From 56a598d6fbc110208cc3691d401bf69c936e0015 Mon Sep 17 00:00:00 2001
From: JHart96 <jordan.da.hart@gmail.com>
Date: Tue, 18 Jun 2024 16:37:04 +0100
Subject: [PATCH] plot_network edgelist fix

---
 .Rhistory       | 562 ++++++++++++++++++++++++------------------------
 R/bison_model.R |   4 +-
 2 files changed, 283 insertions(+), 283 deletions(-)

diff --git a/.Rhistory b/.Rhistory
index a8e73a6..d7db55c 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,284 +1,3 @@
-priors <- expect_warning(bison_brm_get_prior(
-age_diff ~ bison(edge_weight(node_1_id, node_2_id)),
-list(fit_edge, fit_edge),
-list(df_sim, df_sim)
-),
-regexp=NA
-)
-fit_edge
-bison_brm_get_prior(
-age_diff ~ bison(edge_weight(node_1_id, node_2_id)),
-list(fit_edge, fit_edge),
-list(df_sim, df_sim)
-)
-devtools::load_all(".")
-priors <- expect_warning(bison_brm_get_prior(
-age_diff ~ bison(edge_weight(node_1_id, node_2_id)),
-list(fit_edge, fit_edge),
-list(df_sim, df_sim)
-),
-regexp=NA
-)
-priors
-devtools::document()
-devtools::load_all(".")
-devtools::load_all(".")
-sim_data <- simulate_bison_model("binary", aggregated = TRUE)
-df <- sim_data$df_sim
-df$node_1_id
-"A":"F"
-f$node_1_id
-node_names <- c("A", "B", "C", "D", "E", "F", "G", "H")
-df$node_1_id
-node_names[df$node_1_id]
-node_names <- sample(c("A", "B", "C", "D", "E", "F", "G", "H"))
-node_names
-node_names <- c("G", "B", "A", "D", "C", "H", "F", "E")
-node_names[df$node_1_id]
-df$node_1_id
-node_names <- c("G", "B", "A", "D", "J", "C", "H", "F", "E", "K")
-node_names[df$node_1_id]
-sim_data <- simulate_bison_model("binary", aggregated = TRUE)
-df <- sim_data$df_sim
-node_names <- c("G", "B", "A", "D", "J", "C", "H", "F", "E", "K")
-df$node_1_name <- node_names[df$node_1_id]
-df$node_2_name <- node_names[df$node_2_id]
-df
-fit_edge <- bison_model(
-(event | duration) ~ dyad(node_1_id, node_2_id),
-data=df,
-model_type="binary_conjugate",
-priors=get_default_priors("binary_conjugate")
-)
-x <- expect_warning(
-extract_metric(fit_edge, "edge_weight", num_draws=10),
-regexp=NA
-)
-expect_equal(dim(x)[1], 10)
-x
-draw_edgelist_samples(obj, num_draws)
-draw_edgelist_samples(fit_edge, 2)
-devtools::load_all(".")
-extract_metric(fit_edge, "edge_weight", num_draws=10)
-extract_metric(fit_edge, "node_strength", num_draws=50)
-fit_edge$node_to_idx
-df$node_1_name <- node_names[df$node_1_id]
-df$node_2_name <- node_names[df$node_2_id]
-fit_edge <- bison_model(
-(event | duration) ~ dyad(node_1_name, node_2_name),
-data=df,
-model_type="binary_conjugate",
-priors=get_default_priors("binary_conjugate")
-)
-draw_edgelist_samples(fit_edge, 2)
-extract_metric(fit_edge, "edge_weight", num_draws=10)
-extract_metric(fit_edge, "node_strength", num_draws=50)
-devtools::load_all(".")
