tf_smooth {tf}

Simple smoothing of tf objects

Description

Apply running means or medians, lowess or Savitzky-Golay filtering to smooth functional data. This does nothing for tfb-objects, which should be smoothed by using a smaller basis / stronger penalty.

Usage

tf_smooth(x, ...)

## S3 method for class 'tfb'
tf_smooth(x, verbose = TRUE, ...)

## S3 method for class 'tfd'
tf_smooth(
  x,
  method = c("lowess", "rollmean", "rollmedian", "savgol"),
  verbose = TRUE,
  ...
)

Arguments

x

a tf object containing functional data

...

arguments for the respective method. See Details.

verbose

give lots of diagnostic messages? Defaults to TRUE

method

one of "lowess" (see stats::lowess()), "rollmean", "rollmedian" (see zoo::rollmean()) or "savgol" (see pracma::savgol())

Details

tf_smooth.tfd overrides/automatically sets some defaults of the used methods:

• lowess uses a span parameter of f = 0.15 (instead of 0.75) by default.

• rollmean/median use a window size of k = $<$number of grid points$>$/20 (i.e., the nearest odd integer to that) and sets fill= "extend" (i.e., constant extrapolation to replace missing values at the extremes of the domain) by default. Use fill= NA for zoo's default behavior of shortening the smoothed series.

• savgol uses a window size of k = $<$number of grid points$>$/10 (i.e., the nearest odd integer to that).

Value

a smoothed version of the input. For some methods/options, the smoothed functions may be shorter than the original ones (at both ends).

Examples

library(zoo)
library(pracma)
f <- tf_sparsify(tf_jiggle(tf_rgp(4, 201, nugget = 0.05)))
f_lowess <- tf_smooth(f, "lowess")
# these methods ignore the distances between arg-values:
f_mean <- tf_smooth(f, "rollmean")
f_median <- tf_smooth(f, "rollmean", k = 31)
f_sg <- tf_smooth(f, "savgol", fl = 31)
layout(t(1:4))
plot(f, points = FALSE, main = "original")
plot(f_lowess,
  points = FALSE, col = "blue", main = "lowess (default,\n span 0.9 in red)"
)
lines(tf_smooth(f, "lowess", f = 0.9), col = "red", alpha = 0.2)
plot(f_mean,
  points = FALSE, col = "blue", main = "rolling means &\n medians (red)"
)
lines(f_median, col = "red", alpha = 0.2) # note constant extrapolation at both ends!
plot(f, points = FALSE, main = "orginal and\n savgol (red)")
lines(f_sg, col = "red")