compare_MEMS: Function to compare micro effect on macro structure (MEMS)...
In netmediate: Micro-Macro Analysis for Social Networks
compare_MEMS R Documentation
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 as described by Wertsching and Duxbury (2025).
compare_MEMS can also be used as a test of the difference between two MEMS estimates within the same model. This is useful when researchers watn to formally evaluate whether the MEMS for one explanatory micro mechanism is significantly larger or smaller than a second explanatory micro mecahnism.
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,
micro_process2=NULL,
macro_function,
macro_function2=NULL,
object_type=NULL,
interval=c(0,1),
nsim=500,
algorithm="parametric",
silent=FALSE,
full_output=FALSE,
sensitivity_ev=TRUE,
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)
Arguments
partial_model
the micro-model excluding one or more intervening or confounding variables of interest. May also be a fully specified model 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 from partial_model in the case of sensitivity tests. Alternatively, researchers may provide the same model as given in partial_model when comparing MEMS estimates within a single model. In these latter cases, micro_process2 must be provided as well. 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 partial_model output.
micro_process2
an optional character string containing the name of the micro process for comparison. Used when full_model is a different class of model than partial_model or when comparing MEMS estimates for two different explanatory variables within the same model. When full_model is a different class of model than partial_model, the character string provided micro_process2 should match exactly the coefficient name in full_model.
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.
macro_function2
an optional function that calculates the macro statistic of interest. When provided, macro_function2 will be used in place of macro_function when calculating the MEMS provided by the full_model argument. This is intended for use in comparisons between distinct models (e.g., SAOM and ERGM) when a user provided function must be written differently for each model's output. Defaults to NULL.
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.
sensitivity_ev
optional parameter. If set to TRUE, will return E-values for direct, total, and indirect MEMS estimates. Not defined for sensitivity tests between models.
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.
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.
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.
Author(s)
Duxbury, Scott W. Associate Professor, University of North Carolina–Chapel Hill, Department of Sociology.
References
Duxbury, Scott W. 2024. "Micro Effects on Macro Structure in Social Networks." Sociological Methodology.
Wertsching, Jenna, and Scott W. Duxbury. Working paper. "Micro Effects on Macro Structure: Identification, Comparison between Nested Models, and Sensitivity Tests for Functional Form."
Duxbury, Scott W., and Xin Zhao. Working paper. "Sensitivity Tests for Micro-Macro Network Analysis."
See Also
AMME
MEMS
ergm.mma
mediate
mediate_MEMS
Examples
##############
# Not run
###############
library(statnet)
library(igraph)
data("faux.mesa.high")
####################
###mediation analysis
####################
#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")
########################
# Effect size comparison
########################
#Is the effect of racial homophily on transitivity larger or smaller
#than the effect of grade homophily?
compare_MEMS(partial_model=model2,
full_model=model2,
micro_process="nodematch.Race",
micro_process2="absdiff.Grade",
macro_function=transitivity,
object_type = "igraph",
silent=FALSE,
algorithm="parametric")
###################
# Robustness check
##################
#Are MEMS estimates derived from ERGM robust to alternative MPLE estimation strategies?
model_MPLE<-ergmMPLE(faux.mesa.high~edges+nodecov("Grade")+nodefactor("Race")+
nodefactor("Sex")+nodematch("Race")+nodematch("Sex")+absdiff("Grade")+
gwesp(.5,fixed=TRUE),
output="fit")
compare_MEMS(partial_model=model2,
full_model=model_MPLE,
micro_process="gwesp.fixed.0.5",
macro_function=transitivity,
object_type = "igraph",
silent=FALSE,
algorithm="parametric",
sensitivity_ev=FALSE)
###Compare between SAOM and TERGM
#treating behavioral (smoking) autocorrelation
#as outcome
library(RSiena)
##we'll load RSiena since we're using data from here.
