2. Create new edge-list file for ERGM
# Create 22 unique and sequenced IDs
id <- seq(1,22,1)
# Join these IDs to the nodes data (cbind = column bind), and reassign this object to 'nodes'
nodes<-cbind(id, nodes)
nodes
## id std_id gnd grd rce per_cap_inc
## 1 1 104456 2 10 4 4342
## 2 2 113211 2 10 1 13452
## 3 3 114144 1 10 4 13799
## 4 4 114992 1 10 4 13138
## 5 5 118466 1 10 2 8387
## 6 6 118680 2 10 4 9392
## 7 7 122713 2 10 4 12471
## 8 8 122714 1 10 1 10391
## 9 9 122723 1 10 4 17524
## 10 10 125522 1 10 4 12145
## 11 11 126101 2 10 1 8622
## 12 12 126784 2 10 3 17524
## 13 13 128033 2 10 4 11651
## 14 14 128041 1 10 4 10116
## 15 15 132942 2 10 4 12452
## 16 16 134494 1 10 4 5255
## 17 17 138966 2 10 3 7427
## 18 18 139441 2 10 3 11933
## 19 19 139596 2 10 4 8509
## 20 20 140270 1 10 4 12145
## 21 21 140271 2 10 4 9121
## 22 22 140442 1 10 3 7949
edges2<-merge(nodes[,1:2], edges, by.x = "std_id", by.y="alter_id")
head(edges2, n=10)
## std_id id ego_id sem1_friend sem2_friend sem1_wtd_dicht_seat
## 1 104456 1 132942 0 1 1
## 2 104456 1 114992 1 1 0
## 3 104456 1 126784 0 1 0
## 4 104456 1 104456 0 1 0
## 5 104456 1 139596 1 1 0
## 6 113211 2 132942 1 1 0
## 7 113211 2 126784 0 1 0
## 8 113211 2 126101 0 1 1
## 9 114144 3 118680 1 1 0
## 10 114144 3 139596 0 1 0
nrow(edges2) # 144 records
## [1] 144
# Change the 1st col name
names(edges2)[1]<-"alter_id"
names(edges2)[2]<-"alter_R_id"
edges3<- merge(nodes[,1:2], edges2, by.x = "std_id", by.y="ego_id")
names(edges3)[1]<-"ego_id"
names(edges3)[2]<-"ego_R_id"
head(edges3, n=10)
## ego_id ego_R_id alter_id alter_R_id sem1_friend sem2_friend
## 1 104456 1 125522 10 1 1
## 2 104456 1 126784 12 0 1
## 3 104456 1 139596 19 1 1
## 4 104456 1 104456 1 0 1
## 5 104456 1 122713 7 0 1
## 6 104456 1 126101 11 0 1
## 7 104456 1 132942 15 0 1
## 8 104456 1 139441 18 0 1
## 9 104456 1 118680 6 0 1
## 10 104456 1 122723 9 0 1
## sem1_wtd_dicht_seat
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 1
## 8 1
## 9 0
## 10 1
3. Create the network file and assign attributes for nodes and edges
net<-network(edges3[,c("ego_R_id", "alter_R_id")])
# Assign edge-level attributes - dyad attributes
set.edge.attribute(net, "ego_R_id", edges3[,2])
set.edge.attribute(net, "alter_R_id", edges3[,4])
# Assign node-level attributes to actors in "net"
net %v% "gender" <- nodes[,3]
net %v% "grade" <- nodes[,4]
net %v% "race" <- nodes[,5]
net %v% "pci" <- nodes[,6]
summary(net)
## Network attributes:
## vertices = 22
## directed = TRUE
## hyper = FALSE
## loops = FALSE
## multiple = FALSE
## bipartite = FALSE
## total edges = 144
## missing edges = 0
## non-missing edges = 144
## density = 0.3116883
##
## Vertex attributes:
##
## gender:
## integer valued attribute
## 22 values
##
## grade:
## integer valued attribute
## 22 values
##
## pci:
## integer valued attribute
## 22 values
##
## race:
## integer valued attribute
## 22 values
## vertex.names:
## character valued attribute
## 22 valid vertex names
##
## Edge attributes:
##
## alter_R_id:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 9.00 12.50 12.49 17.00 22.00
##
## ego_R_id:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 7.75 12.00 11.97 16.25 22.00
##
## Network edgelist matrix:
## [,1] [,2]
## [1,] 1 10
## [2,] 1 12
## [3,] 1 19
## [4,] 1 1
## [5,] 1 7
## [6,] 1 11
## [7,] 1 15
## [8,] 1 18
## [9,] 1 6
## [10,] 1 9
## [11,] 1 17
## [12,] 1 4
## [13,] 1 22
## [14,] 2 11
## [15,] 2 7
## [16,] 2 15
## [17,] 3 11
## [18,] 3 6
## [19,] 3 19
## [20,] 3 3
## [21,] 4 4
## [22,] 4 1
## [23,] 4 7
## [24,] 4 