calibrate: Calibrate a spectral model

In proximetricsR: Spectral Preprocessing and Chemometric Calibration of NIR Sensors

Calibrate a spectral model

Description

\loadmathjax

Produce calibrations for predictive partial least squares (pls) or extended partial least squares (xls) models using cross-validation and outlier detection. Reproduces the modeling methods in NIRWise PLUS calibration software.

Usage

## S3 method for class 'formula'
calibrate(formula, data, group = NULL,
          preprocess = preprocess_recipe(prep_snv()),
          method,
          metadata = NULL,
          return_inputs = TRUE,
          ...,
          na_action = na.pass)

## Default S3 method:
calibrate(X, Y, data = NULL, group = NULL,
          preprocess = preprocess_recipe(prep_snv()),
          method = fit_plsr(ncomp = min(15, dim(X))),
          control = calibration_control(),
          metadata = NULL,
          skip_indices = NULL,
          return_inputs = TRUE,
          verbose = TRUE,
          ...)

## S3 method for class 'spectral_model'
predict(object, 
         newdata, 
         ncomp = object$final_ncomp, 
         verbose = TRUE, 
         control_limit_conf = 0.99, 
         ...)

Arguments

...	not currently used.
formula	an object of class formula which represents the basic model to be calibrated.
data	a data.frame containing the data of the variables in the model. Must be provided if using S3 method for class formula. Otherwise, optional; however, if using proximate_write_nax for the returned object, this parameter will be required.
X	a numeric matrix of spectral data. The names of the columns must be equivalent to wavelengths, such that they can be coerced to class numeric.
Y	a matrix of one column with the response variable. The column must be named.
group	an optional factor (or character vector that can be coerced to factor by as.factor) that assigns a group/class label to each observation in X (e.g. groups can be given by spectra collected from the same batch of measurements, from the same observation, from observations with very similar origin, etc). This is taken into account for cross-validation for pls tuning (factor optimization) to avoid pseudo- replication. When one observation is selected for cross-validation, all observations of the same group are removed and assigned to validation. The length of the vector must be equal to the number of observations in X.
preprocess	a preprocess_recipe object as returned by the preprocess_recipe function, indicating the pretreatments to be applied on the spectra before the regression steps.
method	an object of class fit_constructor, as returned by fit_plsr or fit_xlsr, indicating what type of regression method to use along with its parameters.
control	a calibration_control object as returned by the calibration_control function, indicating how some aspects of the calibration process must be conducted (e.g. cross-validation and outlier detection).
metadata	either NULL or an object as returned by method add_model_metadata. Contains the specifications for the metadata of the model. Defaults to NULL.
skip_indices	a vector of integers for the indices in the input data to be skipped for the regression. Defaults to NULL
return_inputs	a logical. For calibrate methods, indicates if the input data should be attached to the returned object. Note that this data is crucial for creating an application file.
verbose	a logical indicating whether or not to print a progress bar for the iterations of the validation along with messages of the execution of the cross-validation. For the predict method, messages about the progress are printed. Default is TRUE. Note: In case parallel processing is used, these progress bars are not printed.
object	an object of class spectral_model.
newdata	a data.frame containing the new spectral data of the variables in the model, of similar form as data. Alternatively, can also be a matrix of spectra.
ncomp	a vector for the number of components to be used in the prediction. Default is object$final_ncomp i.e. the optimized number of components found in the object passed to predict.
control_limit_conf	the confidence level for the leverage and spectral residual (Q) control limits returned with the prediction and used by the validation plot. A single number strictly between 0 and 1. Default is 0.99.
na_action	a function to specify the action to be taken if NAs are found in the object passed in data. Default is na.pass.

Details

The resulting object of the calibrate functions provides a complete list of calibration results.

By using the group argument one can specify groups of observations that have something in common (e.g. observations with very similar origin). The purpose of group is to avoid biased cross-validation results due to pseudo-replication. This argument allows to select calibration points that are independent from the validation ones. In this regard, the p argument used in object passed to control (and created with the calibration_control function), refers to the percentage of groups of observations (rather than single observations) to be retained in each sampling iteration.

The regression algorithms implemented here correspond to the partial least squares ("pls") and extended partial least squares ("xls") methods in NIRWise PLUS calibration software. Note that in these particular regression algorithms, the Y-loading of each component is constantly equal to 1, and therefore not considered.

