simcdpar computes the average expected outcomes for count data models with social interactions and standard errors using the Delta method.
This function can be used to examine the effects of changes in the network or in the control variables.
simcdEy(object, Glist, data, group, tol = 1e-10, maxit = 500, S = 1000)A list consisting of:
\(E(y)\), the expectation of y.
the average of \(E(y)\) friends.
the sampling mean of \(E(y)\).
the standard error of the sampling mean of \(E(y)\).
an object of class summary.cdnet, output of the function summary.cdnet or class cdnet, output of the function cdnet.
adjacency matrix. For networks consisting of multiple subnets, Glist can be a list of subnets with the m-th element being an ns*ns adjacency matrix, where ns is the number of nodes in the m-th subnet.
For heterogeneous peer effects (e.g., boy-boy, boy-girl friendship effects), the m-th element can be a list of many ns*ns adjacency matrices corresponding to the different network specifications (see Houndetoungan, 2024).
For heterogeneous peer effects in the case of a single large network, Glist must be a one-item list. This item must be a list of many specifications of large networks.
an optional data frame, list, or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which summary.cdnet is called.
the vector indicating the individual groups (see function cdnet). If missing, the former group saved in object will be used.
the tolerance value used in the Fixed Point Iteration Method to compute the expectancy of y. The process stops if the \(\ell_1\)-distance between two consecutive \(E(y)\) is less than tol.
the maximal number of iterations in the Fixed Point Iteration Method.
number of simulations to be used to compute integral in the covariance by important sampling.
simcdnet