###create a list of adjacency matrices
network_array<-list(s501,s502,s503)
smoking<-as.data.frame(s50s) ##load smoking data
##for our analysis, we'll look at binary smoking behavior
for(i in 1:ncol(smoking)){
smoking[,i][smoking[,i]>1]<-2
}
alcohol<-as.data.frame(s50a) ##we'll use alcohol consumption as a covariate as well
########################################
# Co-evolution model
#######################################
##create "sienaDependent" object,
TLSnet<-sienaDependent(array(c(network_array[[1]],
network_array[[2]],
network_array[[3]]),
dim=c(50,50,3)))
TLSbeh<-sienaDependent(as.matrix(smoking),type="behavior")
#set covariates
Alcohol<-varCovar(as.matrix(alcohol))
###create dataset, but specify network AND behavior
SAOM.Data<-sienaDataCreate(Network=TLSnet,
Behavior=TLSbeh,
Alcohol)
###Create the effects object
SAOM.terms<-getEffects(SAOM.Data)
###We'll start by specifying the NETWORK function
SAOM.terms<-includeEffects(SAOM.terms,egoX,altX,absDiffX,interaction1="Alcohol")
SAOM.terms<-includeEffects(SAOM.terms,egoX,altX,sameX,interaction1="Behavior")
SAOM.terms<-includeEffects(SAOM.terms,transTies,inPop)
###Now let's specify the BEHAVIOR function
SAOM.terms<-includeEffects(SAOM.terms,effFrom,name="Behavior",
interaction1="Alcohol")
SAOM.terms<-includeEffects(SAOM.terms,totSim,name="Behavior",
interaction1="Network")
SAOM.terms<-includeEffects(SAOM.terms,isolate,
name="Behavior",interaction1="Network")
#estimate the model
create.model<-sienaAlgorithmCreate(projname="Co-evolution_output",
seed=21093,
nsub=4,
n3=1000)
TLSmodel<-siena07(create.model,
data=SAOM.Data,
effects=SAOM.terms,
verbose=TRUE,
returnDeps=TRUE)
TLSmodel
#now fit the TERGM
library(statnet)
net_list<-list(as.network(s501),as.network(s502),as.network(s503))
net_list[[1]]<-network::set.vertex.attribute(net_list[[1]],"smoking",s50s[,1])
net_list[[2]]<-network::set.vertex.attribute(net_list[[2]],"smoking",s50s[,2])
net_list[[3]]<-network::set.vertex.attribute(net_list[[3]],"smoking",s50s[,3])
net_list[[1]]<-network::set.vertex.attribute(net_list[[1]],"alcohol",s50a[,1])
net_list[[2]]<-network::set.vertex.attribute(net_list[[2]],"alcohol",s50a[,2])
net_list[[3]]<-network::set.vertex.attribute(net_list[[3]],"alcohol",s50a[,3])
TERGM_1<-tergm(net_list~Form(
~edges+
mutual+
gwesp(.5,fixed=TRUE)+
gwidegree(.5,fixed=TRUE)+
nodeicov("smoking")+
nodeocov("smoking")+
nodematch("smoking")+
nodeicov("alcohol")+
nodeocov("alcohol")+
absdiff("alcohol")),
estimate="CMLE"
)
#create network autocorrelation function for TERGM
Moran_tergm<-function(x){
y<-network::get.vertex.attribute(x,"smoking")
return(nacf(x,y,type="moran",lag=1)[2])
}
#test difference in TERGM and SAOM estimates of the MEMS for
#network selection on same smoking behavior for
#smoking similarity at the aggregate level
compare_MEMS(partial_model=TLSmodel,
full_model=TERGM_1,
micro_process="same Behavior",
macro_function =Moran_dv,
micro_process2="Form~nodematch.smoking",
macro_function2=Moran_tergm,
object_type = "network",
SAOM_data = SAOM.Data,
silent=FALSE)
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| compare_MEMS | R Documentation |
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 as described by Wertsching and Duxbury (2025).
compare_MEMS can also be used as a test of the difference between two MEMS estimates within the same model. This is useful when researchers watn to formally evaluate whether the MEMS for one explanatory micro mechanism is significantly larger or smaller than a second explanatory micro mecahnism.
