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Simple smoothing of tf objects

Source: R/smooth.R
tf_smooth.Rd

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()).

Value

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

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).

Examples

library(zoo)
#> 
#> Attaching package: ‘zoo’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.Date, as.Date.numeric
library(pracma)
#> 
#> Attaching package: ‘pracma’
#> The following object is masked from ‘package:fdasrvf’:
#> 
#>     gradient
f <- tf_sparsify(tf_jiggle(tf_rgp(4, 201, nugget = 0.05)))
f_lowess <- tf_smooth(f, "lowess")
#> Using `f = 0.15` as smoother span for `lowess()`.
# these methods ignore the distances between arg-values:
f_mean <- tf_smooth(f, "rollmean")
#> ✖ Non-equidistant arg-values in `x` ignored by "rollmean".
#> Using `k = 5` observations for rolling data window.
#> Setting `fill = 'extend'` for start/end values.
f_median <- tf_smooth(f, "rollmedian", k = 31)
#> ✖ Non-equidistant arg-values in `x` ignored by "rollmedian".
#> Setting `fill = 'extend'` for start/end values.
f_sg <- tf_smooth(f, "savgol", fl = 31)
#> ✖ Non-equidistant arg-values in `x` ignored by "savgol".
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 = "original and\n savgol (red)")
lines(f_sg, col = "red")