test_boot: Conducting Score Tests for Interaction Using Bootstrap Test
In CVEK: Cross-Validated Kernel Ensemble

Description Usage Arguments Details Value Author(s) References See Also

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

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

`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".

Bootstrap Test

When it comes to small sample size, we can use bootstrap test instead, which can give valid tests with moderate sample sizes and requires similar computational effort to a permutation test.

pvalue

(numeric) p-value of the test.

Wenying Deng

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.

method: generate_kernel

mode: tuning

strategy: ensemble

CVEK documentation built on Jan. 8, 2021, 5:42 p.m.