testing: Conducting Score Tests for Interaction

In IrisTeng/CVEK: Cross-Validated Kernel Ensemble

Description

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

Usage

testing(formula_int, label_names, Y, X1, X2, kern_list, mode = "loocv",
  strategy = "erm", beta = 1, test = "boot", lambda = exp(seq(-5, 5)),
  B = 100, data_test = NULL, fit_test = NULL)

Arguments

formula_int	(formula) A symbolic description of the model with interaction.
label_names	(list) A character string indicating all the interior variables included in each predictor.
Y	(vector of length n) Reponses of the dataframe.
X1	(dataframe, n*p1) The first type of factor in the dataframe (could contains several subfactors).
X2	(dataframe, n*p2) The second type of factor in the dataframe (could contains several subfactors).
kern_list	(list of length K) A list of kernel functions given by user.
mode	(character) A character string indicating which tuning parameter criteria is to be used.
strategy	(character) A character string indicating which ensemble strategy is to be used.
beta	(numeric/character) A numeric value specifying the parameter when strategy = "exp" ensemble_exp.
test	(character) A character string indicating which test is to be used.
lambda	(numeric) A numeric string specifying the range of noise to be chosen. The lower limit of lambda must be above 0.
B	(integer) A numeric value indicating times of resampling w hen test = "boot".

Details

There are two tests available here:

Asymptotic Test

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

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.

Value

pvalue	(numeric) p-value of the test.
u_weight	(numeric) p-value of the test.
lam	(numeric) p-value of the test.
train_RMSE	(numeric) p-value of the test.
test_RMSE	(numeric) p-value of the test.
K_tr	(numeric) p-value of the test.
A_tr	(numeric) p-value of the test.
V0_inv_tr	(numeric) p-value of the test.
K_eig	(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 ̊zˇkova ́, 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

Examples

testing(formula_int = Y ~ X1 * X2,
label_names = list(X1 = c("x1", "x2"), X2 = c("x3", "x4")),
Y, X1, X2, kern_list, mode = "loocv", strategy = "erm",
beta = 1, test = "boot", lambda = exp(seq(-5, 5)), B = 100)

IrisTeng/CVEK documentation built on May 31, 2019, 4:50 p.m.