pcp {unknown}R Documentation

Potential Change Points


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.

See Also

pulini, ssr


pl3 <- pcp(time, conc, data=acth, alpha=0.6)

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