model: Global spectral calibration model

In resemble: Similarity Retrieval and Local Learning for Spectral Chemometrics

Global spectral calibration model

Description

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Fits a global calibration model for spectral data using partial least squares (PLS) or Gaussian process regression (GPR). Unlike mbl, which builds local models for each prediction, model() fits a single global model to the reference data.

Usage

model(Xr, Yr, fit_method, control = model_control(), verbose = TRUE)

## S3 method for class 'resemble_model'
predict(object, newdata, ncomp = NULL, ...)

Arguments

Xr	A numeric matrix of predictor variables, with observations in rows and variables in columns. Typically spectral data.
Yr	A numeric matrix with one column containing the response variable values corresponding to the observations in Xr.
fit_method	An object created by fit_pls or fit_gpr specifying the regression method and its parameters, including centring and scaling options. fit_wapls() is not supported for global models because it requires target observations to compute weights.
control	A list created by model_control() specifying the cross-validation settings. The default is model_control().
verbose	Logical indicating whether progress information should be printed. Default is TRUE.
object	A resemble_model object returned by model.
newdata	A numeric matrix of new observations with the same number of columns as the training data.
ncomp	For PLS models, the number of components to use for prediction. The default is the number used during fitting. Ignored for GPR models. For prediction, this can be a vector of integers representing the predictions coming from models with the requested components.
...	Additional arguments, currently unused.

Details

model() provides a straightforward interface for fitting global calibration models, in contrast to the local modelling approach implemented in mbl. This is useful when:

• the relationship between spectra and the response is approximately linear across the full dataset

• a single portable model is needed for deployment

• computational efficiency is prioritised over predictive performance in heterogeneous datasets

Cross-validation

When validation_type = "lgo", stratified random sampling is used to create training-validation splits that preserve the distribution of the response variable. Cross-validation results include RMSE, R², and standardised RMSE for each component in PLS models, or overall in GPR models.

PLS models

When fit_pls() is used, the function fits a PLS model with the specified number of components. If cross-validation is enabled, the optimal number of components is selected based on the minimum RMSE.

GPR models

When fit_gpr() is used, the function fits a Gaussian process regression model with a dot-product covariance function. The noise variance parameter controls regularisation.

Value

For model(), an object of class resemble_model containing:

• fit_method: Fitting method object used.

• control: Model control object used.

• model: Fitted model object.

• cv_results: Cross-validation results, if validation_type = "lgo".

• n_obs: Number of observations used.

• n_vars: Number of predictor variables.

For predict.resemble_model(), a numeric matrix of predictions. For PLS models, columns correspond to different numbers of components.

Author(s)

Leonardo Ramirez-Lopez

References

de Jong, S. (1993). SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18(3), 251–263.

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.

Shenk, J. S., & Westerhaus, M. O. (1991). Population structuring of near infrared spectra and modified partial least squares regression. Crop Science, 31(6), 1548–1555.

See Also

fit_pls and fit_gpr for specifying the fitting method; model_control for controlling aspects of the modelling process; mbl for memory-based (local) learning.

Examples

library(prospectr)
data(NIRsoil)

# Preprocess spectra
Xr <- savitzkyGolay(NIRsoil$spc, m = 1, p = 2, w = 11)
Yr <- NIRsoil$CEC

# Remove missing values
ok <- !is.na(Yr)
Xr <- Xr[ok, ]
Yr <- as.matrix(Yr[ok])

# Fit a PLS model with 10 components and cross-validation
# Scaling is controlled via fit_pls()
pls_mod <- model(
  Xr = Xr,
  Yr = Yr,
  fit_method = fit_pls(ncomp = 10, scale = FALSE),
  control = model_control(validation_type = "lgo", number = 10)
)

# View cross-validation results
pls_mod$cv_results

# Fit a GPR model (centring/scaling controlled via fit_gpr())
gpr_mod <- model(
  Xr = Xr,
  Yr = Yr,
  fit_method = fit_gpr(noise_variance = 0.001, scale = TRUE),
  control = model_control(validation_type = "lgo")
)

resemble documentation built on April 21, 2026, 1:07 a.m.