The two-stage procedure — two

The two-stage procedure first estimates the centralities then regress the outcome of interest to the estimated centralities and other covariates.

two_stage(A, X, y, r = 1, scaled = 1, weights = rep(1, length(y)), ...)

Arguments

A	The adjacency matrix of the input network
X	The design matrix
y	The response vector
r	The rank of the input adjacency matrix
scaled	Scale \(u\) and \(v\) of norm \(\sqrt{n}\)
weights	The weight vector for each observation in (X,y)

Value

Output a two_stage object

u: The estimated hub centrality
v: The estimated authority centrality
beta: The scaled estimated regression coeffcients
coefficients: The original estimated regression coeffcients without scaling.
residuals: The residuals of the regression
fitted.values: The predicted response
epsa: The estimated \(\sigma_a\)
epsy: The estimated \(\sigma_y\)
A: The adjacency matrix of the input network
X: The input design matrix
y: The input response
method: The estimation method: two_stage
...: Auxiliary output from lm.fit

Examples

n <- 100
p <- 3
sigmaa <- 1
sigmay <- 1e-5
A <- matrix(rnorm(n^2, sd = sigmaa), nrow = n)
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
y <- rnorm(n, sd = sigmay)
ret <- two_stage(A, X, y)