想绘制vs(当为0和1时)的效果。

为此,我运行以下代码:

plot(1:2, pmodel$fit[1:2], ylim=c(0,1), pch=19, xlim=c(.5,2.5), xlab="X",
     ylab = "Predicted values", xaxt = "n", main = "Figure1")
arrows(1:2, (pmodel$fit[1:2] - 1.96 * pmodel$fit[1:2]),
       1:2, (pmodel$fit[1,1] + 1.96 * pmodel$fit[1:2]),
       length=0.05, angle=90, code=3)
axis(1, at=c(1,2), labels=c("0","1"))

我在这里做错了什么?谢谢!

2 个答案:

答案 0 :(得分:1)

请注意,您有ylim = c(0, 1),这是不正确的。在绘制置信区间时,我们必须确保ylim涵盖CI的下限和上限。

## lower and upper bound of CI
lower <- with(pmodel, fit - 1.96 * se.fit)
upper <- with(pmodel, fit + 1.96 * se.fit)

## x-location to plot
xx <- 0:1

## set `xlim` and `ylim`
xlim <- range(xx) + c(-0.5, 0.5)  ## extends an addition 0.5 on both sides
ylim <- range(c(lower, upper))

## produce figure
plot(xx, pmodel$fit, pch = 19, xlim = xlim, ylim = ylim, xaxt = "n",
     xlab = "X", ylab = "Predicted values", main = "Figure1")
arrows(xx, lower, xx, upper, length = 0.05, angle = 90, code = 3)
axis(1, at = xx)

enter image description here

关于您的代码的其他一些评论:

  • fit - 1.96 * se.fit而不是fit - 1.96 * fit;
  • 您可以直接在x-location 0:1上绘图,而不是1:2;
  • 它是fit[1:2]而不是fit[1,1]

答案 1 :(得分:1)

在ggplot中:

df <- data.frame(x=1:2, pred=pmodel$fit[1:2])
df$lower <- df$pred - 1.96 * pmodel$se.fit[1:2] 
df$upper <- df$pred + 1.96 * pmodel$se.fit[1:2]
ggplot(df, aes(x, pred, group=x, col=as.factor(x))) + geom_point(size=2) + 
  geom_errorbar(aes(ymin=lower, ymax=upper), width=0.1)

enter image description here