The calibration_statistics matrix retrieved in the final_model and also in the initial_fit outputs includes a column named Q_value. This value can be used to asses model overfitting. For each observation, \mjeqnq_iq_i is computed as follows:

\mjdeqn

s = \sqrt \frac\sum_i=1^n (y_j - \haty_j)^2 n - 1s = sqrt(sum(y_i - haty_i)^2/(n - 1))

\mjdeqn

q_i = \frac\left |2 y_i - \haty_i - \ddoty_i \right | sq_i = abs(2 y_i - haty_i - ddoty_i)/s

where for ith observation, \mjeqnyy is the observed value, \mjeqn\hatyhaty is the fitted value (using a model with all the observations) and \mjeqn\ddotyddoty is the predicted value during cross-validation.

Value

For calibrate(), an object of class spectral_model which is a list with the following elements:

• formula: The formula used (only output if the S3 method for class 'formula' was used).

• dataclasses: The data classes in the model (only output if the S3 method for class 'formula' was used).

• target_variable: A character for the name of the target/response variable for which the predictive model was built.

• predictor_variables: A character vector for names of the predictor variables (wavelengths) used to build the model.

• final_model: A list with:

  • model_cv: A list of cross-validation results.

  • ncomp: The number of components used for the model. If cross-validation is used, this is the optimal number of components for the chosen tuning parameter and learning rates (see calibration_control).

  • model: An object of class spectral_fit. See spectral_fit for the full structure.

  • calibration_statistics: A matrix showing the prediction statistics for each calibration sample for the optimal number of components used in the model (if cross-validation is used, see calibration_control). It contains the following columns:

    • Sample_index: The indices of the samples.

    • Target: The target/response variable of the samples.

    • fitted_y: The fitted values of the model of each sample. This row is equivalent to the row of the optimal component of fitted_y inside the fitted model in model.

    • residual: The residuals of the fitted values of each sample. Note that the residuals are obtained as the difference of targets and fitted values.

    • predicted_y_in_cv: The predicted values as computed in the cross-validation. Only available for k-fold and leave-one-out cross-validation.

    • cv_residual: The residuals of the predicted values of the cross-validation. Only available for k-fold and leave-one-out cross-validation.

    • Mahalanobis: The squared Mahalanobis distance of each sample in the score space to the origin.

    • Q_value: The Q-value of each sample. See details

  • calibration_statistics_all: A list of matrices with the same information as in calibration_statistics, but for all components.

  • detected_outliers_all: A list of lists, each containing the same information as in the detected_outliers$model_* mentioned below, but for all components in the fitted model.

• detected_outliers: A named list, containing the following entries:

  • model_*: A named list, containing all detected outliers of the particular model, identified based on the calibration residual limit ("calibration"), the Mahalanobis distance limit ("Mahalanobis"), and the validation residual limit ("validation"). The number of such model_* entries depends on the number selected in remove_outliers of the control argument; if it is selected to be 0, then only one model is fitted, so only model_1 exists; for higher choices of remove_outliers, the number of models of this list is at most remove_outliers + 1: for every time a model is fitted, a new entry in the detected_outliers is generated.

  • all: A named list, containing all detected outliers of all models produced, similarly to model_*. In particular, this entry is the combination of all detected outliers in the model_* entries of the list, where the specific type of outlier is retained.

  • removed: A single vector, containing all removed outliers of the final model. This vector is empty whenever the remove_outliers of the control argument is set to 0 or if no outlier has been found. Otherwise, this vector is a combination of all different outliers that were removed whenever a new model has been fitted, while ignoring the specific type of the outlier. In particular, in case the last model still contains at least one outlier, this vector is a combination of all but the last entry of the model_* lists. If the last fitted model does not contain any outlier, this vector is a combination of all model_* lists, and hence the vectorized form of the all entry of the list.

  See calibration_control for more information on the limits and the outlier removal procedure.

• initial_fit: A list similar to final_model, but before any outliers were removed. Only stored if outlier removal is requested (i.e. remove_outliers in the control argument is larger than 0). In that case, the model here contains only the very first model that was fitted without any detected outliers removed.

