Calibrate independent models (iteratively) for multiple properties with
optimization of both the pre-processing recipe (based on a list of different
recipes) and the regression method. This function uses
calibrate to construct such list of models.
Usage
calibrate_models(formulas,
data, group = NULL,
preprocess_recipes,
methods,
control = calibration_control(seed = 1),
metadata_list = NULL,
skip_indices_list = NULL,
return_inputs = TRUE,
...,
na_action = na.pass,
verbose = TRUE,
save_all = FALSE)
# S3 method for class 'spectral_multimodel'
predict(object, newdata, verbose = TRUE, ...)Arguments
- formulas
a list containing one or more objects of class
formulawhere each of them represents the model to be calibrated.- data
a data.frame containing the data of the variables in the model (as in the
calibratefunction).- group
an optional factor (or character vector that can be coerced to
factorbyas.factor) that assigns a group/class label to each observation inX(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 inX.- preprocess_recipes
a list with one or more objects of class
preprocess_recipethat are to be tested for finding the optimal one for each model in the list passed toformulas.- methods
a list containing one or more objects of class
fit_constructoras returned byfit_plsrorfit_xlsr, indicating what type of regression method to use along with its parameters.- control
a
calibration_controlobject as returned by thecalibration_controlfunction, indicating how some aspects of the calibration process must be conducted (e.g. cross-validation and outlier detection). Default iscalibration_control(seed = 1). See details.- metadata_list
a list containing the specifications for the metadata of each model in
formulasgiven in the same order. Each element in the list should be defined as in themetadataargument ofcalibrateusing theadd_model_metadatafunction. Defaults toNULL.- skip_indices_list
a list of vectors of integers for the indices in the input data to be skipped for the computation of each of the models in
formulas. The vectors in this list must be provided in the same order as their corresponding counterparts informulas. Defaults toNULL. In case a list is passed, the list components must be filled withnumeric()for thoseformulaswhere there is no indices to be skipped.- return_inputs
a logical. For
calibratemethods, indicates if the input data should be attached to the returned object. Note that this data is crucial for creating an application file.- ...
arguments to be passed to the
calibratemethod. Not currently used for thepredict.spectral_multimodelmethod.- na_action
a function to specify the action to be taken if
NAs are found in the object passed indata. Default isna.pass.- 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.- save_all
a logical indicating if all the models tested (with the different pre-processing recipes) are to be saved. Default is
FALSE.- object
an object of class
spectral_multimodel.- 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.
Value
A list of class "spectral_multimodel" containing the following
objects:
results_grid: a data.frame with the validation results of the best models found for each pre-processing recipe with the best regression method applied on the spectral data of the model built for each formula.all_models: ifsave_all, a list with thespectral_modelobjects corresponding to all the models tested.final_models: a list containing only thespectral_modelobjects corresponding to the best models found for each formula. This list can be used/passed later to theproximate_write_naxfunction to produce an application file (in that case it might be convenient to add some metadata to the resulting models in the list using theadd_model_metadatafunction).
For predict(), 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 models (object). Contains only the predictions of the optimal number of components (ncomp).model_information: A list, containing information on the models inputs inobject. Each component in the list contains the following information: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.
Details
The object passed to the control argument should indicate a seed
for the random number generator (RNG). This allows the function to use
the same cross-validation validation groups (for leave group-out
cross-validation, see calibration_control) across the same
formula with different recipes. This enables proper model comparisons.
Parallel cross-validation
The cross-validation loop inside each call to calibrate is
implemented with foreach, so it can be parallelised
transparently by registering a parallel backend before calling
calibrate_models. Set allow_parallel = TRUE in
calibration_control (the default) and register a backend, for
example:
cl <- parallel::makeCluster(parallel::detectCores() - 1L)
doParallel::registerDoParallel(cl)
result <- calibrate_models(...)
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.
