Shift the scale of a spectrum using a summary function
The fshift()
methods return a spectral object of the same class as the
one supplied as argument but with the spectral data on a zero-shifted scale.
A range of wavelengths is taken as a zero reference and the summary
calculated with f
for this waveband is substracted. This results in a
zero shift (= additive correction) to the values in the returned object.
Metadata attributes are retained unchanged.
fshift(x, ...) ## Default S3 method: fshift(x, ...) ## S3 method for class 'source_spct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "mean", unit.out = getOption("photobiology.radiation.unit", default = "energy"), ... ) ## S3 method for class 'response_spct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "mean", unit.out = getOption("photobiology.radiation.unit", default = "energy"), ... ) ## S3 method for class 'filter_spct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "min", qty.out = getOption("photobiology.filter.qty", default = "transmittance"), ... ) ## S3 method for class 'reflector_spct' fshift(x, range = c(wl_min(x), wl_min(x) + 10), f = "min", qty.out = NULL, ...) ## S3 method for class 'source_mspct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "mean", unit.out = getOption("photobiology.radiation.unit", default = "energy"), ... ) ## S3 method for class 'raw_spct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "mean", qty.out = NULL, ... ) ## S3 method for class 'cps_spct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "mean", qty.out = NULL, ... ) ## S3 method for class 'generic_spct' fshift(x, range = c(wl_min(x), wl_min(x) + 10), f = "mean", col.names, ...) ## S3 method for class 'response_mspct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "mean", unit.out = getOption("photobiology.radiation.unit", default = "energy"), ..., .parallel = FALSE, .paropts = NULL ) ## S3 method for class 'filter_mspct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "min", qty.out = getOption("photobiology.filter.qty", default = "transmittance"), ..., .parallel = FALSE, .paropts = NULL ) ## S3 method for class 'reflector_mspct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "min", qty.out = NULL, ..., .parallel = FALSE, .paropts = NULL ) ## S3 method for class 'raw_mspct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "min", ..., .parallel = FALSE, .paropts = NULL ) ## S3 method for class 'cps_mspct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "min", ..., .parallel = FALSE, .paropts = NULL ) ## S3 method for class 'generic_mspct' fshift( x, range = c(wl_min(x), wl_min(x) + 10), f = "min", col.names, ..., .parallel = FALSE, .paropts = NULL )
x |
An R object |
... |
additional named arguments passed down to |
range |
An R object on which |
f |
character string "mean", "min" or "max" for scaling so that this
summary value becomes the origin of the spectral data scale in the returned
object, or the name of a function taking |
unit.out |
character Allowed values "energy", and "photon", or its alias "quantum" |
qty.out |
character Allowed values "transmittance", and "absorbance" |
col.names |
character vector containing the names of columns or variables to which to apply the scale shift. |
.parallel |
if TRUE, apply function in parallel, using parallel backend provided by foreach |
.paropts |
a list of additional options passed into the foreach function when parallel computation is enabled. This is important if (for example) your code relies on external data or packages: use the .export and .packages arguments to supply them so that all cluster nodes have the correct environment set up for computing. |
A copy of x
with the spectral data values replaced with values
zero-shifted.
a new object of the same class as x
.
default
: Default for generic function
source_spct
:
response_spct
:
filter_spct
:
reflector_spct
:
source_mspct
:
raw_spct
:
cps_spct
:
generic_spct
:
response_mspct
:
filter_mspct
:
reflector_mspct
:
raw_mspct
:
cps_mspct
:
generic_mspct
:
Other rescaling functions:
fscale()
,
getNormalized()
,
getScaled()
,
is_normalized()
,
is_scaled()
,
normalize()
,
setNormalized()
,
setScaled()
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