RandomForestsGLS (version 0.1.5)

RFGLS_predict: Prediction of mean function with RF-GLS

Description

The function RFGLS_predict predicts the mean function at a given set of covariates. It uses a fitted RF-GLS model in Saha et al. 2020 to obtain the predictions.

Some code blocks are borrowed from the R package: randomForest: Breiman and Cutler's Random Forests for Classification and Regression
https://CRAN.R-project.org/package=randomForest .

Usage

RFGLS_predict(RFGLS_out, Xtest, h = 1, verbose = FALSE)

Value

A list comprising:

predicted_matrix

an \(ntest \times ntree\) matrix of predictions. t-th column denote the predictions at \(ntest\) datapoints obtained from the t-th tree.

predicted

preducted values at the \(ntest\) prediction points. Average (rowMeans) of the treewise predctions in predicted_matrix

Arguments

RFGLS_out

an object obtained from RFGLS_estimate_spatial or
RFGLS_estimate_timeseries.

Xtest

an \(ntest \times p\) matrix of covariates for prediction. Its Structure should be identical (including intercept) with that of covariates provided for estimation purpose in X in RFGLS_out.

h

number of core to be used in parallel computing setup for bootstrap samples. If h = 1, there is no parallelization. Default value is 1.

verbose

if TRUE, model specifications along with information regarding OpenMP support and progress of the algorithm is printed to the screen. Otherwise, nothing is printed to the screen. Default value is FALSE.

Author

Arkajyoti Saha ,
Sumanta Basu ,
Abhirup Datta

References

Saha, A., Basu, S., & Datta, A. (2020). Random Forests for dependent data. arXiv preprint arXiv:2007.15421.

Andy Liaw, and Matthew Wiener (2015). randomForest: Breiman and Cutler's Random Forests for Classification and Regression. R package version 4.6-14.
https://CRAN.R-project.org/package=randomForest

Examples

rmvn <- function(n, mu = 0, V = matrix(1)){
  p <- length(mu)
  if(any(is.na(match(dim(V),p))))
    stop("Dimension not right!")
  D <- chol(V)
  t(matrix(rnorm(n*p), ncol=p)%*%D + rep(mu,rep(n,p)))
}

set.seed(2)
n <- 200
x <- as.matrix(rnorm(n),n,1)

sigma.sq <- 1
rho <- 0.5

set.seed(3)
b <- rho
s <- sqrt(sigma.sq)
eps = arima.sim(list(order = c(1,0,0), ar = b),
                n = n, rand.gen = rnorm, sd = s)

y <- eps + 10*sin(pi * x[,1])

estimation_result <- RFGLS_estimate_timeseries(y, x, ntree = 10)
Xtest <- matrix(seq(0,1, by = 1/1000), 1001, 1)
RFGLS_predict <- RFGLS_predict(estimation_result, Xtest)