predict.fosr.vs: Prediction for Function-on Scalar Regression with variable...

In refund: Regression with Functional Data

Prediction for Function-on Scalar Regression with variable selection

Description

Given a "fosr.vs" object and new data, produces fitted values.

Usage

## S3 method for class 'fosr.vs'
predict(object, newdata = NULL, ...)

Arguments

object	an object of class "fosr.vs".
newdata	a data frame that contains the values of the model covariates at which predictors are required.
...	additional arguments.

Value

fitted values.

Author(s)

Yakuan Chen [e-mail]

See Also

fosr.vs

Examples

## Not run: 
I = 100
p = 20
D = 50
grid = seq(0, 1, length = D)

beta.true = matrix(0, p, D)
beta.true[1,] = sin(2*grid*pi)
beta.true[2,] = cos(2*grid*pi)
beta.true[3,] = 2

psi.true = matrix(NA, 2, D)
psi.true[1,] = sin(4*grid*pi)
psi.true[2,] = cos(4*grid*pi)
lambda = c(3,1)

set.seed(100)

X = matrix(rnorm(I*p), I, p)
C = cbind(rnorm(I, mean = 0, sd = lambda[1]), rnorm(I, mean = 0, sd = lambda[2]))

fixef = X%*%beta.true
pcaef = C %*% psi.true
error = matrix(rnorm(I*D), I, D)

Yi.true = fixef
Yi.pca = fixef + pcaef
Yi.obs = fixef + pcaef + error

data = as.data.frame(X)
data$Y = Yi.obs
fit.mcp = fosr.vs(Y~., data = data[1:80,], method="grMCP")
predicted.value = predict(fit.mcp, data[81:100,])


## End(Not run)

refund documentation built on Nov. 14, 2023, 5:07 p.m.