honigs {prospectr} | R Documentation |
Select calibration samples from a data matrix
or
data.frame
using the Honings et al. (1985) method
honigs(X,k,type)
X |
numeric |
k |
number of samples to select for calibration |
type |
type of data: 'A' for absorbance (default), 'R' for reflectance, 'CR' for continuum-removed reflectance |
The Honigs algorithm is a simple method to select
calibration samples based on their absorption features.
Absorbance, reflectance and continuum-removed reflectance
values (see continuumRemoval
) can be used
(type
argument). The algorithm can be described as
follows: let A be a matrix of (i \times j)
absorbance values:
the observation (row) with the maximum absolute absorbance (max(|A|)) is selected and assigned to the calibration set.
a vector of weights W is computed as A_j/max_A where A_j is the column of A having the maximum absolute absorbance and max_A is the absorbance value corresponding to the maximum absolute absorbance of A.
each row A_i is multiplied by the corresponding weight W_i and the resulting vector is substracted from the original row A_i.
the row of the selected observation and the column with the maximum absolute absorbance is removed from the matrix
go back to step 1 and repeat the procedure until the desired number of selected samples is reached
The observation with the maximum absorbance is considered to have an unusual composition. The algorithm selects therefore this observation and remove from other samples the selected absorption feature by substraction. Samples with low concentration related to this absorption will then have large negative absorption after the substraction step and hence will be likely to be selected rapidly by the selection procedure as well.
a list
with components:
'model
' numeric vector
giving the row
indices of the input data selected for calibration
'test
' numeric vector
giving the row
indices of the remaining observations
'bands
' indices of the columns used during
the selection procedure
The selection procedure is sensitive to noisy features in
the signal. The number of samples selected k
selected by the algorithm cannot be greater than the number
of wavelengths.
Antoine Stevens
Honigs D.E., Hieftje, G.M., Mark, H.L. and Hirschfeld, T.B. 1985. Unique-sample selection via Near-Infrared spectral substraction. Analytical Chemistry, 57, 2299-2303
kenStone
, naes
,
duplex
, shenkWest
data(NIRsoil) sel <- honigs(NIRsoil$spc,k=10,type='A') wav <- as.numeric(colnames(NIRsoil$spc)) # spectral library matplot(wav,t(NIRsoil$spc),type='l',xlab='wavelength /nm',ylab='Abs',col='grey50') # plot calibration spectra matlines(wav,t(NIRsoil$spc[sel$model,]),type='l',xlab='wavelength /nm',ylab='Abs',lwd=2,lty=1) # add bands used during the selection process abline(v=wav[sel$bands])