predict_Ohit {Ohit}

Make predictions based on a fitted "Ohit" object

Description

This function returns predictions from a fitted "Ohit" object.

Usage

predict_Ohit(object, newX)

Arguments

object

Fitted "Ohit" model object.

newX

Matrix of new values for X at which predictions are to be made.

Value

pred_HDIC

The predicted value based on the model determined by OGA+HDIC.

pred_Trim

The predicted value based on the model determined by OGA+HDIC+Trim.

Author(s)

Hai-Tang Chiou, Ching-Kang Ing and Tze Leung Lai.

References

Ing, C.-K. and Lai, T. L. (2011). A stepwise regression method and consistent model selection for high-dimensional sparse linear models. Statistica Sinica, 21, 1473–1513.

Examples

# Example setup (Example 3 in Section 5 of Ing and Lai (2011))
n = 410
p = 4000
q = 10
beta_1q = c(3, 3.75, 4.5, 5.25, 6, 6.75, 7.5, 8.25, 9, 9.75)
b = sqrt(3/(4 * q))

x_relevant = matrix(rnorm(n * q), n, q)
d = matrix(rnorm(n * (p - q), 0, 0.5), n, p - q)
x_relevant_sum = apply(x_relevant, 1, sum)
x_irrelevant = apply(d, 2, function(a) a + b * x_relevant_sum)
X = cbind(x_relevant, x_irrelevant)
epsilon = rnorm(n)
y = as.vector((x_relevant %*% beta_1q) + epsilon)

# with intercept
fit1 = Ohit(X[1:400, ], y[1:400])
predict_Ohit(fit1, rbind(X[401:401, ]))
predict_Ohit(fit1, X[401:410, ])
# without intercept
fit2 = Ohit(X[1:400, ], y[1:400], intercept = FALSE)
predict_Ohit(fit2, rbind(X[401:401, ]))
predict_Ohit(fit2, X[401:410, ])