MRFA (version 0.6)

predict.MRFA: Prediction of Multi-Resolution Functional ANOVA (MRFA) Model

Description

The function computes the predicted responses.

Usage

# S3 method for MRFA
predict(object, xnew, lambda = object$lambda, parallel = FALSE, ...)

Value

lambda

as above.

coefficients

coefficients with respect to the basis function value.

y_hat

a matrix with dimension n_new by length(lambda) displaying predicted responses at locations xnew.

Arguments

object

a class MRFA object estimated by MRFA_fit.

xnew

a testing matrix with dimension n_new by d in which each row corresponds to a predictive location.

lambda

a value, or vector of values, indexing the path. The default is object$lambda.

parallel

logical. If TRUE, apply function in parallel in ldply using parallel backend provided by foreach.

...

for compatibility with generic method predict.

Author

Chih-Li Sung <[e-mail]>

See Also

MRFA_fit for fitting a multiresolution functional ANOVA model.

Examples

if (FALSE) {

#####             Testing function: OTL circuit function                     #####
#####  Thanks to Sonja Surjanovic and Derek Bingham, Simon Fraser University #####
otlcircuit <- function(xx)
{
  Rb1  <- 50   + xx[1] * 100
  Rb2  <- 25   + xx[2] * 45
  Rf   <- 0.5  + xx[3] * 2.5
  Rc1  <- 1.2  + xx[4] * 1.3
  Rc2  <- 0.25 + xx[5] * 0.95
  beta <- 50   + xx[6] * 250

  Vb1 <- 12*Rb2 / (Rb1+Rb2)
  term1a <- (Vb1+0.74) * beta * (Rc2+9)
  term1b <- beta*(Rc2+9) + Rf
  term1 <- term1a / term1b

  term2a <- 11.35 * Rf
  term2b <- beta*(Rc2+9) + Rf
  term2 <- term2a / term2b

  term3a <- 0.74 * Rf * beta * (Rc2+9)
  term3b <- (beta*(Rc2+9)+Rf) * Rc1
  term3 <- term3a / term3b

  Vm <- term1 + term2 + term3
  return(Vm)
}

library(MRFA)
#####   Training data and testing data   #####
set.seed(2)
n <- 1000; n_new <- 100; d <- 6
X.train <- matrix(runif(d*n), ncol = d)
Y.train <- apply(X.train, 1, otlcircuit)
X.test <- matrix(runif(d*n_new), ncol = d)
Y.test <- apply(X.test, 1, otlcircuit)

#####   Fitting    #####
MRFA_model <- MRFA_fit(X.train, Y.train, verbose = TRUE)

#####   Prediction   ######
Y.pred <- predict(MRFA_model, X.test, lambda = min(MRFA_model$lambda))$y_hat
print(sqrt(mean((Y.test - Y.pred)^2)))
}