local_fit: Local fit functions

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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These functions define the way in which each local fit/prediction is done within each iteration in the mbl function.

Usage

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local_fit_pls(pls_c)

local_fit_wapls(min_pls_c, max_pls_c)

local_fit_gpr(noise_variance = 0.001)

Arguments

pls_c

an integer indicating the number of pls components to be used in the local regressions when the partial least squares (local_fit_pls) method is used.

min_pls_c

an integer indicating the minimum number of pls components to be used in the local regressions when the weighted average partial least squares (local_fit_wapls) method is used. See details.

max_pls_c

integer indicating the maximum number of pls components to be used in the local regressions when the weighted average partial least squares (local_fit_wapls) method is used. See details.

noise_variance

a numeric value indicating the variance of the noise for Gaussian process local regressions (local_fit_gpr). Default is 0.001.

Details

These functions are used to indicate how to fit the regression models within the mbl function.

There are three possible options for performing these regressions:

Value

An object of class local_fit mirroring the input arguments.

Author(s)

Leonardo Ramirez-Lopez

References

Shenk, J., Westerhaus, M., and Berzaghi, P. 1997. Investigation of a LOCAL calibration procedure for near infrared instruments. Journal of Near Infrared Spectroscopy, 5, 223-232.

Rasmussen, C.E., Williams, C.K. Gaussian Processes for Machine Learning. Massachusetts Institute of Technology: MIT-Press, 2006.

See Also

mbl

Examples

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local_fit_wapls(min_pls_c = 3, max_pls_c = 12)

resemble documentation built on Nov. 9, 2020, 5:08 p.m.