Summary and print methods for the class CDnetNPL as returned by the function CDnetNPL.
# S3 method for CDnetNPL
summary(object, cov.ctr = list(), Glist, data, ...)# S3 method for summary.CDnetNPL
print(x, ...)
# S3 method for CDnetNPL
print(x, ...)
# S3 method for summary.CDnetNPLs
print(x, ...)
an object of class CDnetNPL, output of the function CDnetNPL.
list of control values for the covariance containing two integers, R and S. The covariance summations from 0
to infinity. But the summed elements decreases exponentially. The summations are approximated by summations from 0 to R.
The covariance also requires computing \(\Phi(x) - \Phi(x - 1)\), where \(\Phi\) is
the normal' probability density function. This is done using important sampling, where S numbers are generated
form the uniform distribution.
further arguments passed to or from other methods.
an object of class summary.CDnetNPL, output of the function summary.CDnetNPL,
class summary.CDnetNPLs, list of outputs of the function summary.CDnetNPL
(when the model is estimated many times to control for the endogeneity)
or class CDnetNPL of the function CDnetNPL.
A list consisting of:
number of sub-networks.
number of individuals in each network.
number of iterations performed by the NPL algorithm.
NPL estimator.
pseudo-likelihood value.
ybar (see details), expectation of y.
average of the expectation of y among friends.
step-by-step output as returned by the optimizer.
covariance matrix of the estimate.
vector of marginal effects.
covariance matrix of the marginal effects.
returned value of the control values for the covariance.
list of formula, name of the object Glist, number of friends in the network and name of the object data.