Description Usage Arguments Value References Examples
Computes the bias corrected estimator of a single regression coefficient in the high dimensional binary outcome regression model and the corresponding standard error.
It also constructs the confidence interval for the target regression coefficient and tests whether it is equal to a prespecified value b0
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 
X 
Design matrix, of dimension n x p 
y 
Outcome vector, of length n 
index 
An integer between 1 and p indicating the index of the targeted regression coefficient. For example, 
model 
The fitted GLM, either 
intercept 
Should intercept be fitted for the initial estimator (default = 
init.coef 
Initial estimator of the regression vector (default = 
lambda 
The tuning parameter used in the construction of 
mu 
The dual tuning parameter used in the construction of the projection direction (default = 
step 
The step size used to compute 
resol 
The factor by which 
maxiter 
Maximum number of steps along which 
b0 
The null value to be tested against 
alpha 
Level of significance to test the null hypothesis that the target regression coefficient is equal to 
verbose 
Should inetrmediate message(s) be printed (default = 
prop.est 
The bias corrected estimator of the target regression coefficient 
se 
The standard error of the biascorrected estimator 
CI 
The confidence interval for the target regression coefficient 
decision 

proj 
The projection direction, of length p 
glmSIHR
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  sp = 20
n = 400
p = 800
f = function(x){
pnorm(x)
}
sig1 = toeplitz(seq(0.6, 0,length.out = p/10))
Sig = Matrix::bdiag(rep(list(sig1),10))+diag(rep(0.4,p))
X = MASS::mvrnorm(n, mu=rep(0,p), Sigma=Sig)
b = rep(0,p)
b[1:sp] = rep(c(0.4,0.4), sp/2)
prob = f(X %*% b)
y = array(dim = 1)
for(i in 1:n){
y[i] = rbinom(1,1,prob[i])
}
Est = SIHR::GLM_binary(X = X, y = y, index = 1, model = "probit", intercept = FALSE)

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