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lpc()
function argument.lpc.control(iter =100, cross=TRUE,
boundary = 0.005, convergence.at = 0.00001,
mult=NULL, ms.h=NULL, ms.sub=30,
pruning.thresh=0.0, rho0=0.4)
iter
iterations.x0
, then the missing points will be set at
random (For example, if $d=2$, h
used in
function lpc
is used here toomin(max(ms.sub, floor(ms.sub*N/100)), 10*ms.sub)
trajectories.
control
argument of the lpc
function.[2] Einbeck, J. and Zayed, M. (2011). Some asymptotics for localized principal components and curves. Working paper, Durham University. Unpublished.
data(calspeedflow)
fit1 <- lpc(calspeedflow[,c(3,4)], x0=c(50,60),scaled=TRUE,
control=lpc.control(iter=20, boundary=0))
plot(fit1, type=c("curve","start","mass"))
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