Call:
loess(formula = y ~ x)
Number of Observations: 10
Equivalent Number of Parameters: 4.95
Residual Standard Error: 8.734e-16
Trace of smoother matrix: 5.47
Control settings:
normalize: TRUE
span : 0.75
degree : 2
family : gaussian
surface : interpolate cell = 0.2
Douglas Bates <bates at stat.wisc.edu> 12/13/02 04:15PM >>>
"Zhongming Yang" <Zhongming.Yang at cchmc.org> writes:
Hi,
I use lm or loess to make smoothing. After smoothing I need
Standard Error" in my script. Could you please tell me how can I get
this information?
A preferred way would be to use
sqrt(deviance(fm)/df.residual(fm))
if fm is your fitted model.
pFor example
data(Formaldehyde)
fm <- lm(optden ~ carb, data = Formaldehyde)
summary(fm)
Call:
lm(formula = optden ~ carb, data = Formaldehyde)
Residuals:
1 2 3 4 5 6
-0.006714 0.001029 0.002771 0.007143 0.007514 -0.011743
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.005086 0.007834 0.649 0.552
carb 0.876286 0.013535 64.744 3.41e-07 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.008649 on 4 degrees of freedom
Multiple R-Squared: 0.999, Adjusted R-squared: 0.9988
F-statistic: 4192 on 1 and 4 DF, p-value: 3.409e-07
sqrt(deviance(fm)/df.residual(fm))