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,
micro_process2=NULL,
macro_function,
macro_function2=NULL,
object_type=NULL,
interval=c(0,1),
nsim=500,
algorithm="parametric",
silent=FALSE,
full_output=FALSE,
sensitivity_ev=TRUE,
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)Arguments
partial_model |
the micro-model excluding one or more intervening or confounding variables of interest. May also be a fully specified model in the case of sensitivity tests. Currently accepts |
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 from |
micro_process |
a character string containing the name of the micro process of interest. The character string should exactly match coefficient names in |
micro_process2 |
an optional character string containing the name of the micro process for comparison. Used when |
macro_function |
a |
macro_function2 |
an optional function that calculates the macro statistic of interest. When provided, |
object_type |
A character string that tells netmediate the type of object to apply the
|
interval |
The value of tuning parameters to assign to |
nsim |
The number of simulations or bootstrap samples to use during estimation. |
algorithm |
The estimation algorithm to be used. Currently accepts |
silent |
logical parameter. Whether to provide updates on the progress of the simulation or not. |
full_output |
logical parameter. If set to |
sensitivity_ev |
optional parameter. If set to |
SAOM_data |
required when the model is a |
SAOM_var |
optional parameter when the model is a |
time_interval |
an optional parameter to be used with |
covar_list |
an optional list of sender/receiver covariates used in |
edgelist |
an optional three column edgelist providing the sender, receiver, and time of event occurrence when using rem. |
net_logit_y |
the dependent variable for |
net_logit_x |
the matrix of independent variables for |
group_id |
optional vector of group identifiers to use when estimating a |
node_numbers |
a numeric vector containing the number of nodes in each group_id when using |
mediator |
a character string detailing the mediator of interest. Intended for internal use with the |
link_id |
a vector or list of vectors corresponding to unique identifiers. Intended for internal use with the |
controls |
a vector of character strings listing the controls to be calculated when using |
control_functions |
a list of functions to calculate the macro control variables provided in controls. Intended for internal use with the |
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.
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. |
Author(s)
Duxbury, Scott W. Associate Professor, University of North Carolina–Chapel Hill, Department of Sociology.
References
Duxbury, Scott W. 2024. "Micro Effects on Macro Structure in Social Networks." Sociological Methodology.
Wertsching, Jenna, and Scott W. Duxbury. Working paper. "Micro Effects on Macro Structure: Identification, Comparison between Nested Models, and Sensitivity Tests for Functional Form."
Duxbury, Scott W., and Xin Zhao. Working paper. "Sensitivity Tests for Micro-Macro Network Analysis."
See Also
AMME
MEMS
ergm.mma
mediate
mediate_MEMS
Examples
##############
# Not run
###############
library(statnet)
library(igraph)
data("faux.mesa.high")
####################
###mediation analysis
####################
#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")
########################
# Effect size comparison
########################
#Is the effect of racial homophily on transitivity larger or smaller
#than the effect of grade homophily?
compare_MEMS(partial_model=model2,
full_model=model2,
micro_process="nodematch.Race",
micro_process2="absdiff.Grade",
macro_function=transitivity,
object_type = "igraph",
silent=FALSE,
algorithm="parametric")
###################
# Robustness check
##################
#Are MEMS estimates derived from ERGM robust to alternative MPLE estimation strategies?
model_MPLE<-ergmMPLE(faux.mesa.high~edges+nodecov("Grade")+nodefactor("Race")+
nodefactor("Sex")+nodematch("Race")+nodematch("Sex")+absdiff("Grade")+
gwesp(.5,fixed=TRUE),
output="fit")
compare_MEMS(partial_model=model2,
full_model=model_MPLE,
micro_process="gwesp.fixed.0.5",
macro_function=transitivity,
object_type = "igraph",
silent=FALSE,
algorithm="parametric",
sensitivity_ev=FALSE)
###Compare between SAOM and TERGM
#treating behavioral (smoking) autocorrelation
#as outcome
library(RSiena)
##we'll load RSiena since we're using data from here.
