Nothing
#
# This function estimates the MEMS using ERGM with parametric algorithm
#
#
# model is the ERGM object
#micro_process is the micro process of interest, provided as a character string
#macro_function is the function to be calculated on the network object. NOTE: currently only supports statistics calculated on network and igraph objects.
#object type is the type of object used in the function to calculate macro statistics. Currently only supports igraph and network objects. If left NULL, the object is assumed to be a network object.
#the interval provides the values over which to calculate the MEMS. It defaults to 0 and 1
#nsim is the number of Monte Carlo samples
# silent tells R whether to provide updates on the algorithm's progress
#full_output tells R whether to return simulated distribution in addition to summary statistic
MEMS_pooled_ergm_param <- function(model,
micro_process,
macro_function,
object_type=object_type,
interval=interval,
nsim=nsim,
silent=silent,
full_output=full_output,
mediator=mediator,
link_id=link_id,
controls=controls,
control_functions=control_functions) {
aMEMS_tracer<-1
coef_list<-list()
cov_list<-list()
theta_list<-list()
ergm_mat_list<-list()
for(i in 1:length(model)){
coef_list[[i]]<-model[[i]]$coefficients
cov_list[[i]]<-model[[i]]$covar
if(any(is.na(coef_list[[i]]))|
any(is.infinite(coef_list[[i]]))){
stop("Infinite or missing values in parameter estimates. Algorithm cannot continue.")
}
if(any(is.na(cov_list[[i]]))|
any(is.infinite(cov_list[[i]]))){
stop("Infinite or missing values in covariance matrix estimates. Algorithm cannot continue.")
}
theta_list[[i]]<-MASS::mvrnorm(n=nsim,
mu=coef_list[[i]],
Sigma=cov_list[[i]],
empirical = TRUE)
# ergm_mat_list[[i]]<-ergMargins::edge.prob2(model[[i]])
ergm_mat_list[[i]]<-model[[i]]$network
}
interval<-sort(interval) #order from lowest to highest
mat_list<-list()
#create list of values to predict over
for(j in 1:length(ergm_mat_list)){
mat_list[[j]]<-ergm_mat_list[[j]]
for(i in 1:length(interval)){
mat_list[[j]][[i]]<-ergm_mat_list[[j]]
}
}
#create lists of data for each link_id and control. These lists provide the data for AMME function
if(!is.null(link_id)){
link_list_data<-list()
for(i in 1:nsim){
link_list_data[[i]]<-matrix(NA,nrow=length(unique(link_id[[1]])),ncol=length(interval))
rownames(link_list_data[[i]])<-unique(link_id[[1]])
}
if(length(controls)>0){
controls_list<-as.list(controls)
for(i in 1:length(controls)){
sim_list<-list()
for(j in 1:nsim){
sim_list[[j]]<-matrix(NA,nrow=length(unique(link_id[[i+1]])),ncol=length(interval))
rownames(sim_list[[j]])<-unique(link_id[[i+1]])
}
controls_list[[i]]<-sim_list
names(controls_list)<-controls
}
}else{
controls_list<-NULL
}
}
message("Computing MEMS over ",interval[1],"-",interval[length(interval)]," interval")
output_list<-list()
for(i in 1:length(ergm_mat_list)){
output_list[[i]]<-matrix(NA,nrow=nsim,ncol=length(interval))
}
for(entry in 1:length(output_list)){
#simulate values for j parameters
for(j in 1:nrow(theta_list[[entry]])){
#create networks for k intervals
net_list<-list()
net_list[[entry]]<-model[[entry]]$network
for(i in 1:length(interval)){
# pred_mat<-mat_list[[entry]][[i]]
# start.drops<-ncol(pred_mat)-5
# pred_mat<-pred_mat[,-c(1,start.drops:ncol(pred_mat))]
cbcoef<-theta_list[[entry]][j,]
cbcoef[micro_process]<-cbcoef[micro_process]*interval[i]
net_list[[entry]][[i]]<-simulate(model[[entry]],
nsim=1,
coef=cbcoef)
#predict ties
# lp <- as.matrix(pred_mat) %*% cbcoef
# result <- c(1/(1 + exp(-lp)))
# pred_mat<-mat_list[[entry]][[i]]
# pred_mat$y <- rbinom(nrow(mat_list[[entry]][[i]]),1,result) # create predicted ties
#create network
# el<-pred_mat[,c("i.name","j.name","y")]
# el<-el[el$y==1,-c(3)]
# el<-as.matrix(el)
#create network, assign vertex attributes, by creating empty network and adding new edges
# net_list[[entry]][[i]]<-model[[entry]]$network
# net_list[[entry]][[i]][,]<-0
# net_list[[entry]][[i]]<-network::add.edges(net_list[[entry]][[i]],tail=el[,1],head=el[,2])
}
##now need to calculate statistics on each network in list
