pcp {unknown} | R Documentation |
Detect change points to the $d$th derivative of the mean function using partial smoothing spline models.
pcp(x, y, data, d=1, spline = list(nb=~x, rk=cubic(x)), spar="v", limnla=c(-10, 3), alpha)
The mean function is assumed to be smooth, except for some
potential change points to the derivatives. The smooth function plus
representations of those potential change points is called a partial
spline model (Wahba, 1990). We use the methods proposed in Yang (2002)
to detect potential change points. The ssr
function in the
assist package is used to fit partial spline models.
See ASSIST manual
for more information about nb, rk, spar and
limnla.
a vector of pulse locations.
Yu-Chieh Yang, Anna Liu, Yuedong Wang
Wahba, G. (1990), Spline Models for Observational Data, SIAM, CBMS-NSF Regional Conference Series in Applied Mathematics, Vol. 59, Philadelphia.
Wang, Y. and Ke, C. (2002), ASSIST: A Suite of S-plus functions Implementing Spline smoothing Techniques. Available at cran.r-project.org. Manual for the ASSIST package is available at www.pstat.ucsb.edu/faculty/yuedong/software.
Yang, Y. (2002), Detecting Change Points and Hormone Pulses Using Partial Spline Models, Ph.D. Thesis, University of California-Santa Barbara, Dept. of Statistics and Applied Probability.
pl3 <- pcp(time, conc, data=acth, alpha=0.6)