11
## [25,] 4 19
## [26,] 4 21
## [27,] 5 5
## [28,] 5 14
## [29,] 5 18
## [30,] 5 12
## [31,] 5 16
## [32,] 6 3
## [33,] 6 6
## [34,] 6 12
## [35,] 7 9
## [36,] 7 7
## [37,] 8 8
## [38,] 8 11
## [39,] 8 13
## [40,] 8 16
## [41,] 9 11
## [42,] 9 10
## [43,] 9 16
## [44,] 9 15
## [45,] 9 9
## [46,] 9 17
## [47,] 9 19
## [48,] 9 7
## [49,] 10 10
## [50,] 10 19
## [51,] 10 13
## [52,] 10 9
## [53,] 10 17
## [54,] 10 20
## [55,] 11 11
## [56,] 11 8
## [57,] 11 18
## [58,] 11 16
## [59,] 11 15
## [60,] 11 2
## [61,] 11 9
## [62,] 11 17
## [63,] 12 1
## [64,] 12 13
## [65,] 12 7
## [66,] 12 9
## [67,] 12 10
## [68,] 12 19
## [69,] 12 17
## [70,] 12 6
## [71,] 12 16
## [72,] 12 2
## [73,] 12 5
## [74,] 12 15
## [75,] 12 21
## [76,] 13 21
## [77,] 13 13
## [78,] 13 10
## [79,] 13 9
## [80,] 13 8
## [81,] 14 17
## [82,] 14 5
## [83,] 14 11
## [84,] 14 19
## [85,] 14 16
## [86,] 15 19
## [87,] 15 1
## [88,] 15 15
## [89,] 15 11
## [90,] 15 9
## [91,] 15 2
## [92,] 15 18
## [93,] 15 21
## [94,] 15 12
## [95,] 15 7
## [96,] 15 17
## [97,] 16 12
## [98,] 16 16
## [99,] 16 15
## [100,] 16 9
## [101,] 16 11
## [102,] 16 18
## [103,] 16 13
## [104,] 16 14
## [105,] 16 17
## [106,] 16 20
## [107,] 16 8
## [108,] 16 5
## [109,] 17 22
## [110,] 17 17
## [111,] 18 16
## [112,] 18 11
## [113,] 18 22
## [114,] 18 18
## [115,] 18 17
## [116,] 18 15
## [117,] 18 5
## [118,] 19 7
## [119,] 19 3
## [120,] 19 10
## [121,] 19 19
## [122,] 19 1
## [123,] 19 15
## [124,] 19 16
## [125,] 19 9
## [126,] 20 18
## [127,] 20 16
## [128,] 20 20
## [129,] 20 19
## [130,] 20 11
## [131,] 20 10
## [132,] 20 21
## [133,] 21 20
## [134,] 21 12
## [135,] 21 15
## [136,] 21 13
## [137,] 22 18
## [138,] 22 11
## [139,] 22 5
## [140,] 22 9
## [141,] 22 19
## [142,] 22 17
## [143,] 22 15
## [144,] 22 22
plot(net,displaylabels=TRUE)
4. Run the ERGM
m1<-ergm(net ~ edges + mutual + nodematch("gender") + absdiff("pci"),burnin=15000,MCMCsamplesize=30000,verbose=FALSE)
## Warning in control.ergm.toplevel(control, ...): Passing burnin to ergm(...)
## is deprecated and may be removed in a future version. Specify it as
## control.ergm(MCMC.burnin=...) instead.
## Warning in control.ergm.toplevel(control, ...): Passing MCMCsamplesize to
## ergm(...) is deprecated and may be removed in a future version. Specify it
## as control.ergm(MCMC.samplesize=...) instead.
## Starting maximum likelihood estimation via MCMLE:
## Iteration 1 of at most 20:
## The log-likelihood improved by 0.0003606
## Step length converged once. Increasing MCMC sample size.
## Iteration 2 of at most 20:
## The log-likelihood improved by 0.0002192
## Step length converged twice. Stopping.
##
## This model was fit using MCMC. To examine model diagnostics and check for degeneracy, use the mcmc.diagnostics() function.
#mcmc.diagnostics(m1)
summary(m1)
##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: net ~ edges + mutual + nodematch("gender") + absdiff("pci")
##
## Iterations: 2 out of 20
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -2.316e+00 2.205e-01 0 < 1e-04 ***
## mutual 2.413e+00 3.537e-01 0 < 1e-04 ***
## nodematch.gender 1.457e-02 1.752e-01 0 0.933756
## absdiff.pci 1.115e-04 3.061e-05 0 0.000299 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Null Deviance: 640.5 on 462 degrees of freedom
## Residual Deviance: 468.6 on 458 degrees of freedom
##
## AIC: 476.6 BIC: 493.1 (Smaller is better.)
# show the exp() for the ERGM coefficients
lapply(m1[1],exp)
## $coef
## edges mutual nodematch.gender absdiff.pci
## 0.09870989 11.16682098 1.01467728 1.00011154