#then need to calculate difference statistics and MEMS or aMEMS
#then need to store output
if(object_type[1]%in%c("network")){
for(i in 1:length(interval)){
b<-macro_function(net_list[[entry]][[i]])
if(!is.null(link_id)){
node_start<- which(is.na(link_list_data[[j]][,i]))[1]
node_index<-node_start+(length(b)-1)
link_list_data[[j]][node_start:node_index,i]<-b
} ##store vector of output when calling AMME
if(length(b)>1){
b<-mean(b,na.rm=TRUE)
aMEMS_tracer<-1} ##handle node and multiple obs characteristics for MEMS output
output_list[[entry]][j,i]<-b
}
}else{
#for igraph functions
for(i in 1:length(interval)){
net_list[[entry]][[i]]<-intergraph::asIgraph(net_list[[entry]][[i]])
b<-macro_function(net_list[[entry]][[i]])
if(!is.null(link_id)){
node_start<- which(is.na(link_list_data[[j]][,i]))[1]
node_index<-node_start+(length(b)-1)
link_list_data[[j]][node_start:node_index,i]<-b
} #store vector of output when calling AMME
if(length(b)>1){
b<-mean(b,na.rm=TRUE)
aMEMS_tracer<-1} ##handle node and multiple obs characteristics
output_list[[entry]][j,i]<-b
if(length(controls)>0){
net_list[[entry]][[i]]<-intergraph::asNetwork(net_list[[entry]][[i]]) # convert back to network for control operations
}
}
}
##get controls
if(length(controls)>0){
for(control in 1:length(controls_list)){
if(object_type[control+1]%in%c("network")){
for(i in 1:length(interval)){
b<-control_functions[[control]](net_list[[entry]][[i]])
node_start<- which(is.na(controls_list[[control]][[j]][,i]))[1]
node_index<-node_start+(length(b)-1)
controls_list[[control]][[j]][node_start:node_index,i]<-b
}
}else{
#for igraph functions
for(i in 1:length(interval)){
net_list[[i]]<-intergraph::asIgraph(net_list[[entry]][[i]])
b<-control_functions[[control]](net_list[[entry]][[i]])
node_start<- which(is.na(controls_list[[control]][[j]][,i]))[1]
node_index<-node_start+(length(b)-1)
controls_list[[control]][[j]][node_start:node_index,i]<-b
net_list[[entry]][[i]]<-intergraph::asNetwork(net_list[[entry]][[i]]) #convert back to network object for further loops
}
}
}
} #closes if controls statement
if(silent==FALSE){
print(paste("Simulation ",j," out of", nsim," complete for network ", entry))
}
}
} #close entry loop
if(aMEMS_tracer==1 &is.null(link_id)){
message("More than one macro statistic is being calculated. Reporting the aMEMS.")
}
for(i in 1:length(output_list)){
if(i == 1){
output_data<-output_list[[i]]
}else{
output_data<-rbind(output_data,output_list[[i]])
}
}
diff_data<-matrix(NA,nrow=nrow(output_data),ncol=ncol(output_data)-1)
for(i in 1:ncol(diff_data)){
k<-i+1
diff_data[,i]<-output_data[,k]-output_data[,i]
}
summary_dat<-matrix(NA,nrow=2,ncol=5)
rownames(summary_dat)<-c("MEMS","Prop. Change in M")
colnames(summary_dat)<-c("Estimate","Std. Dev.","lower 95% CI","Upper 95% CI","MC p-val")
summary_dat[1,1]<-mean(diff_data,na.rm=TRUE)
summary_dat[1,2]<-sd(diff_data,na.rm=TRUE)
if(summary_dat[1,1]<0){
summary_dat[1,5]<-length(diff_data[which(diff_data>0)])/(length(model)*nsim*ncol(diff_data))
}else{
summary_dat[1,5]<-length(diff_data[which(diff_data<0)])/(length(model)*nsim*ncol(diff_data))
}
summary_dat[1,3]<-quantile(diff_data,.025,na.rm=TRUE)
summary_dat[1,4]<-quantile(diff_data,.975,na.rm=TRUE)
prop_change<-matrix(NA,nrow=nrow(output_data),ncol=ncol(output_data)-1)
for(i in 1:ncol(prop_change)){
k<-i+1
prop_change[,i]<-(diff_data[,i]/output_data[,k])
}
prop_change<-prop_change[!is.infinite(prop_change)]
summary_dat[2,1]<-mean(prop_change,na.rm=TRUE)
message("Note that a single aMEMS is returned for all networks. To obtain distinct MEMS estimates for each network, call the MEMS function specifying a single model, rather than a list of models.")
if(full_output==FALSE){
return(summary_dat)
}else{
out_dat<-list(summary_dat=summary_dat,
output_data=output_data,
mems_samples=diff_data)
if(is.null(link_id)){
return(out_dat) #return data no controls
}else{
#return data for AMME call
out_dat<-list(out_dat_main=link_list_data,
out_dat_controls=controls_list)
return(out_dat)
}
}
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.