After partitioning using PULS, this function can plot the functional waves and color different clusters as well as their medoids.

ggwave( toclust.fd, intervals, puls.obj, xlab = NULL, ylab = NULL, lwd = 0.5, alpha = 0.4, lwd.med = 1 )

toclust.fd | A functional data object (i.e., having class |
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intervals | A data set (or matrix) with rows are intervals and columns are the beginning and ending indexes of of the interval. |

puls.obj | A |

xlab | Labels for x-axis. If not provided, the labels stored in |

ylab | Labels for y-axis. If not provided, the labels stored in |

lwd | Linewidth of normal waves. |

alpha | Transparency of normal waves. |

lwd.med | Linewidth of medoid waves. |

A ggplot2 object.

# \donttest{ library(fda) # Build a simple fd object from already smoothed smoothed_arctic data(smoothed_arctic) NBASIS <- 300 NORDER <- 4 y <- t(as.matrix(smoothed_arctic[, -1])) splinebasis <- create.bspline.basis(rangeval = c(1, 365), nbasis = NBASIS, norder = NORDER) fdParobj <- fdPar(fdobj = splinebasis, Lfdobj = 2, # No need for any more smoothing lambda = .000001) yfd <- smooth.basis(argvals = 1:365, y = y, fdParobj = fdParobj) Jan <- c(1, 31); Feb <- c(31, 59); Mar <- c(59, 90) Apr <- c(90, 120); May <- c(120, 151); Jun <- c(151, 181) Jul <- c(181, 212); Aug <- c(212, 243); Sep <- c(243, 273) Oct <- c(273, 304); Nov <- c(304, 334); Dec <- c(334, 365) intervals <- rbind(Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec) PULS4_pam <- PULS(toclust.fd = yfd$fd, intervals = intervals, nclusters = 4, method = "pam") ggwave(toclust.fd = yfd$fd, intervals = intervals, puls = PULS4_pam)# }