loexp | R Documentation |
Expectile regression for time series curve by decomposing data matrix
Y = X B + E, where B is "mostly non-negative".
loexp(y, w=rep(1, length(y)), sigma=40, polyo=2, alpha=0.5, biweight=4.685, tol=1e-04, maxIter=50)
y |
A |
w |
A |
sigma |
The standard deviation of Gaussian kernel. |
polyo |
The order of polynomial, currently can be 0, 1, or 2. |
alpha |
The desired expectile. |
biweight |
Parameter used in Tukey's biweight function. |
tol |
The tolerance for expectile estimation. |
maxIter |
The maximum number of iterations in estimation step. |
A list
with components:
intparams |
An |
dblparams |
A |
y |
The input |
w |
The input |
outy |
The output |
outw |
The output |
Asa Wirapati, Mark Robinson
[1] ...
data(loexp.example) plot(ex1, pch=19, type="b") # 50th expectile (median) lines(loexp(ex1, alpha=0.5)$outy, lwd=4, col="blue") # 0.5th expectile (baseline) lines(loexp(ex1, alpha=0.005)$outy, lwd=4, col="red") plot(ex2.y, pch=19, type="b") # give weight=0 for 0s lines(loexp(ex2.y, w=ex2.w, alpha=0.005)$outy, lwd=4, col="blue")
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