Description Usage Arguments Slots Author(s)
Return the covariance matrices
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any paramaters to be input into the function |
m
spectroscopic data
modelname
name of model to be used for calculating the covariance matrix. Available models are "full", "parsimonious". Default is "full".
spectra
type of spectra. Available models are "diag", "unknown" and "kernel". Default is "diag".
time
type of time. Available models are "diag", "unknown" and "kernel". Default is "diag".
kerneltypeSpectra
kernel to be used for covariance matrix of spectra Available kernels are "epanechnikov", "gaussian", "exponential", "uniform", "quadratic", "circular", "triangular", "rational quadratic", "inverse multiquadratic". Default is "exponential".
kerneltypeTime
kernel to be used for covariance matrix of time Available kernels are "epanechnikov", "gaussian", "exponential", "uniform", "quadratic", "circular", "triangular", "rational quadratic", "inverse multiquadratic". Default is "exponential".
h
used for kernel calculation
s
correction limit paramater for flip flop algorithm
lambdaS
regularisation for spectra for flip flop algorithm
lambdaT
regularisation for spectra for flip flop algorithm
validation
to optimize lambda in case of th model is : M = parsimonious, S=unknown, T=unknow
listLambdaS
list of lambdaS used in prediction in case validation is TRUE
listLambdaT
list of lambdaT used in prediction in case validation is TRUE
model
use in prediction in case of validation is TRUE
covMat
returning the covariance matrx
Asmita Poddar & Florent Latimier
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