如何解决从R中的黄土回归结果中提取残留标准误差
> summary(fit.loess[[i]])
Call:
loess(formula = dfcpm[,ncol(dfcpm)] ~ dfcpm[,i],data = dfcpm,span = 0.5,degree = 1,normalize = FALSE,family = "gaussian")
Number of Observations: 88
Equivalent Number of Parameters: 4.7
Residual Standard Error: 21.7
Trace of smoother matrix: 5.53 (exact)
Control settings:
span : 0.5
degree : 1
family : gaussian
surface : interpolate cell = 0.2
normalize: FALSE
parametric: FALSE
drop.square: FALSE
现在,我要提取该模型的残差标准误差。我如何提取它?我无法在模型对象的任何位置(即21.7)找到该值。
> names(fit.loess[[i]])
[1] "n" "fitted" "residuals" "enp" "s" "one.delta" "two.delta" "trace.hat"
[9] "divisor" "robust" "pars" "kd" "call" "terms" "xnames" "x"
[17] "y" "weights"
解决方法
它是s
返回中的loess
元素。
> lo <- loess(mpg ~ wt,data=mtcars)
> print(lo)
#Call:
#loess(formula = mpg ~ wt,data = mtcars)
#
#Number of Observations: 32
#Equivalent Number of Parameters: 5
#Residual Standard Error: 2.711
> lo$s
#[1] 2.711351
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