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问题描述

对于R的统计"数据包的LOWESS回归线的置信区间(CI),我找不到任何令人满意的答案:

I didn't find any satisfactory answer to the confidence intervals (CIs) for LOWESS regression line of the 'stats' package of R:

plot(cars, main = "lowess(cars)")
lines(lowess(cars), col = 2)

但是我不确定如何围绕它绘制95%的CI?但是,我知道我可以从中获取估计的差异

But I'm unsure how to draw a 95% CI around it?? However, I know I could get the estimated variance from

V = s^2*sum(w^2)

其中,s2 =估计误差方差,w =应用于X的权重.因此,95%CI应该是

where, s2= estimated error variance, and w=weights applied to the X. Therefore, the 95% CIs should be

Y plus/minus 2*sqrt(V(Y))

我知道有一种方法可以使黄土适合CI,但是我更喜欢LOWESS,因为它很坚固.感谢您的建议.

I know there's a way of getting the CIs from loess fit, but I'd rather prefer LOWESS because it is robust. Thanks for your suggestions.

推荐答案

您可以使用predict()loess()进行此操作. lowessloess更旧,并且功能较少,但速度更快.但是在这种情况下,我将按以下方式使用loess.

You can do this with predict() and loess(). lowess is older than loess and has fewer features, though it is a bit faster. But in this context, I'd use loess as follows.

plot(cars)
plx<-predict(loess(cars$dist ~ cars$speed), se=T)

lines(cars$speed,plx$fit)
lines(cars$speed,plx$fit - qt(0.975,plx$df)*plx$se, lty=2)
lines(cars$speed,plx$fit + qt(0.975,plx$df)*plx$se, lty=2)

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09-15 03:17