sieve.sgd.predict: Sieve-SGD makes prediction with new predictors.

In Sieve: Nonparametric Estimation by the Method of Sieves

Sieve-SGD makes prediction with new predictors.

Description

Sieve-SGD makes prediction with new predictors.

Usage

sieve.sgd.predict(sieve.model, X)

Arguments

sieve.model	a list initiated using sieve.sgd.preprocess and sieve.sgd.solver. Check the documentation of sieve.sgd.preprocess for more information.
X	a data frame containing prediction features/ independent variables.

Value

sieve.sgd.predict will update the given sieve.model input list.

inf.list

In each entry of the list inf.list, the array prdy is the predicted outcome under the given hyperparameter combination.

Examples

frho.para <- xdim <- 1 ##predictor dimension
frho <- 'additive' ###truth is a sum of absolute functions 
type <- 'cosine' ###use cosine functions as the basis functions
#generate training data
TrainData <- GenSamples(s.size = 1e3, xdim = xdim, 
                                frho.para = frho.para, 
                                frho = frho, noise.para = 0.1)
#preprocess the model
sieve.model <- sieve.sgd.preprocess(X = TrainData[,2:(xdim+1)], 
                                    type = type,
                                    s = c(1,2),
                                    r0 = c(0.5, 2, 4),
                                    J = c(1, 4, 8))

##train the model
sieve.model <- sieve.sgd.solver(sieve.model = sieve.model, 
                                X = TrainData[,2:(xdim+1)], 
                                Y  = TrainData[,1])
##generate new data
NewData <- GenSamples(s.size = 5e2, xdim = xdim, 
                      frho.para = frho.para, 
                      frho = frho, noise.para = 0.1)
sieve.model <- sieve.sgd.predict(sieve.model, X = NewData[, 2:(xdim+1)])
plot(NewData[, 2:(xdim+1)], sieve.model$best_model$prdy)

Sieve documentation built on Oct. 19, 2023, 5:12 p.m.