netmediate (version 1.0.1)

compare_MEMS: Function to compare micro effect on macro structure (MEMS) estimates between models.

Description

compare_MEMS implements parametric and nonparametric routines to compare MEMS estimate between models. When compared between nested models, compare_MEMS results can be interpreted as the portion of a MEMS explained by a mediating or confounding variable. When compared between models with distinct functional forms and the same specification, compare_MEMS results can be interpreted as the sensitivity of MEMS results to decision about model functional form.

The difference in MEMS is the change in MEMS after one or more micro-processes are included into a model or, in the case of sensitivity tests, when the functional form is changed. Let \(MEMS_p\) represent the MEMS obtained from a model that omits one or more intervening variables and \(MEMS_f\) be the MMES obtained from a model that includes the intervening variable(s). The change in MEMS is given $$\Delta MEMS=MEMS_p-MEMS_f$$. \(MEMS_p\) and \(MEMS_f\) may also be have the same specification but use distinct functional forms or other modeling decisions in the case of sensitivity tests. Tuning parameters can be assigned to toggle the strength of \(\theta\) in model-implied estimates of \(MEMS\). MEMS currently accepts glm, glmer, ergm, btergm, sienaFit, rem.dyad, and netlogit objects and implements both parametric and nonparametric estimation. Pooled estimation for multiple network models is also implemented for ergm and sienaFit objects.

Usage

compare_MEMS(partial_model,
      full_model,
      micro_process,
      macro_function,
      object_type=NULL,
      interval=c(0,1),
      nsim=500,
      algorithm="parametric",
      silent=FALSE,
      full_output=FALSE,
      SAOM_data=NULL,
      SAOM_var=NULL,
      time_interval=NULL,
      covar_list=NULL,
      edgelist=NULL,
      net_logit_y=NULL,
      net_logit_x=NULL,
      group_id=NULL,
      node_numbers=NULL,
      mediator=NULL,
      link_id=NULL,
      controls=NULL,
      control_functions=NULL)

Value

If full_output=FALSE, then a table is returned with the change MEMS, its standard error, confidence interval, and p-value, and the same results for the partial and complete MEMS.

If full_output=TRUE, then a list is returned with the following three elements.

diff_MEMS_results

is the table of summary output containing the MEMS, its standard error, confidence interval, and p-value, and a list of the simulated values of the change in MEMS.

p_MEMS_results

contains the summary statistics for the partial MEMS along with all simulated statistics.

f_MEMS_results

contains the summary statistics for the full MEMS along with all simulated statistics.

Arguments

partial_model

the micro-model excluding one or more intervening or confounding variables of interest. May also be a fully specified model with a distinct functional form in the case of sensitivity tests. Currently accepts glm, glmer, ergm, btergm, sienaFit, rem.dyad, and netlogit objects. Pooled estimation for multiple network models is also implemented for ergm and sienaFit objects. To implement pooled estimation, model should be provided as a list of ergm or sienaFit objects.

full_model

the micro-model including one or more intervening or confounding variables of interest. May also be a fully specified model with a distinct functional form in the case of sensitivity tests. Currently accepts glm, glmer, ergm, btergm, sienaFit, rem.dyad, and netlogit objects. Pooled estimation for multiple network models is also implemented for ergm and sienaFit objects. To implement pooled estimation, model should be provided as a list of ergm or sienaFit objects.

micro_process

a character string containing the name of the micro process of interest. The character string should exactly match coefficient names in model output.

macro_function

a function that calculates the macro statistic of interest. Currently accepts user defined functions as well as functions inherent in the igraph and statnet packages for R.

object_type

A character string that tells netmediate the type of object to apply the macro_function to. Currently accepts igraph and network objects. If left NULL, network objects are assumed. Can be over-ridden to use other object types with a user-function by defining a function that accepts either a network or igraph object and returns a numeric value or vector of numeric values (see examples).

interval

The value of tuning parameters to assign to \(\theta\). Should be provided as a vector of numeric values with 2 entries.

nsim

The number of simulations or bootstrap samples to use during estimation.

algorithm

The estimation algorithm to be used. Currently accepts "parametric" and "nonparametric". If "parametric", estimation is obtained with Monte Carlo sampling. If "nonparametric", estimation uses bootstrap resampling.

silent

logical parameter. Whether to provide updates on the progress of the simulation or not.

full_output

logical parameter. If set to TRUE, compare_MEMS will return all sampled statistics and complete results for \(MEMS_p\) and \(MEMS_f\).

SAOM_data

required when the model is a sienaFit object; ignored otherwise. If a sienaFit object is provided, SAOM_data should be the siena object that contains the data for SAOM estimation. If using pooled estimation on multiple sienaFit objects (i.e., providing a list of sienaFit objects), then SAOM_data should be provided as an ordered list with each entry containing the siena object corresponding to list of sienaFit objects.