-extract_metric(fit_edge, "node_strength", num_draws=50)
-sim_data <- simulate_bison_model("binary", aggregated = TRUE)
-df <- sim_data$df_sim
-node_names <- c("G", "B", "A", "D", "J", "C", "H", "F", "E", "K")
-df$node_1_name <- node_names[df$node_1_id]
-df$node_2_name <- node_names[df$node_2_id]
-fit_edge <- bison_model(
-(event | duration) ~ dyad(node_1_name, node_2_name),
-data=df,
-model_type="binary_conjugate",
-priors=get_default_priors("binary_conjugate")
-)
-draw_edgelist_samples(fit_edge, 2)
-extract_metric(fit_edge, "edge_weight", num_draws=10)
-extract_metric(fit_edge, "node_strength", num_draws=50)
-sim_data$df_sim
-df
-df_2 <- df[,c("node_1_name", "node_2_name")]
-df$weight <- df$event/df$duration
-df_2 <- df[,c("node_1_name", "node_2_name")]
-df_2$weight <- df$event/df$duration
-df_2$weight
-df_2$weight
-?igraph::from_edgelist
-net <- igraph::from_edgelist(df[, c("node_1_name", "node_2_name")])
-net <- igraph::from_edgelist(df[, c("node_1_name", "node_2_name")])
-E(net)$weight <- df$event/df$duration
-E(net)
-net <- igraph::from_edgelist(df[, c("node_1_name", "node_2_name")])
-net
-E(net)
-net
-df[, c("node_1_name", "node_2_name")]
-igraph::from_edgelist(df[, c("node_1_name", "node_2_name")])
-net <- from_edgelist(df[, c("node_1_name", "node_2_name")])
-net
-net <- graph_from_edgelist(df[, c("node_1_name", "node_2_name")])
-df[, c("node_1_name", "node_2_name")]
-raph_from_edgelist(matrix(df[, c("node_1_name", "node_2_name")]))
-graph_from_edgelist(matrix(df[, c("node_1_name", "node_2_name")]))
-matrix(df[, c("node_1_name", "node_2_name")])
-df
-df[, c("node_1_name", "node_2_name")]
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]))
-net
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net) <- df$event/df$duration
-E(net)
-df$event/df$duration
-E(net)$weight <- df$event/df$duration
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-strength(net)
-x
-net
-strength(net)
-x[1, ]
-cor(strength(net), x[1, ])
-strength(net)
-x[1, ]
-cor(strength(net), x[, 1])
-V(net)
-colnames(x)
-x
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-x[, 1]
-x[1, ]
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-x <- expect_warning(
-extract_metric(fit_edge, "node_strength", num_draws=50),
-regexp=NA
-)
-net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=FALSE)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
-net <- graph_from_edgelist(as.matrix(df[, c("node_1_name", "node_2_name")]), directed=T)
-E(net)$weight <- df$event/df$duration
-cor(strength(net), x[1, ])
 net <- graph_from_edgelist(as.matrix(df[, c("node_2_name", "node_1_name")]), directed=T)
 E(net)$weight <- df$event/df$duration
 cor(strength(net), x[1, ])
@@ -510,3 +229,284 @@ devtools::document()
 devtools::install()
 devtools::load_all(".")
 devtools::test_active_file()
+devtools::test_active_file()
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>",
+tidy.opts=list(width.cutoff=80),
+tidy=TRUE
+)
+library(bisonR)
+library(dplyr)
+sim_data <- simulate_bison_model("binary", aggregated = FALSE)
+df <- sim_data$df_sim
+head(df)
+priors <- get_default_priors("binary")
+priors
+prior_check(priors, "binary")
+priors$edge <- "normal(-1, 2.5)"
+prior_check(priors, "binary")
+fit_edge <- bison_model(
+(event | duration) ~ dyad(node_1_id, node_2_id),
+data=df,
+model_type="binary",
+priors=priors
+)
+plot_trace(fit_edge, par_ids=2)
+plot_predictions(fit_edge, num_draws=20, type="density")
+plot_predictions(fit_edge, num_draws=20, type="point")
+summary(fit_edge)
+plot_network(fit_edge, lwd=5)
+fit_null <- bison_model(
+(event | duration) ~ 1,
+data=df,
+model_type="binary",
+priors=priors
+)
+model_comparison(list(non_random_model=fit_edge, random_model=fit_null))
+df_dyadic <- df %>%
+distinct(node_1_id, node_2_id, age_diff)
+df_dyadic
+fit_dyadic <- bison_brm (
+bison(edge_weight(node_1_id, node_2_id)) ~ age_diff,
+fit_edge,
+df_dyadic,
+num_draws=5, # Small sample size for demonstration purposes
+refresh=0
+)
+summary(fit_dyadic)
+cv_samples <- extract_metric(fit_edge, "global_cv")
+head(cv_samples)
+plot(density(cv_samples))
+cv_samples <- extract_metric(fit_edge, "global_diameter")
+head(cv_samples)
+extract_metric(fit_edge, "global_diameter")
+extract_metric(fit_edge, "global_clustering")
+devtools::load_all(".")
