Gstat2_9Results | visualperception

> data01 <- read.csv(file="path.csv")
> data01
gakuryoku ict ses kyomi oya
1 90 63 13 15 17
2 70 35 11 12 6
3 48 22 9 18 12
4 50 20 13 9 10
5 57 47 11 17 6
6 46 42 3 5 3
7 41 56 10 0 2
8 53 40 1 9 12
9 60 51 16 7 7
10 83 68 9 20 8
11 100 45 8 9 7
12 78 50 6 8 10
13 75 11 10 14 7
14 100 92 19 19 14
15 69 52 12 10 8
16 58 50 14 12 14
17 47 44 10 6 6
18 54 65 12 13 5
19 100 64 6 17 19
20 51 6 10 9 13
21 51 32 18 12 9
22 37 64 13 13 6
23 76 35 9 13 6
24 73 63 14 15 8
25 100 93 8 14 16
26 50 40 18 17 11
27 72 83 20 13 14
28 44 63 15 15 18
29 49 35 0 13 9
30 71 53 10 12 19
31 84 54 11 13 11
32 43 57 13 7 6
33 72 16 10 8 17
34 53 56 15 1 16
35 43 30 13 12 11
36 77 16 8 16 2
37 46 21 11 10 4
38 31 42 12 12 19
39 91 65 9 15 11
40 31 67 9 15 20
41 43 40 5 13 7
42 52 56 7 10 15
43 46 40 16 13 17
44 58 27 2 0 6
45 60 21 12 14 18
46 68 49 4 2 1
47 19 34 12 7 14
48 46 44 9 11 7
49 69 68 13 14 8
50 54 51 13 5 10
51 56 1 13 1 7
52 50 26 3 11 7
53 68 32 10 13 17
54 84 82 13 9 19
55 49 37 11 6 8
56 68 41 7 18 8
57 78 80 10 13 0
58 93 45 13 7 4
59 60 35 6 5 3
60 45 41 15 1 0
61 32 60 18 5 14
62 37 56 9 8 9
63 45 50 5 15 8
64 58 31 12 3 6
65 24 70 8 12 5
66 80 56 12 15 9
67 54 30 14 5 1
68 77 79 20 19 7
69 32 11 11 6 13
70 53 47 19 5 3
71 46 57 6 9 6
72 73 69 7 9 16
73 64 35 6 10 1
74 49 18 4 10 9
75 100 71 14 20 14
76 66 54 13 18 6
77 91 45 6 12 18
78 88 56 13 10 13
79 74 13 7 9 3
80 43 26 20 8 6
81 87 53 7 14 2
82 93 50 5 18 11
83 60 45 12 8 9
84 44 48 3 14 16
85 49 21 11 12 8
86 47 44 6 3 13
87 10 69 7 10 10
88 49 40 16 2 19
89 75 60 13 9 17
90 74 42 7 4 6
91 88 28 2 7 9
92 30 45 6 9 16
93 64 65 12 7 11
94 50 42 20 5 18
95 57 41 2 18 11
96 47 12 8 8 11
97 67 50 12 12 10
98 69 26 12 0 5
99 62 38 4 13 6
100 73 46 8 12 11
101 32 63 7 6 10
102 64 29 16 6 12
103 76 22 0 5 4
104 70 30 0 14 7
105 98 55 13 10 18
106 29 30 16 13 9
107 64 55 20 14 13
108 54 66 11 0 16
109 63 31 14 9 15
110 76 10 0 10 6
111 58 37 13 7 10
112 60 70 9 5 7
113 55 72 3 10 3
114 48 71 12 7 20
115 33 43 12 9 13
116 65 33 3 0 12
117 31 36 14 0 13
118 50 33 8 3 6
119 42 27 2 8 10
120 39 30 5 9 5
121 64 33 5 6 5
122 47 70 17 7 8
123 60 49 14 10 17
124 44 42 9 5 9
125 78 70 6 10 13
126 57 4 4 0 3
127 79 22 3 15 14
128 73 21 18 16 12
129 58 32 3 5 0
130 37 45 4 11 18
131 33 84 12 17 9
132 100 29 1 16 12
133 80 67 14 11 15
134 26 31 11 10 12
135 72 24 13 14 16
136 57 69 14 6 13
137 48 82 13 8 10
138 100 84 11 10 12
139 69 36 9 15 9
140 56 29 0 8 5
141 58 55 15 16 13
142 43 29 10 9 5
143 89 45 15 20 15
144 47 33 5 8 12
145 58 73 10 7 10
146 50 61 10 10 2
147 52 21 6 6 5
148 47 0 6 4 1
149 90 44 14 18 13
150 52 39 9 11 9
151 51 49 2 2 2
152 81 27 7 13 7
153 83 25 5 15 6
154 43 53 8 8 6
155 56 20 0 8 13
156 58 56 1 10 10
157 70 73 13 14 8
158 12 28 20 4 19
159 52 26 10 2 6
160 73 59 15 8 11
161 80 32 14 9 7
162 91 62 20 10 14
163 43 43 8 13 9
164 85 28 16 13 9
165 18 73 11 9 17
166 50 41 13 11 13
167 60 33 18 17 6
168 37 54 9 13 5
169 43 10 4 8 5
170 84 60 14 20 8
171 47 91 4 9 11
172 55 28 12 13 0
173 60 57 20 11 16
174 53 68 10 4 11
175 57 57 16 4 2
176 70 25 12 11 11
177 63 70 5 17 7
178 5 76 20 19 15
179 60 32 15 14 13
180 39 46 9 11 6
181 74 44 7 1 3
182 78 37 7 15 4
183 100 38 16 7 14
184 71 80 7 18 11
185 56 79 8 10 17
186 53 51 6 8 2
187 67 48 9 20 14
188 56 56 10 10 6
189 61 49 8 7 14
190 49 33 17 9 6
191 74 89 11 1 12
192 46 30 14 0 17
193 82 23 7 14 5
194 73 44 9 9 16
195 78 30 9 10 10
196 19 32 7 1 0
197 67 0 13 12 6
198 18 10 7 9 0
199 65 57 10 11 14
200 84 61 12 14 18
> head(data01, n=3)
gakuryoku ict ses kyomi oya
1 90 63 13 15 17
2 70 35 11 12 6
3 48 22 9 18 12
> cov01 <- cov(data01)
> cov01
gakuryoku ict ses kyomi oya
gakuryoku 380.575276 47.84342 -1.239397 29.162010 7.501307
ict 47.843417 395.04884 20.934070 19.341307 26.191357
ses -1.239397 20.93407 23.894573 2.140352 6.646080
kyomi 29.162010 19.34131 2.140352 24.035151 4.508819
oya 7.501307 26.19136 6.646080 4.508819 25.427111
> library("sem")
> model01 <- specifyEquations()
1: gakuryoku = b1*kyomi + b2*ict
2: kyomi = b3*oya + b4*ict
3: ict = b5*ses
4:
Read 3 items
NOTE: adding 3 variances to the model
> sem01 <- sem(model=model01,S=cov01,N=nrow(data01),fixed.x=c("ses","oya"))
> summary(sem01,fit.indices=c("GFI","AGFI","CFI","NFI","RMSEA","SRMR","AIC"))

