Christopher Moore, M.P.P.
Doctoral Student
Quantitative Methods in Education
University of Minnesota
moor0554 at umn.edu
http://www.tc.umn.edu/~moor0554/
> ##########
>
> ##Solution developed 12/30/08 attributes <-
> data.frame(NAME=nc.sids at data$NAME, ID=1:dim(nc.sids at data)[1],
> SID79=nc.sids at data$SID79) for(i in 1:6)
> print(merge(data.frame(ID=wts$neighbours[[i]]), attributes, by="ID"))
ID NAME SID79
1 17 Caswell 2
2 19 Chatham 3
3 41 Guilford 38
4 68 Orange 6
5 76 Randolph 12
6 79 Rockingham 5
ID NAME SID79
1 14 Caldwell 9
2 18 Catawba 21
3 49 Iredell 5
4 97 Wilkes NA
ID NAME SID79
1 5 Ashe 0
2 86 Surry 6
3 97 Wilkes NA
ID NAME SID79
1 62 Montgomery 8
2 77 Richmond 7
3 84 Stanly 7
4 90 Union 9
ID NAME SID79
1 3 Alleghany 3
2 95 Watauga 1
3 97 Wilkes NA
ID NAME SID79
1 12 Burke 15
2 14 Caldwell 9
3 56 McDowell 5
4 61 Mitchell 2
5 95 Watauga 1
> attributes$LAG.NEW.W=rep(NA, dim(nc.sids at data)[1]) for(i in
> 1:dim(nc.sids at data)[1]) attributes[i,"LAG.NEW.W"] <-
> round(mean(merge(data.frame(ID=wts$neighbours[[i]]), attributes,
> by="ID")$SID79, na.rm=T), 3) attributes$LAG.ORIG.W <- round(lag(wts,
> nc.sids at data$SID79), 3) attributes[,c(1,3:5)]
NAME SID79 LAG.NEW.W LAG.ORIG.W
1 Alamance 11 11.000 11.000
2 Alexander 2 11.667 8.750
3 Alleghany 3 3.000 2.000
4 Anson 4 7.750 7.750
5 Ashe 0 2.000 1.333
6 Avery 0 6.400 6.400
7 Beaufort 4 5.167 5.167
8 Bertie 5 5.200 5.200
9 Bladen 5 21.400 21.400
10 Brunswick 6 9.667 9.667
11 Buncombe 18 5.143 5.143
12 Burke 15 10.143 10.143
13 Cabarrus 20 12.800 12.800
14 Caldwell 9 7.800 6.500
15 Camden 2 2.667 2.667
16 Carteret 4 14.333 14.333
17 Caswell 2 12.800 12.800
18 Catawba 21 9.833 9.833
19 Chatham 3 12.875 12.875
20 Cherokee 1 1.500 1.500
21 Chowan 1 1.000 1.000
22 Clay 0 2.000 2.000
23 Cleveland 21 15.400 15.400
24 Columbus 17 10.000 10.000
25 Craven 18 6.000 6.000
26 Cumberland 57 9.333 9.333
27 Currituck 2 2.000 2.000
28 Dare 1 0.000 0.000
29 Davidson 8 13.429 13.429
30 Davie 3 8.000 8.000
31 Duplin 7 11.500 11.500
32 Durham 22 9.600 9.600
33 Edgecombe 9 9.800 9.800
34 Forsyth 18 9.429 9.429
35 Franklin 0 11.429 11.429
36 Gaston 26 21.000 21.000
37 Gates 2 2.400 2.400
38 Graham 1 2.000 2.000
39 Granville 4 12.600 12.600
40 Greene 4 15.250 15.250
41 Guilford 38 8.714 8.714
42 Halifax 17 3.857 3.857
43 Harnett 10 17.000 17.000
44 Haywood 8 6.500 6.500
45 Henderson 8 7.600 7.600
46 Hertford 5 3.333 3.333
47 Hoke 6 22.200 22.200
48 Hyde 0 1.250 1.250
49 Iredell 5 12.125 10.778
50 Jackson 5 4.250 4.250
51 Johnston 13 12.571 12.571
52 Jones 2 13.200 13.200
53 Lee 6 6.000 6.000
54 Lenoir 14 10.833 10.833
55 Lincoln 7 20.500 20.500
56 McDowell 5 7.333 7.333
57 Macon 3 1.800 1.800
58 Madison 2 9.000 9.000
59 Martin 1 7.667 7.667
60 Mecklenburg 35 13.400 13.400
61 Mitchell 2 2.000 2.000
62 Montgomery 8 7.286 7.286
63 Moore 5 13.889 13.889
64 Nash 7 12.143 12.143
65 New Hanover 9 4.500 4.500
66 Northampton 3 9.000 9.000
67 Onslow 23 4.000 4.000
68 Orange 6 8.400 8.400
69 Pamlico 1 11.000 11.000
70 Pasquotank 4 1.333 1.333
71 Pender 3 10.143 10.143
72 Perquimans 0 2.333 2.333
73 Person 4 8.500 8.500
74 Pitt 11 9.000 9.000
75 Polk 0 8.000 8.000
76 Randolph 12 12.167 12.167
77 Richmond 7 7.667 7.667
78 Robeson 26 20.200 20.200
79 Rockingham 5 14.800 14.800
80 Rowan 8 8.500 8.500
81 Rutherford 8 11.167 11.167
82 Sampson 4 16.857 16.857
83 Scotland 16 11.000 11.000
84 Stanly 7 9.143 9.143
85 Stokes 5 16.750 16.750
86 Surry 6 6.750 5.400
87 Swain 2 3.600 3.600
88 Transylvania 4 9.750 9.750
89 Tyrrell 0 0.000 0.000
90 Union 9 16.500 16.500
91 Vance 6 2.000 2.000
92 Wake 31 8.429 8.429
93 Warren 2 7.500 7.500
94 Washington 0 2.000 2.000
95 Watauga 1 3.000 2.250
96 Wayne 23 9.167 9.167
97 Wilkes NA 3.375 3.375
98 Wilson 13 11.167 11.167
99 Yadkin 1 8.000 6.400
100 Yancey 1 6.750 6.750
>
> ##########
>