+extract_metric(fit_edge, "global_clustering")
+extract_metric(fit_edge, "global_clustering")
+devtools::load_all(".")
+extract_metric(fit_edge, "global_clustering")
+extract_metric(fit_edge, "global_clustering")
+extract_metric(fit_edge, "global_clustering")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_clustering[0.2]")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_clustering[0.2]")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_clustering[0.2]")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_clustering[0.2]")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_clustering[0.2]")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_clustering[0.2]")
+devtools::load_all(".")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_clustering[0.2]")
+extract_metric(fit_edge, "node_clustering[0.2]")
+extract_metric(fit_edge, "node_degree[0.2]")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_degree[0.2]")
+extract_metric(fit_edge, "node_clustering[0.2]")
+devtools::load_all(".")
+extract_metric(fit_edge, "node_clustering[0.2]")
+devtools::load_all(".")
+extract_metric(fit_edge, "global_clustering[0.2]")
+fit_edge <- bison_model(
+(event | duration) ~ dyad(node_1_id, node_2_id),
+data=df,
+model_type="binary_conjugate",
+priors=get_default_priors("binary_conjugate")
+)
+```
+sim_data <- simulate_bison_model("binary", aggregated = TRUE)
+df <- sim_data$df_sim
+fit_edge <- bison_model(
+(event | duration) ~ dyad(node_1_id, node_2_id),
+data=df,
+model_type="binary_conjugate",
+priors=get_default_priors("binary_conjugate")
+)
+dim(metrics)
+dim(metrics)
+dim(metrics)
+metrics <- extract_metric(fit_edge, "edge_weight", num_draws=10)
+dim(metrics)
+metrics <- extract_metric(fit_edge, "edge_weight", num_draws=10)
+dim(metrics)
+metrics <- extract_metric(fit_edge, "edge_weight", num_draws=10)
+head(metrics)
+head(metrics[, 1:3])
+head(metrics[, :6])
+head(metrics[, 1:6])
+head(metrics[:, 1:6]) # Show first
+head(metrics) # Show first
+metrics[1:6,1:4] # Preview first few samples and nodes
+metrics[1:6,1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_strength", num_draws=10)
+metrics[1:6,1:5] # Preview first few samples and nodes
+metrics[1:6,1:5] # Preview first few samples and nodes
+extract_metric(fit_edge, "node_betweenness")
+metrics[1:6,1:5] # Preview first few samples and nodes
+extract_metric(fit_edge, "node_betweenness")
+metrics[1:6,1:5] # Preview first few samples and nodes
+extract_metric(fit_edge, "node_betweenness")
+metrics[1:6,1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_betweenness")
+metrics[1:6,1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_eigen")
+metrics[1:6,1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_degree[0.2]")
+metrics[1:6,1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_closeness")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+devtools::load_all(".")
