HDGLM_test {HDGLM}

Tests the Coefficients of High Dimensional Generalized Linear Models

Description

Tests for whole or partial regression coefficient vectors for high dimensional generalized linear models.

Usage

HDGLM_test(Y, X, beta_0 = NULL, nuisance = NULL, model = "gaussian")

Arguments

Y

a vector of observations of length n, where n is the sample size.

X

a design matrix with n rows and p columns, where p is the dimension of the covariates.

beta_0

a vector with length p. It is the value of regression coefficient under the null hypothesis in global test. The default is \beta_0=0 and it can be non-zero in the global test. In the test with nuisance coefficients, we only deal with \beta_0^{(2)}=0.

nuisance

an index indicating which coefficients are nuisance parameter. The default is "NULL" (the global test).

model

a character string to describe the model and link function. The default is "gaussian", which denotes the linear model using identity link. The other options are "poisson", "logistic" and "negative_binomial" models, where the poisson and negative binomial models using log link.

Value

An object of class "HDGLM_test" is a list containing the following components:

test_stat

the standardized test statistic

test_pvalue

pvalue of the test against the null hypothesis

Note

In global test, the function "HDGLM_test" can deal with the null hypothesis with non-zero coefficients (\beta_0). However, in test with nuisance coefficient, the function can only deal with the null hypothesis with zero coefficients (\beta_0^{(2)}) in this version.

Author(s)

Bin Guo

References

Guo, B. and Chen, S. X. (2015). Tests for High Dimensional Generalized Linear Models.

Examples

## Example: Linear model
## Global test: if the null hypothesis is true (beta_0=0)
alpha=runif(5,min=0,max=1)
## Generate the data
DGP_0=DGP(80,320,alpha)
result=HDGLM_test(DGP_0$Y,DGP_0$X)
## Pvalue
result$test_pvalue

## Global test: if the alternative hypothesis is true
## (the square of the norm of the first 5 nonzero coefficients to be 0.2)
## Generate the data
DGP_0=DGP(80,320,alpha,sqrt(0.2),5)
result=HDGLM_test(DGP_0$Y,DGP_0$X)
## Pvalue
result$test_pvalue

## Test with nuisance coefficients: if the null hypothesis is true (beta_0^{(2)}=0)
## The first 10 coefficients to be the nuisance coefficients
betanui=runif(10,min=0,max=1)
## Generate the data
DGP_0=DGP(80,320,alpha,0,no=NA,betanui)
result=HDGLM_test(DGP_0$Y,DGP_0$X,nuisance=c(1:10))
## Pvalue
result$test_pvalue

## Test with nuisance coefficients: if the alternative hypothesis is true
## (the square of the norm of the first 5 nonzero coefficients in beta_0^{(2)} to be 2)
## The first 10 coefficients to be the nuisance coefficients
betanui=runif(10,min=0,max=1)
## Generate the data
DGP_0=DGP(80,330,alpha,sqrt(2),no=5,betanui)
result=HDGLM_test(DGP_0$Y,DGP_0$X,nuisance=c(1:10))
## Pvalue
result$test_pvalue