Nothing
#
# This function estimates the MEMS using REM with parametric algorithm
#
#
# model is the REM 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
#time interval is an optional parameter to be used with rem.dyad models or GLM/GLMER models for REM data. Time interval tells which time points to compute the MEMS. Two options are available: aggregate or a continuous vector of numeric values. If "aggregate" is specified, a single MEMS is calculated by creating an aggregate cross-sectional representation of the event sequence treating the final observed time of event activity as the limiting condition for simulated event sequences. If a continuous interval is specified of the form c(0,2, 3), unique networks are created containing all ties that occur between those intervals. For example, if c(1,2,5) is specified, unique networks are constructed using all ties that occur between the 1 and 2 time periods and the 2 and 5 time periods, and the MEMS is computed over those two networks. If unspecified, defaults to all time periods.
#full_output tells R whether to return simulated distribution in addition to summary statistic
#edgelist is an optional parameter only used with rem.dyad models when the time interval is left unspecified or set to "aggregate". In this case, the edgelist should be a 3 column matrix used as the dependent variable for rem.dyad. The first column contains the time variable, the second column the senders, and the third column the receivers.
MEMS_rem_param <- function(model=model,
micro_process=micro_process,
macro_function=macro_function,
object_type=object_type,
interval=interval,
nsim=nsim,
silent=silent,
full_output=full_output,
time_interval=time_interval,
covar_list=covar_list,
edgelist=edgelist,
algorithm=algorithm,
mediator=mediator,
link_id=link_id,
controls=controls,
control_functions=control_functions) {
if(is.null(time_interval)){
time_interval<-"aggregate"
}
if(is.null(edgelist) &"aggregate"%in%time_interval){
stop("An edgelist must be provided if time interval is left null or aggregating over all events.")
}
time_interval<-unique(time_interval)
if(length(time_interval)>20){
message("More than 10 time intervals specified. Long run times are possible.")
}
aMEMS_tracer<-0
coef<-model$coef
cov_mat<-model$cov
if(any(is.na(coef))|
any(is.infinite(coef))){
stop("Infinite or missing values in parameter estimates. Algorithm cannot continue.")
}
if(any(is.na(cov_mat))|
any(is.infinite(cov_mat))){
stop("Infinite or missing values in covariance matrix estimates. Algorithm cannot continue.")
}
theta<-MASS::mvrnorm(n=nsim,
mu=coef,
Sigma=cov_mat,
empirical = TRUE)
interval<-sort(interval) #order from lowest to highest
##create time interval windows
if(length(time_interval)==1){
if(time_interval=="aggregate"){
time_interval<-c(0,max(edgelist[,1]))
}else{
time_interval<-c(0,time_interval)
}
}
#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
}
}
interval_lengths<-length(time_interval)-1
output_list<-list()
for(i in 1:interval_lengths){
output_list[[i]]<-matrix(NA,nrow=nsim,ncol=length(interval))
}
##start simulation--loop over each time interval
message("Computing MEMS over ",interval[1],"-",interval[length(interval)]," interval")
for(entry in 1:length(output_list)){
future_interval<-time_interval[entry+1]
#simulate values for j parameters
for(j in 1:nrow(theta)){
#create networks for k intervals
net_list<-list()
for(i in 1:length(interval)){
theta2<-theta[j,]
theta2[micro_process]<-theta2[micro_process]*interval[i]
if(is.null(covar_list)){ #include covariates if provided
sim_sequence<-simulate(model,coef=theta2)
}else{
sim_sequence<-simulate(model,coef=theta2,covar=covar_list)
}
sim_sequence<-sim_sequence[which(sim_sequence[,1]<=time_interval[time_interval==future_interval] &
sim_sequence[,1]>=time_interval[time_interval==time_interval[entry]]),]
#create network
el<-as.matrix(sim_sequence[,-c(1)])
net_list[[i]]<- as.network(matrix(0, nrow=model$n, ncol=model$n))
net_list[[i]]<-network::add.edges(net_list[[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[[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
}
}else{
#for igraph functions
for(i in 1:length(interval)){
net_list[[i]]<-intergraph::asIgraph(net_list[[i]])
b<-macro_function(net_list[[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[[i]]<-intergraph::asNetwork(net_list[[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[[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[[i]])
b<-control_functions[[control]](net_list[[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[[i]]<-intergraph::asNetwork(net_list[[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 time_interval ", entry))
}
}
}
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)])/(interval_lengths*nsim*ncol(diff_data))
}else{
summary_dat[1,5]<-length(diff_data[which(diff_data<0)])/(interval_lengths*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.