## ----include = FALSE----------------------------------------------------------
options(rmarkdown.html_vignette.check_title = FALSE)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>", 
  out.width = "100%"
)
library(iglm)

## -----------------------------------------------------------------------------
library(iglm)

## -----------------------------------------------------------------------------
n_actors =100

attribute_info = rnorm(n_actors)
attribute_cov = diag(attribute_info)
edge_cov = outer(attribute_info, attribute_info, FUN = function(x,y){abs(x-y)})
set.seed(123)

alpha = 0.3
block <- matrix(nrow = 50, ncol = 50, data = 1)
neighborhood <- as.matrix(Matrix::bdiag(replicate(n_actors/50, block, simplify=FALSE)))

overlapping_degree = 0.5
neighborhood = matrix(nrow = n_actors, ncol = n_actors, data = 0)
block <- matrix(nrow = 5, ncol = 5, data = 0)
size_neighborhood <- 5
size_overlap <-  ceiling(size_neighborhood*overlapping_degree)

end <- floor((n_actors-size_neighborhood)/size_overlap)
for(i in 0:end){
  neighborhood[(1+size_overlap*i):(size_neighborhood+size_overlap*i), (1+size_overlap*i):(size_neighborhood+size_overlap*i)] = 1
}
neighborhood[(n_actors-size_neighborhood+1):(n_actors), (n_actors-size_neighborhood+1):(n_actors)] = 1

type_x <- "binomial"
type_y <- "binomial"
formula_beg = as.formula("xyz_obj ~ 1 ")
formula_model = as.formula("xyz_object ~ 1 ")

object = iglm.data(neighborhood = neighborhood, directed = F, type_x = type_x, type_y = type_y)

## -----------------------------------------------------------------------------
formula <- object ~ edges + attribute_y + attribute_x + popularity

## -----------------------------------------------------------------------------
# Parameters of edges(mode = "local"), attribute_y, and attribute_x
gt_coef = c(3,-1,-1)
# Parameters for popularity effect
gt_coef_pop =  c(rnorm(n = n_actors, -2, 1))
# Define the sampler
sampler_tmp = sampler.iglm(n_burn_in = 100, n_simulation = 10,
                               sampler.x = sampler.net_attr(n_proposals =  n_actors*10,seed = 13),
                               sampler.y = sampler.net_attr(n_proposals =  n_actors*10, seed = 32),
                               sampler.z = sampler.net_attr(n_proposals = sum(neighborhood>0)*10, seed = 134),
                               init_empty = F)

model_tmp_new <- iglm(formula = formula,
                           coef = gt_coef,  coef_popularity = gt_coef_pop, sampler = sampler_tmp, 
                          control = control.iglm(accelerated = F,max_it = 200, display_progress = F, var = T))

## -----------------------------------------------------------------------------
# Simulate new networks
model_tmp_new$simulate()
# Get the samples
tmp <- model_tmp_new$get_samples()

## -----------------------------------------------------------------------------
# First set the first simulated network as the target for estimation
model_tmp_new$set_target(tmp[[1]])
model_tmp_new$estimate()
model_tmp_new$iglm.data$degree_distribution(plot = TRUE)


## -----------------------------------------------------------------------------
model_tmp_new$model_assessment(formula = ~  degree_distribution + 
                                 geodesic_distances_distribution + edgewise_shared_partner_distribution + mcmc_diagnostics)
model_tmp_new$results$plot(model_assessment = T)