predict.cv.RMSS {RMSS}

Predictions for cv.RMSS Object

Description

predict.cv.RMSS returns the predictions for a cv.RMSS object.

Usage

## S3 method for class 'cv.RMSS'
predict(
  object,
  newx,
  h_ind = NULL,
  t_ind = NULL,
  u_ind = NULL,
  group_index = NULL,
  dynamic = FALSE,
  ...
)

Arguments

object

An object of class cv.RMSS.

newx

New data for predictions.

h_ind

Index for robustness parameter.

t_ind

Index for sparsity parameter.

u_ind

Index for diversity parameter.

group_index

Groups included in the ensemble. Default setting includes all the groups.

dynamic

Argument to determine whether dynamic predictions are used based on deviating cells. Default is FALSE.

...

Additional arguments for compatibility.

Value

The predictions for the cv.RMSS object.

Author(s)

Anthony-Alexander Christidis, [e-mail]

See Also

cv.RMSS

Examples

# Simulation parameters
n <- 50
p <- 100
rho <- 0.8
rho.inactive <- 0.2
group.size <- 5
p.active <- 15
snr <- 2
contamination.prop <- 0.3

# Setting the seed
set.seed(0)

# Block Correlation
sigma.mat <- matrix(0, p, p)
sigma.mat[1:p.active, 1:p.active] <- rho.inactive
for(group in 0:(p.active/group.size - 1))
  sigma.mat[(group*group.size+1):(group*group.size+group.size),
            (group*group.size+1):(group*group.size+group.size)] <- rho
diag(sigma.mat) <- 1

# Simulation of beta vector
true.beta <- c(runif(p.active, 0, 5)*(-1)^rbinom(p.active, 1, 0.7), 
               rep(0, p - p.active))

# Setting the SD of the variance
sigma <- as.numeric(sqrt(t(true.beta) %*% sigma.mat %*% true.beta)/sqrt(snr))

# Simulation of test data
m <- 2e3
x_test <- mvnfast::rmvn(m, mu = rep(0, p), sigma = sigma.mat)
y_test <- x_test %*% true.beta + rnorm(m, 0, sigma)

# Simulation of uncontaminated data 
x <- mvnfast::rmvn(n, mu = rep(0, p), sigma = sigma.mat)
y <- x %*% true.beta + rnorm(n, 0, sigma)

# Contamination of data 
contamination_indices <- 1:floor(n*contamination.prop)
k_lev <- 2
k_slo <- 100
x_train <- x
y_train <- y
beta_cont <- true.beta
beta_cont[true.beta!=0] <- beta_cont[true.beta!=0]*(1 + k_slo)
beta_cont[true.beta==0] <- k_slo*max(abs(true.beta))
for(cont_id in contamination_indices){
 
 a <- runif(p, min = -1, max = 1)
 a <- a - as.numeric((1/p)*t(a) %*% rep(1, p))
  x_train[cont_id,] <- mvnfast::rmvn(1, rep(0, p), 0.1^2*diag(p)) + k_lev * a / 
                        as.numeric(sqrt(t(a) %*% solve(sigma.mat) %*% a))
  y_train[cont_id] <- t(x_train[cont_id,]) %*% beta_cont
}

# CV RMSS
rmss_fit <- cv.RMSS(x = x_train, y = y_train,
                    n_models = 3,
                    h_grid = c(35), t_grid = c(6, 8, 10), u_grid = c(1:3),
                    initial_estimator = "robStepSplitReg",
                    tolerance = 1e-1,
                    max_iter = 1e3,
                    neighborhood_search = FALSE,
                    neighborhood_search_tolerance = 1e-1,
                    n_folds = 5,
                    alpha = 1/4,
                    gamma = 1, 
                    n_threads = 1)
rmss_coefs <- coef(rmss_fit, 
                   h_ind = rmss_fit$h_opt, 
                   t_ind = rmss_fit$t_opt, 
                   u_ind = rmss_fit$u_opt,
                   group_index = 1:rmss_fit$n_models)
sens_rmss <- sum(which((rmss_coefs[-1]!=0)) <= p.active)/p.active
spec_rmss <- sum(which((rmss_coefs[-1]!=0)) <= p.active)/sum(rmss_coefs[-1]!=0)
rmss_preds <- predict(rmss_fit, newx = x_test,
                      h_ind = rmss_fit$h_opt, 
                      t_ind = rmss_fit$t_opt, 
                      u_ind = rmss_fit$u_opt,
                      group_index = 1:rmss_fit$n_models,
                      dynamic = FALSE)
rmss_mspe <- mean((y_test - rmss_preds)^2)/sigma^2