• final_ncomp: An integer, indicating the final/optimal number of components to be used.

• preprocess: A preprocess_recipe object mirroring the input of the preprocess argument.

• processed_wavs: A processed_wavs object providing the spectral variables that existed in the data right before each preprocessing step.

• method: A fit_constructor object mirroring the input of the method argument.

• control: A calibration_control object mirroring the input of the control argument.

• preprocessed_X: The preprocessed spectral data for the observations of the final model. Spectra with missing values, skipped indices and removed outliers are discarded from the matrix.

• skipped_indices: A list with two objects:

  • missing_response: A vector of indices of observations with missing response values.

  • manually_skipped: A vector of indices mirroring the input of the skip_indices argument.

• input_data: A list, which is only returned if return_inputs is set to TRUE. Mirrors the input of the data argument.

For predict(), the output is an object of class spectral_prediction, which is a list with the following elements:

• predictions: A matrix with the predictions of the response variable using the new spectral data (newdata), based on the provided model (object). Contains only the predictions of the requested number of components (ncomp).

• scores: A matrix with the projected new data onto the score space of the provided model. Contains the scores of all possible number of components.

• q_limit: A named vector (one value per requested component) with the upper control limit for the spectral residual Q, taken as the calibration quantile at control_limit_conf.

• leverage_limit: A named vector (one value per requested component) with the upper control limit for the leverage (Mahalanobis distance) of a new observation at control_limit_conf.

• control_limit_conf: The confidence level of q_limit and leverage_limit.

• model_information: A list, containing information on the model input of object:

  • target_var: A character, indicating the name of the target variable.

  • preprocess_recipe: A character, indicating the spectral preprocessing recipe and its order.

  • model_grid: A matrix, containing the grid of the model object, such as the coefficient of determination and the RMSE of the validation for the requested number of components.

  • unit: A character, indicating the units of the model.

  • opt_comp: An integer, signifying the optimal number of components as computed by the validation process of the model.

Parallel cross-validation

The cross-validation loop is implemented with foreach, so it can be parallelised transparently by registering a parallel backend before calling calibrate. Set allow_parallel = TRUE in calibration_control (the default) and register a backend, for example:

cl <- parallel::makeCluster(parallel::detectCores() - 1L)
doParallel::registerDoParallel(cl)

model <- calibrate(...)

parallel::stopCluster(cl)

When no parallel backend is registered, foreach falls back silently to sequential execution regardless of the allow_parallel setting. Note that progress bars are suppressed during parallel execution.

Author(s)

Leonardo Ramirez-Lopez and Claudio Orellano

See Also

preprocess_recipe,

fit_plsr,

fit_xlsr,

calibration_control,

calibrate_models

Examples

data("proximateCannabis")
simple_model <- calibrate(CBDA ~ spc,
  data = proximateCannabis, preprocess = preprocess_recipe(prep_snv()),
  method = fit_xlsr(5), control = calibration_control("kfold"),
  verbose = FALSE
)

method <- fit_plsr(15)
control <- calibration_control(validation_type = "kfold", number = 3, folds = "sequential")
pretreats <- preprocess_recipe(
  prep_resample(grid = c(1001, 1700, 5)),
  prep_derivative(m = 2, w = 9, p = 5, algorithm = "nwp"),
  prep_snv(),
  prep_smooth(w = 5, algorithm = "moving-average"),
  device = "proximate"
)
skip_indices <- c(5, 13, 21, 73)
# With formula
complex_model_formula <- calibrate(
  CBDA ~ spc,
  data = proximateCannabis, preprocess = pretreats, method = method,
  control = control, skip_indices = skip_indices, verbose = FALSE
)
# Default, need care with Y
Y <- matrix(proximateCannabis$CBDA)
colnames(Y) <- "CBDA"
complex_model_default <- calibrate(
  X = proximateCannabis$spc, Y = Y, data = proximateCannabis, preprocess = pretreats,
  method = method, control = control, skip_indices = skip_indices, verbose = FALSE
)

# Predict the skipped indices
predict(complex_model_formula,
  newdata = proximateCannabis[skip_indices, ],
  ncomp = complex_model_formula$final_ncomp,
  verbose = FALSE
)

proximetricsR documentation built on Sept. 4, 2026, 5:08 p.m.