#this program demonstrates some of the network randomization bits and
#example models.
#Moody, 5.16.2018;
#clear everything to start…
rm(list = ls())
gc()
#load basic data manipulation bits
library(dplyr);
library(readr);
library(magrittr) #Supports pipe (%>%) commands that allow you to perform multiple operations with one statement
library(tidyr) #Additional functions for manipulating data
library(ggplot2)
#load the data
#first build the edgelist & nodelist info
setwd(“C:/jwm/SNH18wd”)
AHS_Base <- read_csv (‘ahs_wpvar.csv’,
col_names = TRUE);
AHS_adjlist %
select(ego_nid, mfnid_1:mfnid_5, ffnid_1:ffnid_5, grade, sex, commcnt) %>%
filter(commcnt==1);
AHS_Edges %
rename( id = `ego_nid`,
gender = `sex`) %>%
gather(Alter_Label, Target, mfnid_1:mfnid_5, ffnid_1:ffnid_5, na.rm = TRUE)
AHS_Edges=AHS_Edges %>% filter (Target != 99999);
AHS_Edges=AHS_Edges %>%select(id, Target);
library(“statnet”)
g=as.network(AHS_Edges)
g %v% “grade” <- AHS_adjlist$grade
g %v% “sex” <- AHS_adjlist$sex
g %v% “degree” <- degree(g)
#Lets just do some randomization checks…compare our observed to a real.
tri=triad.census(g) #The full triad census as an 16-element vector
#how transitive is our network?
obs_tran=gtrans(g)
#distribution of dyads
dyads=dyad.census(g)
dyads
#to simulate…first initialize an empty vector
ran_tran=vector(length=500)
j=1
while(j<501){
r=rguman(1,nv=71,mut=dyads[1],asym=dyads[2],null=dyads[3])
ran_tran[j]=gtrans(r)
if (is.na(ran_tran[j])) next else j=j+1 #skip bad sim cases
}
dt=as.data.frame(ran_tran)
ggplot(dt,aes(x=ran_tran))+geom_histogram()+
geom_vline(xintercept=obs_tran,
linetype=1,
color=”red”)
#Draw a highly reciprocal network
g=Pt])/1000
p
r.triad=triad.census(r)