+metrics <- extract_metric(fit_edge, "node_clustering[0.2]")
+metrics[1:6, 1:5]
+metrics <- extract_metric(fit_edge, "node_cv")
+metrics <- extract_metric(fit_edge, "global_cv")
+metrics[1:6, 1:5]
+metrics <- extract_metric(fit_edge, "global_cv")
+metrics[1:6]
+metrics <- extract_metric(fit_edge, "global_density")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_clustering[0.2]")
+metrics[1:6] # Preview first few samples
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>",
+tidy.opts=list(width.cutoff=80),
+tidy=TRUE
+)
+library(bisonR)
+library(dplyr)
+sim_data <- simulate_bison_model("binary", aggregated = TRUE)
+df <- sim_data$df_sim
+fit_edge <- bison_model(
+(event | duration) ~ dyad(node_1_id, node_2_id),
+data=df,
+model_type="binary_conjugate",
+priors=get_default_priors("binary_conjugate")
+)
+metrics <- extract_metric(fit_edge, "edge_weight", num_draws=10)
+metrics[1:6, 1:5] # Preview first few samples and edges
+metrics <- extract_metric(fit_edge, "node_strength", num_draws=10)
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_degree[0.2]")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_eigen")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_betweenness")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_closeness")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_clustering[0.2]")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "global_cv")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_density")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_std")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_diameter")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_clustering[0.2]")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_clustering[-0.5]")
+metrics[1:6] # Preview first few samples
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>",
+tidy.opts=list(width.cutoff=80),
+tidy=TRUE
+)
+library(bisonR)
+library(dplyr)
+sim_data <- simulate_bison_model("binary", aggregated = TRUE)
+df <- sim_data$df_sim
+fit_edge <- bison_model(
+(event | duration) ~ dyad(node_1_id, node_2_id),
+data=df,
+model_type="binary_conjugate",
+priors=get_default_priors("binary_conjugate")
+)
+metrics <- extract_metric(fit_edge, "edge_weight", num_draws=10)
+metrics[1:6, 1:5] # Preview first few samples and edges
+metrics <- extract_metric(fit_edge, "node_strength", num_draws=10)
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_degree[0.2]")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_eigen")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_betweenness")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_closeness")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_clustering[0.2]")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "global_cv")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_density")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_std")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_diameter")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_clustering[-0.5]")
+metrics[1:6] # Preview first few samples
+metrics # Preview first few samples
+devtools::load_all(".")
+devtools::load_all(".")
+metrics <- extract_metric(fit_edge, "global_clustering[-0.5]")
+metrics # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_clustering[0.2]")
+metrics # Preview first few samples
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>",
+tidy.opts=list(width.cutoff=80),
+tidy=TRUE
+)
+library(bisonR)
+library(dplyr)
+sim_data <- simulate_bison_model("binary", aggregated = TRUE)
+df <- sim_data$df_sim
+fit_edge <- bison_model(
+(event | duration) ~ dyad(node_1_id, node_2_id),
+data=df,
+model_type="binary_conjugate",
+priors=get_default_priors("binary_conjugate")
+)
+metrics <- extract_metric(fit_edge, "edge_weight", num_draws=10)
+metrics[1:6, 1:5] # Preview first few samples and edges
+metrics <- extract_metric(fit_edge, "node_strength", num_draws=10)
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_degree[0.2]")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_eigen")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_betweenness")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_closeness")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "node_clustering[0.2]")
+metrics[1:6, 1:5] # Preview first few samples and nodes
+metrics <- extract_metric(fit_edge, "global_cv")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_density")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_std")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_diameter")
+metrics[1:6] # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_clustering[0.2]")
+metrics # Preview first few samples
+metrics <- extract_metric(fit_edge, "global_clustering[0.2]")
+metrics[1:6] # Preview first few samples
+devtools::install()
+devtools::install()
+devtools::test()
+devtools::test()
+devtools::build_vignettes()
+remotes::install_github("JHart96/bisonR")
diff --git a/R/bison_model.R b/R/bison_model.R
index 5a4cb9b..05b2f32 100644
--- a/R/bison_model.R
+++ b/R/bison_model.R
@@ -311,8 +311,8 @@ plot_network <- function(obj, ci=0.9, lwd=2, threshold=NULL) {
     threshold_ids <- edgelist[, 4] >= threshold
   }
   net <- igraph::graph_from_edgelist(as.matrix(edgelist[threshold_ids, 1:2]), directed=obj$directed)
-  lb <- edgelist[, 3]
-  ub <- edgelist[, 5]
+  lb <- edgelist[threshold_ids, 4]
+  ub <- edgelist[threshold_ids, 5]
   coords <- igraph::layout_nicely(net)
   igraph::plot.igraph(net, edge.width=lb * lwd, layout=coords, vertex.label.color="white", vertex.color=bison_colors[1], edge.color=rgb(0, 0, 0, 1), edge.arrow.size=0)
   igraph::plot.igraph(net, edge.width=ub * lwd, layout=coords, vertex.label.color="white", vertex.color=bison_colors[1], edge.color=rgb(0, 0, 0, 0.3), edge.arrow.size=0, add=TRUE)