meffects: Marginal Effects for Count Data Models and Tobit Models with...

In CDatanet: Econometrics of Network Data

Marginal Effects for Count Data Models and Tobit Models with Social Interactions

Description

meffects computes marginal effects for count data and Tobit models with social interactions. It is a generic function which means that new printing methods can be easily added for new classes.

Usage

meffects(model, ...)

## S3 method for class 'cdnet'
meffects(
  model,
  Glist,
  cont.var,
  bin.var,
  type.var,
  Glist.contextual,
  data,
  tol = 1e-10,
  maxit = 500,
  boot = 1000,
  progress = TRUE,
  ncores = 1,
  ...
)

## S3 method for class 'summary.cdnet'
meffects(
  model,
  Glist,
  cont.var,
  bin.var,
  type.var,
  Glist.contextual,
  data,
  tol = 1e-10,
  maxit = 500,
  boot = 1000,
  progress = TRUE,
  ncores = 1,
  ...
)

## S3 method for class 'sart'
meffects(
  model,
  Glist,
  cont.var,
  bin.var,
  type.var,
  Glist.contextual,
  data,
  tol = 1e-10,
  maxit = 500,
  boot = 1000,
  progress = TRUE,
  ncores = 1,
  ...
)

## S3 method for class 'summary.sart'
meffects(
  model,
  Glist,
  cont.var,
  bin.var,
  type.var,
  Glist.contextual,
  data,
  tol = 1e-10,
  maxit = 500,
  boot = 1000,
  progress = TRUE,
  ncores = 1,
  ...
)

Arguments

model	an object of class cdnet (summary.cdnet) or sart (summary.sart), output of the function cdnet or sart, respectively.
...	Additional arguments passed to methods.
Glist	The network matrix used to obtain model. Typically, this is the Glist argument supplied to the function cdnet or sart.
cont.var	A character vector of continuous variable names for which the marginal effects should be computed.
bin.var	A character vector of binary variable names for which the marginal effects should be computed.
type.var	A list indicating "own" and contextual variables that appear in the cont.var and bin.var arguments. The list contains pairs of variable names, with the first element being the "own" variable and the second being the contextual variable. When a variable has no associated contextual variable, only the variable name is included. For example, type.var = list(c("x1", "gx1"), c("x2", "gx2"), "x3") means that gx1 is the contextual variable for x1, gx2 is the contextual variable for x2, and x3 has no contextual variable. This information is used to compute the indirect and total marginal effects for x1, x2, and x3.
Glist.contextual	The network matrix used to compute contextual variables, if any are specified in the type.var argument. For networks consisting of multiple subnets, Glist can be a list of subnets, where the m-th element is an ns*ns adjacency matrix, with ns denoting the number of nodes in the m-th subnet.
data	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(model), typically the environment from which meffects is called.
tol	The tolerance value used in the fixed-point iteration method to compute y. The process stops if the \ell_1-distance between two consecutive values of y is less than tol.
maxit	The maximum number of iterations in the fixed-point iteration method.
boot	The number of bootstrap simulations to compute standard errors and confidence intervals.
progress	A logical value indicating whether the progress of the bootstrap simulations should be printed to the console.
ncores	Number of CPU cores (threads) used to run the bootstrap process in parallel.

Value

A list containing:

info

General information about the model.

estimate

The Maximum Likelihood (ML) estimates of the parameters.

Ey

E(y), the expected values of the endogenous variable.

GEy

The average of E(y) among peers.

cov

A list containing covariance matrices (if cov = TRUE).

details

Additional outputs returned by the optimizer.

meffects

A list containing the marginal effects.

Examples

#' set.seed(123)
M      <- 5 # Number of sub-groups
nvec   <- round(runif(M, 100, 200))
n      <- sum(nvec)

# Adjacency matrix
A      <- list()
for (m in 1:M) {
  nm           <- nvec[m]
  Am           <- matrix(0, nm, nm)
  max_d        <- 30 #maximum number of friends
  for (i in 1:nm) {
    tmp        <- sample((1:nm)[-i], sample(0:max_d, 1))
    Am[i, tmp] <- 1
  }
  A[[m]]       <- Am
}
Anorm  <- norm.network(A) #Row-normalization

# X
X      <- cbind(rnorm(n, 1, 3), rexp(n, 0.4))

# Two group:
group  <- 1*(X[,1] > 0.95)

# Networks
# length(group) = 2 and unique(sort(group)) = c(0, 1)
# The networks must be defined as to capture:
# peer effects of `0` on `0`, peer effects of `1` on `0`
# peer effects of `0` on `1`, and peer effects of `1` on `1`
G        <- list()
cums     <- c(0, cumsum(nvec))
for (m in 1:M) {
  tp     <- group[(cums[m] + 1):(cums[m + 1])]
  Am     <- A[[m]]
  G[[m]] <- norm.network(list(Am * ((1 - tp) %*% t(1 - tp)),
                              Am * ((1 - tp) %*% t(tp)),
                              Am * (tp %*% t(1 - tp)),
                              Am * (tp %*% t(tp))))
}

# Parameters
lambda <- c(0.2, 0.3, -0.15, 0.25) 
Gamma  <- c(4.5, 2.2, -0.9, 1.5, -1.2)
delta  <- rep(c(2.6, 1.47, 0.85, 0.7, 0.5), 2) 

# Data
data   <- data.frame(X, peer.avg(Anorm, cbind(x1 = X[,1], x2 =  X[,2])))
colnames(data) = c("x1", "x2", "gx1", "gx2")

ytmp   <- simcdnet(formula = ~ x1 + x2 + gx1 + gx2, Glist = G, Rbar = rep(5, 2),
                   lambda = lambda, Gamma = Gamma, delta = delta, group = group,
                   data = data)
y      <- ytmp$y
hist(y, breaks = max(y) + 1)
table(y)

# Estimation
est    <- cdnet(formula = y ~ x1 + x2 + gx1 + gx2, Glist = G, Rbar = rep(5, 2), group = group,
                optimizer = "fastlbfgs", data = data,
                opt.ctr = list(maxit = 5e3, eps_f = 1e-11, eps_g = 1e-11))

meffects(est, Glist = G, data = data, cont.var = c("x1", "x2", "gx1", "gx2"),
         type.var = list(c("x1", "gx1"), c("x2", "gx2")), Glist.contextual = Anorm,
         boot = 100, ncores = 2)

CDatanet documentation built on Nov. 11, 2025, 5:14 p.m.