binning | R Documentation |
Compute average values of a signal in pre-determined bins (col-wise subsets). The bin size can be determined either directly or by specifying the number of bins. Sometimes called boxcar transformation in signal processing
binning(X, bins, bin.size)
X |
a numeric matrix or vector to process (optionally a data frame that can be coerced to a numerical matrix). |
bins |
the number of bins. |
bin.size |
the desired size of the bins. |
a matrix or vector with average values per bin.
Antoine Stevens & Leonardo Ramirez-Lopez
savitzkyGolay
, movav
,
gapDer
, continuumRemoval
data(NIRsoil) wav <- as.numeric(colnames(NIRsoil$spc)) # 5 first spectra matplot(wav, t(NIRsoil$spc[1:5, ]), type = "l", xlab = "Wavelength /nm", ylab = "Absorbance" ) NIRsoil$spc_binned <- binning(NIRsoil$spc, bin.size = 20) # bin means matpoints(as.numeric(colnames(NIRsoil$spc_binned)), t(NIRsoil$spc_binned[1:5, ]), pch = 1:5 ) NIRsoil$spc_binned <- binning(NIRsoil$spc, bins = 20) dim(NIRsoil$spc_binned) # 20 bins # 5 first spectra matplot(wav, t(NIRsoil$spc[1:5, ]), type = "l", xlab = "Wavelength /nm", ylab = "Absorbance" ) # bin means matpoints(as.numeric(colnames(NIRsoil$spc_binned)), t(NIRsoil$spc_binned[1:5, ]), pch = 1:5 )
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