SAOM_var

optional parameter when the model is a sienaFit object. SAOM_var is a list of of the varCovar and varDyadCovar objects used to assign time varying node and dyad covariates when calling sienaDataCreate. If provided, netmediate assigns the varying node covariates and dyad covariates to each simulated network. This parameter is required when macro_function computes a statistic that varies as a function of time varying node or dyad covariates (i.e., network segregation, assorativity). Time invariant characteristics (coCovar and coDyadCovar) are handled internally by MEMS and should not be provided. When providing a list of sienaFit objects for pooled estimation, SAOM_var should be provided as a list of lists, where each entry in the list contains a list of varCovar and varDyadCovar objects associated with corresponding sienaFit object.

time_interval

an optional parameter to be used with rem.dyad objects. May be provided as a numeric vector or the character string "aggregate". If a numeric vector is provided unique network snapshots at each interval. For example, time_interval=c(0,2,3) would induce two networks, one for the 0 - 2 time period and one for the 2 - 3 time period. If specified as "aggregate", the MEMS is calculated by creating an aggregated cross-sectional representation of the entire event sequence. If left NULL, defaults to |"aggregate".

covar_list

an optional list of sender/receiver covariates used in rem.dyad estimation. Only required for rem.dyad objects when covariates are included. The list format should correspond to the format required by rem.dyad

.

edgelist

an optional three column edgelist providing the sender, receiver, and time of event occurrence when using rem.rem.dyad. Only required when time_interval is set to NULL or "aggregate". Ignored for other types of models.

net_logit_y

the dependent variable for netlogit objects. Should be provided as a vector. Only required when model is a netlogit object.

net_logit_x

the matrix of independent variables for netlogit type objects. Only required when model is a netlogit object.

group_id

optional vector of group identifiers to use when estimating a glm or glmer on grouped data (i.e., multiple time periods, multiple networks). When specified, MEMS will induce unique networks for each grouping factor. If left unspecified, all groups/time periods are pooled. If using glmer, the grouping factor does not have to be provided as part of the model or used as a random effect.

node_numbers

a numeric vector containing the number of nodes in each group_id when using glm or glmer. If estimating MEMS aggregated over all networks (i.e., group_id=NULL), this shoud be the total number of nodes in all networks. Required when using glm or glmer, ignored otherwise.

mediator

a character string detailing the mediator of interest. Intended for internal use with the AMME function; not intended for end users.

link_id

a vector or list of vectors corresponding to unique identifiers. Intended for internal use with the AMME function; not intended for end users.

controls

a vector of character strings listing the controls to be calculated when using AMME. Intended for internal use with the AMME function; not intended for end users.

control_functions

a list of functions to calculate the macro control variables provided in controls. Intended for internal use with the AMME function; not intended for end users.

Author

Duxbury, Scott W. Associate Professor, University of North Carolina--Chapel Hill, Department of Sociology.

Details

Compares MEMS estimates between two models. If one or more confounding or intervening variables are excluded or included between models, the change in MEMS can be interpreted as the portion of the MEMS explained by one or more confounding or intervening variable. If two models are provided with the same specification but a distinct functional form, the change in MEMS is a sensitivty test of how much the MEMS estimate changes because of a model decision. This can be useful, for example, when comparing TERGM and SAOM estimates as each models make distinct assumptions about sources of network change and the temporal ordering of tie changes.

compare_MEMS functionality inherits directly from the MEMS command. See the MEMS page for more details.

References

Duxbury, Scott W. 2024. "Micro Effects on Macro Structure in Social Networks." Sociological Methodology.

Wertsching, Jenna, and Scott W. Duxbury. Working paper. "Comparing Micro Effects on Macro Structure between Nested Models."

See Also

AMME MEMS ergm.mma mediate

Examples

# \dontshow{
require(ergm)
require(network)
require(sna)


set.seed(21093)
a1<-network::as.network(matrix(c(rbinom(10, 1,.3),
            rbinom(10, 1,.3),
             rbinom(10, 1,.3),
              rbinom(10, 1,.3),
               rbinom(10, 1,.3),
            rbinom(10, 1,.3),
            rbinom(10, 1,.3),
            rbinom(10, 1,.3),
            rbinom(10, 1,.3),
            rbinom(10, 1,.3)),
          nrow=10,ncol=10))

network::set.vertex.attribute(a1,"var.1",rbinom(10,1,.3))
a<-ergm(a1~edges+nodeifactor("var.1")+nodeofactor("var.1"))

compare_MEMS(partial_model=a,
            full_model=a,
      micro_process="nodeifactor.var.1.1",
      macro_function=gtrans,
      nsim=20,
      silent=TRUE)

# }

# \donttest{

##############
# Not run
###############
library(statnet)
library(igraph)
data("faux.mesa.high")

  #how much of the effect of racial homophily on transitivity
    #is explained by triadic closure effects?

model<-ergm(faux.mesa.high~edges+nodecov("Grade")+nodefactor("Race")+
               nodefactor("Sex")+nodematch("Race")+nodematch("Sex")+absdiff("Grade"))

model2<-ergm(faux.mesa.high~edges+nodecov("Grade")+nodefactor("Race")+
               nodefactor("Sex")+nodematch("Race")+nodematch("Sex")+absdiff("Grade")+
               gwesp(.5,fixed=TRUE))


compare_MEMS(partial_model=model,
              full_model=model2,
              micro_process="nodematch.Race",
             macro_function=transitivity,
             object_type = "igraph",
             silent=FALSE,
             algorithm="parametric")


# }