###create a list of adjacency matrices
network_array<-list(s501,s502,s503)
smoking<-as.data.frame(s50s) ##load smoking data
##for our analysis, we'll look at binary smoking behavior
for(i in 1:ncol(smoking)){
smoking[,i][smoking[,i]>1]<-2
}
alcohol<-as.data.frame(s50a) ##we'll use alcohol consumption as a covariate as well
########################################
# Co-evolution model
#######################################
##create "sienaDependent" object,
TLSnet<-sienaDependent(array(c(network_array[[1]],
network_array[[2]],
network_array[[3]]),
dim=c(50,50,3)))
TLSbeh<-sienaDependent(as.matrix(smoking),type="behavior")
#set covariates
Alcohol<-varCovar(as.matrix(alcohol))
###create dataset, but specify network AND behavior
SAOM.Data<-sienaDataCreate(Network=TLSnet,
Behavior=TLSbeh,
Alcohol)
###Create the effects object
SAOM.terms<-getEffects(SAOM.Data)
###We'll start by specifying the NETWORK function
SAOM.terms<-includeEffects(SAOM.terms,egoX,altX,absDiffX,interaction1="Alcohol")
SAOM.terms<-includeEffects(SAOM.terms,egoX,altX,sameX,interaction1="Behavior")
SAOM.terms<-includeEffects(SAOM.terms,transTies,inPop)
###Now let's specify the BEHAVIOR function
SAOM.terms<-includeEffects(SAOM.terms,effFrom,name="Behavior",
interaction1="Alcohol")
SAOM.terms<-includeEffects(SAOM.terms,totSim,name="Behavior",
interaction1="Network")
SAOM.terms<-includeEffects(SAOM.terms,isolate,
name="Behavior",interaction1="Network")
#estimate the model
create.model<-sienaAlgorithmCreate(projname="Co-evolution_output",
seed=21093,
nsub=4,
n3=1000)
TLSmodel<-siena07(create.model,
data=SAOM.Data,
effects=SAOM.terms,
verbose=TRUE,
returnDeps=TRUE)
TLSmodel
#now fit the TERGM
library(statnet)
net_list<-list(as.network(s501),as.network(s502),as.network(s503))
net_list[[1]]<-network::set.vertex.attribute(net_list[[1]],"smoking",s50s[,1])
net_list[[2]]<-network::set.vertex.attribute(net_list[[2]],"smoking",s50s[,2])
net_list[[3]]<-network::set.vertex.attribute(net_list[[3]],"smoking",s50s[,3])
net_list[[1]]<-network::set.vertex.attribute(net_list[[1]],"alcohol",s50a[,1])
net_list[[2]]<-network::set.vertex.attribute(net_list[[2]],"alcohol",s50a[,2])
net_list[[3]]<-network::set.vertex.attribute(net_list[[3]],"alcohol",s50a[,3])
TERGM_1<-tergm(net_list~Form(
~edges+
mutual+
gwesp(.5,fixed=TRUE)+
gwidegree(.5,fixed=TRUE)+
nodeicov("smoking")+
nodeocov("smoking")+
nodematch("smoking")+
nodeicov("alcohol")+
nodeocov("alcohol")+
absdiff("alcohol")),
estimate="CMLE"
)
#create network autocorrelation function for TERGM
Moran_tergm<-function(x){
y<-network::get.vertex.attribute(x,"smoking")
return(nacf(x,y,type="moran",lag=1)[2])
}
#test difference in TERGM and SAOM estimates of the MEMS for
#network selection on same smoking behavior for
#smoking similarity at the aggregate level
compare_MEMS(partial_model=TLSmodel,
full_model=TERGM_1,
micro_process="same Behavior",
macro_function =Moran_dv,
micro_process2="Form~nodematch.smoking",
macro_function2=Moran_tergm,
object_type = "network",
SAOM_data = SAOM.Data,
silent=FALSE)
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