In desmarais-lab/dnehm: A package for network event history analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(neha)

# Simulate data for NEHA
# basic data parameters
cascades <- 50
nodes <- 20
times <- 30
nties <- 25
# generate dataframe
time <- sort(rep(1:times,nodes))
node <- paste("n",as.character(rep(1:nodes,times)),sep="")
intercept <- rep(1,length(time))
covariate <- runif(length(time))-2
data_for_sim <- data.frame(
  time, node, intercept, covariate, stringsAsFactors = FALSE
)

# regression parameters
beta <- cbind(c(-2.5,.25))
rownames(beta) <- c("intercept","covariate")

# generate network effects
possible_ties <- rbind(t(combn(1:nodes,2)),t(combn(1:nodes,2))[,c(2,1)])
possible_ties <- paste(
    paste("n",possible_ties[,1],sep=""),
    paste("n",possible_ties[,2],sep=""),
    sep="_"
)
ties <- sample(possible_ties,nties)
gamma <- cbind(rep(1.5,length(ties)))
rownames(gamma) <- ties

# initiate simulated data object
simulated_data <- NULL

 # generate the data one cascade at a time
for(c in 1:cascades) {
    simulated_cascade <- simulate_neha_discrete(
        x=data_for_sim,
        node="node",
        time="time",
        beta=beta,
        gamma=gamma,
        a=-6
    )
    simulated_cascade <- data.frame(
        simulated_cascade,
        cascade=c,
        stringsAsFactors=FALSE
    )
    simulated_data <- rbind(simulated_data, simulated_cascade)
}

# infer edges
neha_results <- neha(
    simulated_data,
    node="node",
    time="time",
    event="event",
    cascade="cascade",
    covariates="covariate",
    ncore=1
 )

# estimate NEHA logistic regression
neha_estimate <- glm(
    neha_results$combined_formula,
    data=neha_results$data_for_neha,
    family=binomial
)

summary(neha_estimate)