predict.PartialLeastSquaresModel: Predict Partial Least Squares model

In brandon-mosqueda/SKM: Sparse Kernels Methods

Predict Partial Least Squares model

Description

Obtains the predictions using a fitted model object of class PartialLeastSquaresModel.

Usage

## S3 method for class 'PartialLeastSquaresModel'
predict(model, x, components_num = NULL, format = "list")

Arguments

model	(Model) An object of a fitted model.
x	(matrix) The predictor variables to be used to compute the predictions. It has to be in the same format provided when fitting the model.
components_num	(numeric(1)) A numeric value with the number of components to use to compute the predictions. NULL by default which uses the optimal number of components determined with cross validation when the model was fitted.
format	(character(1)) The expected format of the predictions. The available options are "list" and "data.frame". "data.frame" is more useful with multivariate models if you want in a tabular structure the predicted values. See Value section below for more information. "list" by default.

Value

When format is "list"

For univariate models a named list with the element "predicted" which contains the predicted values is returned.

For multivariate models a named list is returned where there is an named element for each response variable in the fitted model. Each element of this list contains a inner list in the same format as described for the univariate case.

When format is "data.frame"

For univariate models a data.frame with the column predicted which contains the predicted values.

For multivariate models a data.frame with a column for each response variable with the predicted values of each response.

Examples

## Not run: 
# Use all default hyperparameters -------------------------------------------
x <- to_matrix(iris[, -1])
y <- iris$Sepal.Length
model <- partial_least_squares(x, y)

# Obtain the optimal number of components to use with predict
model$optimal_components_num

# Predict using the fitted model
predictions <- predict(model, x)
# Obtain the predicted values
predictions$predicted

# Predict with a non optimal number of components ---------------------------
x <- to_matrix(iris[, -1])
y <- iris$Sepal.Length
model <- partial_least_squares(x, y, method = "orthogonal")

# Obtain the optimal number of components to use with predict
model$optimal_components_num

# Predict using the fitted model with the optimal number of components
predictions <- predict(model, x)
# Obtain the predicted values
predictions$predicted

# Predict using the fitted model without the optimal number of components
predictions <- predict(model, x, components_num = 2)
# Obtain the predicted values
predictions$predicted

# Multivariate analysis -----------------------------------------------------
x <- to_matrix(iris[, -c(1, 2)])
y <- iris[, c(1, 2)]
model <- partial_least_squares(x, y, method = "wide_kernel")

# Predict using the fitted model
predictions <- predict(model, x)
# Obtain the predicted values of the first response variable
predictions$Sepal.Length$predicted
# Obtain the predicted values of the second response variable
predictions$Sepal.Width$predicted

# Obtain the predictions in a data.frame not in a list
predictions <- predict(model, x, format = "data.frame")
head(predictions)

## End(Not run)

brandon-mosqueda/SKM documentation built on Feb. 8, 2025, 5:24 p.m.