predict.BayesianModel: Predict Bayesian model

In brandon-mosqueda/SKM: Sparse Kernels Methods

Predict Bayesian model

Description

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

Usage

## S3 method for class 'BayesianModel'
predict(model, indices, format = "list")

Arguments

model	(Model) An object of a fitted model.
indices	(numeric) A numeric vector with the indices of the elements used to fit the model you want the predictions.
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 categorical variables the returned list includes the element "probabilities" too with a data.frame of the predicted probabilities of each class.

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, so for categorical variables, a data.frame with the predicted probabilities is included too.

When format is "data.frame"

For univariate models a data.frame with the column predicted which contains the predicted values. For categorical variables, a column for each class with the predicted probability of this class is included too.

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

Examples

## Not run: 
# Univariate analysis -------------------------------------------------------
x <- list(list(x = to_matrix(iris[, -5]), model = "BRR"))
y <- iris$Species
model <- bayesian_model(x, y, testing_indices = c(1:5, 51:55, 101:105))

# Predict using the fitted model (of the specified testing indices)
predictions <- predict(model, indices = c(1:5, 51:55, 101:105))
# Obtain the predicted values
predictions$predicted
# Obtain the predicted probabilities
predictions$probabilities

# Predict using the fitted model (with different indices)
predictions <- predict(model, indices = 1:50)
predictions$predicted

# Multivariate analysis -----------------------------------------------------
x <- list(list(x = to_matrix(iris[, -c(1, 2)]), model = "fixed"))
y <- iris[, c(1, 2)]
y[c(5, 55, 105), 1] <- NA
y[c(6, 66, 106), 2] <- NA
model <- bayesian_model(x, y, iterations_number = 1000)

# Predict using the fitted model, with different indices and data.frame
# format
predictions <- predict(model, indices = c(10, 20, 30), format = "data.frame")
head(predictions)

## End(Not run)

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