SachaEpskamp

# FIML multilevel
# Model described in https://psyarxiv.com/8ha93/
# See also http://statmodel.com/bmuthen/articles/Article_055.pdf

# Load packages:
library("lavaan")
library("psychonetrics")
library("bootnet")
library("mvtnorm")
library("qgraph")

SachaEpskamp

library("qgraph")

# https://twitter.com/EikoFried/status/1208502815099932685
# Symposia:
Symposia <- list(
  # 1. 
  centrality = c("Eiko Fried", "Ciaran O'Driscoll", "Joshua Buckman", "Donald Robinaugh", "Teague Henry", "Laura Bringmann"),
  
  computational = c("Julian Burger", "Donald Robinaugh", "Lucy Robinson", "Jonas Haslbeck", "Teague Henry", "Sacha Epskamp"),
  

SachaEpskamp

\documentclass{article}

\usepackage[
    paperwidth=27cm,paperheight=13cm,
    margin=1cm,
    ]{geometry}
    
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}

SachaEpskamp

library("bootnet")
library("mvtnorm")
library("qgraph")

# Sample size:
n <- 40000

# Generate 10-node chain graph with positive edges:
net <- genGGM(10, propPositive = 1, constant = 1.1)

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library("SEset")
library("qgraph")
library("pcalg")

# For true DAG:
A <- matrix(c(
  0,0.25,0.25,
  0,0,0,
  0,0,0
),3,3,byrow=TRUE)

SachaEpskamp

library("SEset")
library("qgraph")
library("pcalg")

# For true DAG:
A <- matrix(c(
  0,0,0,
  0.25,0,0,
  0,0.25,0
),3,3,byrow=TRUE)

SachaEpskamp

library("parSim")

parSim(
  ### SIMULATION CONDITIONS
  # Vary sample size:
  sampleSize = c(250, 500, 1000),
  
  # Vary missingness:
  missing = c(0, 0.1, 0.25),
  

SachaEpskamp

library("parSim")

parSim(
  ### SIMULATION CONDITIONS
  # Vary sample size:
  sampleSize = c(250, 500, 1000),
  
  # Vary missingness:
  missing = c(0, 0.1, 0.25),
  

SachaEpskamp

compareNetworks <- function(true,est, directed = FALSE){
  cor0 <- function(x,y,...){
    if (sum(!is.na(x)) < 2 || sum(!is.na(y)) < 2 || sd(x,na.rm=TRUE)==0 | sd(y,na.rm=TRUE) == 0){
      return(0)
    } else {
      return(cor(x,y,...))
    }
  }
  
  bias <- function(x,y) mean(abs(x-y),na.rm=TRUE)

SachaEpskamp

# Install from github:
devtools::install_github("sachaepskamp/qgraph")
library("qgraph")

# Example network to play with:
# Load data:
library("psychTools")
data(bfi)

# Compute polychoric correlations: