Method to compute the "parallel" mean and the SEM. The spectral values are summarised across members of a collection of spectra or of a spectral object containing multiple spectra in long form.
s_mean_se(x, na.rm, mult, ...)# S3 method for default
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
# S3 method for generic_spct
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
# S3 method for filter_mspct
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
# S3 method for source_mspct
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
# S3 method for response_mspct
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
# S3 method for reflector_mspct
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
# S3 method for calibration_mspct
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
# S3 method for cps_mspct
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
# S3 method for raw_mspct
s_mean_se(x, na.rm = FALSE, mult = 1, ...)
If x is a collection spectral of objects, such as a
"filter_mspct" object, the returned object belongs to the same class
as the members of the collection, such as "filter_spct", containing
the summary spectrum, with variables with names tagged for summaries other
than mean or median.
An R object.
logical A value indicating whether NA values should be stripped before the computation proceeds.
numeric number of multiples of standard error.
Further arguments passed to or from other methods.
Parallel summaries differ fundamentally from the "deepest curves" obtained through functional data analysis (FDA) in that in functional data analysis one of the input curves is returned as the deepest one based on a decision criterion. In contrast the parallel summaries from package 'photobioloy' return one or more "fictional" curves different to any of those passed as inputs. This curve is constructed from independent summaries at each wavelength value.
Method specializations compute the mean and SEM at
each wavelength across a group of spectra stored in an object of one of the
classes defined in package 'photobiology'. Omission of NAs is done
separately at each wavelength. Interpolation is not applied, so all spectra
in x must share the same set of wavelengths. An error is triggered
if this condition is nor fulfilled. The value passed as argument to `mult`