Examples
# \donttest{
data("NIRcannabis")
# the list of formulas for the models to be built
app_formulas <- list(THC ~ spc, THCA ~ spc, CBD ~ spc, CBDA ~ spc)
# the list of pre-processing recipes to be tested
precipes <- list(
recipe_1 = preprocess_recipe(
prep_resample(grid = c(1001, 1700, 2)),
prep_snv(),
prep_derivative(m = 1, w = 9, p = 7, algorithm = "nwp"),
device = "proximate"
),
recipe_2 = preprocess_recipe(
prep_resample(grid = c(1001, 1700, 2)),
prep_snv(),
prep_derivative(m = 2, w = 11, p = 9, algorithm = "nwp"),
device = "proximate"
)
)
optimized_app <- calibrate_models(
formulas = app_formulas,
data = NIRcannabis,
preprocess_recipes = precipes,
methods = list(fit_plsr(15, type = "nwp")),
return_inputs = TRUE,
save_all = FALSE
)
#> --- Finding model for THC ~ spc ----
#> + testing preprocessing recipe index 1
#> + testing preprocessing recipe index 2
#> --- Finding model for THCA ~ spc ----
#> + testing preprocessing recipe index 1
#> + testing preprocessing recipe index 2
#> --- Finding model for CBD ~ spc ----
#> + testing preprocessing recipe index 1
#> + testing preprocessing recipe index 2
#> --- Finding model for CBDA ~ spc ----
#> + testing preprocessing recipe index 1
#> + testing preprocessing recipe index 2
#> --- Fitting final model for THC ~ spc ----
#> Fitting model...
#> Cross-validating...
#>
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#> --- Fitting final model for THCA ~ spc ----
#> Fitting model...
#> Cross-validating...
#>
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#> --- Fitting final model for CBD ~ spc ----
#> Fitting model...
#> Cross-validating...
#>
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#> --- Fitting final model for CBDA ~ spc ----
#> Fitting model...
#> Cross-validating...
#>
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optimized_app
#> Grid search results:
#> formula recipe min property max property ncomp rsq rmse
#> 1 THC ~ spc 1 0.0136 5.44 2 0.532 0.679
#> 2 * THC ~ spc 2 0.0136 5.44 3 0.690 0.578
#> 3 THCA ~ spc 1 0.0200 12.13 7 0.780 1.539
#> 4 * THCA ~ spc 2 0.0200 12.13 6 0.820 1.407
#> 5 CBD ~ spc 1 0.0262 5.89 1 0.148 0.675
#> 6 * CBD ~ spc 2 0.0262 5.89 1 0.175 0.668
#> 7 CBDA ~ spc 1 0.0100 24.64 4 0.684 3.454
#> 8 * CBDA ~ spc 2 0.0100 24.64 2 0.682 3.431
#> largest_residual rsq_sd rmse_sd largest_residual_sd outliers method
#> 1 1.87 0.1112 0.171 0.978 0 PLS (nwp)
#> 2 1.67 0.1025 0.191 1.047 0 PLS (nwp)
#> 3 4.27 0.1033 0.366 1.323 0 PLS (nwp)
#> 4 3.63 0.0726 0.265 0.944 0 PLS (nwp)
#> 5 2.17 0.0849 0.301 1.599 0 PLS (nwp)
#> 6 2.18 0.0734 0.300 1.573 0 PLS (nwp)
#> 7 11.37 0.2049 1.312 6.147 0 PLS (nwp)
#> 8 11.66 0.2310 1.477 6.749 0 PLS (nwp)
#>
#> *best model
#> ---
#> Suggested models:
#> Model: THC ~ spc
#> Spectral preprocessing recipe (device: "proximate"):
#> - Step 1: prep_resample
#> min_wav: 1001; max_wav: 1700; resolution: 2
#> - Step 2: prep_snv
#> - Step 3: prep_derivative
#> m: 2; w: 11; p: 9; algorithm: 'nwp'
#> Method: PLS (nwp)
#>
#> Model: THCA ~ spc
#> Spectral preprocessing recipe (device: "proximate"):
#> - Step 1: prep_resample
#> min_wav: 1001; max_wav: 1700; resolution: 2
#> - Step 2: prep_snv
#> - Step 3: prep_derivative
#> m: 2; w: 11; p: 9; algorithm: 'nwp'
#> Method: PLS (nwp)
#>
#> Model: CBD ~ spc
#> Spectral preprocessing recipe (device: "proximate"):
#> - Step 1: prep_resample
#> min_wav: 1001; max_wav: 1700; resolution: 2
#> - Step 2: prep_snv
#> - Step 3: prep_derivative
#> m: 2; w: 11; p: 9; algorithm: 'nwp'
#> Method: PLS (nwp)
#>
#> Model: CBDA ~ spc
#> Spectral preprocessing recipe (device: "proximate"):
#> - Step 1: prep_resample
#> min_wav: 1001; max_wav: 1700; resolution: 2
#> - Step 2: prep_snv
#> - Step 3: prep_derivative
#> m: 2; w: 11; p: 9; algorithm: 'nwp'
#> Method: PLS (nwp)
#>
# }