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I am using earth packageearth: Multivariate Adaptive Regression Spline Models regression to get a constant piecewise approximation of my data. I want to plot a band of confidence around it. Does this make sense to estimate a confidence interval of the smoothed function? If yes how can I do this?

I know that confidence intervals cannot be calculated directly, since it is a non parametric regression but I want to have a coherent result like (plot a band of confidence around my smoothed function) what I can get using lm or loess smoothing.

EDIT

I add some code to clarify my idea, Here what I would do if I am using linear regression or loess.

set.seed(1)
x <- rnorm(15)
dat <- data.frame(x = x, y = c(x + rnorm(15)))

Using lm

mod <- lm(y ~ x,data=dat)
predfit <- predict(mod,se=TRUE,interval="confidence")$fit

Using loess

level=0.95
mod <- loess(y~x,data=dat)
pred <- predict(mod, se = TRUE)
y = pred$fit
    ci <- pred$se.fit * qt(level / 2 + .5, pred$df)
data.frame(ymin = y - ci,
           ymax = y + ci)

My question how to get ymin and ymax if I use earth :

library(earth)
mod = earth(y~x,data=dat)
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  • $\begingroup$ please edit your source data.frame "dat". When I try to create it a error occurs: (longer object length is not a multiple of shorter object length).Thx $\endgroup$ – Ladislav Naďo Jan 20 '14 at 15:24
  • $\begingroup$ Yes, it makes sense to estimate a confidence band of the smoothed function. A reference is the scb function for simultaneously confidence band in package locfit. $\endgroup$ – Randel Jan 23 '14 at 4:15
  • $\begingroup$ Wasserman (2006) stated that, "For more complicated methods like trees, MARS and projection pursuit regression, I am not aware of rigorous results that lead to valid bands." Maybe we need to wait for the breakthrough in theory. $\endgroup$ – Randel Jan 23 '14 at 19:07
  • $\begingroup$ @Randel thanks for your interest. I read this also, but I think we can get some practical results using bootstrapping methods... $\endgroup$ – agstudy Jan 23 '14 at 19:17
  • $\begingroup$ I consulted a professor about this, and he told me that it is possible to construct the confidence interval for a fixed point, but impossible to construct the confidence band. But a non-rigorous variant of confidence band, variability band, can be constructed. $\endgroup$ – Randel Feb 6 '14 at 20:17
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Earth will estimate prediction intervals for you. Do it like this:

mod = earth(y~x,data=dat, ncross=30, nfold=3, varmod.method="lm")
plotmo(mod, pt.col=1, level=.95) # show prediction intervals

summary(mod) will print this:

varmod: method "lm"    min.sd 0.124    iter.rsq 0.016

stddev of predictions:
            coefficients iter.stderr iter.stderr%
(Intercept)       1.2265      0.2059           17
y                 0.0883      0.1918          217

                          mean   smallest   largest   ratio
95% prediction interval   4.86       3.91       5.1     1.3

The above code illustrates the principle, but a problem with your small sample size is that there is not enough data to estimate prediction intervals reliably. (We know this because the iter.stderr% are very big, and the iter.rsq is very small. There is a vignette that comes with the earth package that explains in detail how to get prediction intervals, and some of the potential pitfalls.)

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