Conduct score tests comparing a fitted model and a more general alternative model using bootstrap test.
test_boot(Y, X, y_fixed, alpha0, K_ens, K_int, sigma2_hat, tau_hat, B)(matrix, n*1) The vector of response variable.
(matrix, n*d_fix) The fixed effect matrix.
(vector of length n) Estimated fixed effect of the response.
(vector of length n) Kernel effect estimator of the estimated ensemble kernel matrix.
(matrix, n*n) Estimated ensemble kernel matrix.
(matrix, n*n) The kernel matrix to be tested.
(numeric) The estimated noise of the fixed effect.
(numeric) The estimated noise of the kernel effect.
(integer) A numeric value indicating times of resampling when test = "boot".
(numeric) p-value of the test.
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.
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