predict.orthoDr: Predictions under 'orthoDr' models

In orthoDr: Semi-Parametric Dimension Reduction Models Using Orthogonality Constrained Optimization

Predictions under orthoDr models

Description

The prediction function for orthoDr fitted models

Usage

## S3 method for class 'orthoDr'
predict(object, testx, ...)

Arguments

object	A fitted orthoDr object
testx	Testing data
...	Additional parameters, not used.

Value

The predicted object

Examples

# generate some survival data
N <- 100
P <- 4
dataX <- matrix(rnorm(N * P), N, P)
Y <- exp(-1 + dataX[, 1] + rnorm(N))
Censor <- rbinom(N, 1, 0.8)

# fit the model with keep.data = TRUE
orthoDr.fit <- orthoDr_surv(dataX, Y, Censor,
  ndr = 1,
  method = "dm", keep.data = TRUE
)

# predict 10 new observations
predict(orthoDr.fit, matrix(rnorm(10 * P), 10, P))

# generate some personalized dose scenario

exampleset <- function(size, ncov) {
  X <- matrix(runif(size * ncov, -1, 1), ncol = ncov)
  A <- runif(size, 0, 2)

  Edr <- as.matrix(c(0.5, -0.5))

  D_opt <- X %*% Edr + 1

  mu <- 2 + 0.5 * (X %*% Edr) - 7 * abs(D_opt - A)

  R <- rnorm(length(mu), mu, 1)

  R <- R - min(R)

  datainfo <- list(X = X, A = A, R = R, D_opt = D_opt, mu = mu)
  return(datainfo)
}

# generate data

set.seed(123)
n <- 150
p <- 2
ndr <- 1
train <- exampleset(n, p)
test <- exampleset(500, p)

# the direct learning method
orthofit <- orthoDr_pdose(train$X, train$A, train$R,
  ndr = ndr, lambda = 0.1,
  method = "direct", K = as.integer(sqrt(n)), keep.data = TRUE,
  maxitr = 150, verbose = FALSE, ncore = 2
)

predict(orthofit, test$X)

# the pseudo direct learning method
orthofit <- orthoDr_pdose(train$X, train$A, train$R,
  ndr = ndr, lambda = seq(0.1, 0.2, 0.01),
  method = "pseudo_direct", K = as.integer(sqrt(n)), keep.data = TRUE,
  maxitr = 150, verbose = FALSE, ncore = 2
)

predict(orthofit, test$X)

orthoDr documentation built on April 30, 2023, 5:12 p.m.