test_asymp {CVEK}

Conducting Score Tests for Interaction Using Asymptotic Test

Description

Conduct score tests comparing a fitted model and a more general alternative model using asymptotic test.

Usage

test_asymp(Y, X, y_fixed, alpha0, K_ens, K_int, sigma2_hat, tau_hat, B)

Arguments

Y

(matrix, n*1) The vector of response variable.

X

(matrix, n*d_fix) The fixed effect matrix.

y_fixed

(vector of length n) Estimated fixed effect of the response.

alpha0

(vector of length n) Kernel effect estimator of the estimated ensemble kernel matrix.

K_ens

(matrix, n*n) Estimated ensemble kernel matrix.

K_int

(matrix, n*n) The kernel matrix to be tested.

sigma2_hat

(numeric) The estimated noise of the fixed effect.

tau_hat

(numeric) The estimated noise of the kernel effect.

B

(integer) A numeric value indicating times of resampling when test = "boot".

Details

Asymptotic Test

This is based on the classical variance component test to construct a testing procedure for the hypothesis about Gaussian process function.

Value

pvalue

(numeric) p-value of the test.

Author(s)

Wenying Deng

References

Xihong Lin. Variance component testing in generalised linear models with random effects. June 1997.

Arnab Maity and Xihong Lin. Powerful tests for detecting a gene effect in the presence of possible gene-gene interactions using garrote kernel machines. December 2011.

Petra Bu z kova, Thomas Lumley, and Kenneth Rice. Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions. January 2011.

See Also

method: generate_kernel

mode: tuning

strategy: ensemble