predict_Ohit: Make predictions based on a fitted "Ohit" object

In Ohit: OGA+HDIC+Trim and High-Dimensional Linear Regression Models

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, ])

Example output

$pred_HDIC
[1] 16.18587

$pred_Trim
[1] 16.16235

$pred_HDIC
 [1]  16.1858664  -3.2580596 -21.3708495 -17.3621501  -9.2934443  -1.6120540
 [7]  18.5471672 -11.6529395 -33.3141578   0.6153522

$pred_Trim
 [1]  16.1623475  -3.2242840 -21.4416871 -17.4033878  -9.2498644  -1.6231749
 [7]  18.5459885 -11.5762733 -33.2060854   0.5952212

$pred_HDIC
[1] 16.18521

$pred_Trim
[1] 16.16174

$pred_HDIC
 [1]  16.1852111  -3.2588039 -21.3717391 -17.3630388  -9.2941446  -1.6126408
 [7]  18.5461139 -11.6535413 -33.3148442   0.6146596

$pred_Trim
 [1]  16.1617442  -3.2249725 -21.4424906 -17.4041887  -9.2504693  -1.6236990
 [7]  18.5450519 -11.5768345 -33.2066904   0.5945924

Ohit documentation built on May 1, 2019, 8:43 p.m.