Model Chisquare = 10.3179 Df = 4 Pr(>Chisq) = 0.03539997
Goodness-of-fit index = 0.9800788
Adjusted goodness-of-fit index = 0.9252953
RMSEA index = 0.08909019 90% CI: (0.02123716, 0.1579803)
Bentler-Bonett NFI = 0.8458732
Bentler CFI = 0.8890511
SRMR = 0.05586612
AIC = 26.3179

Normalized Residuals
Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.6817 0.0000 0.0928 0.3132 0.3977 2.8621

R-square for Endogenous Variables
kyomi gakuryoku ict
0.0489 0.0954 0.0464

Parameter Estimates
Estimate Std Error z value Pr(>|z|)
b1 1.16161564 0.27329655 4.250385 2.134031e-05 gakuryoku <--- kyomi
b2 0.06423574 0.06709983 0.957316 3.384078e-01 gakuryoku <--- ict
b3 0.13619336 0.06701647 2.032237 4.212965e-02 kyomi <--- oya
b4 0.03992979 0.01700218 2.348510 1.884871e-02 kyomi <--- ict
b5 0.87610147 0.28146638 3.112633 1.854266e-03 ict <--- ses
V[kyomi] 22.64878520 2.27056204 9.974969 1.961633e-23 kyomi <--> kyomi
V[gakuryoku] 343.62697237 34.44892748 9.974969 1.961633e-23 gakuryoku <--> gakuryoku
V[ict] 376.70847447 37.76537921 9.974969 1.961633e-23 ict <--> ict

Iterations = 0
> standardizedCoefficients(sem01)
Std. Estimate
1 b1 0.29084525 gakuryoku <--- kyomi
2 b2 0.06550719 gakuryoku <--- ict
3 b3 0.14073166 kyomi <--- oya
4 b4 0.16263341 kyomi <--- ict
5 b5 0.21546595 ict <--- ses
6 V[kyomi] 0.95108561 kyomi <--> kyomi
7 V[gakuryoku] 0.90460917 gakuryoku <--> gakuryoku
8 V[ict] 0.95357442 ict <--> ict
9 1.00000000 ses <--> ses
10 0.26962953 oya <--> ses
11 1.00000000 oya <--> oya
> # パス図の出力
> pathDiagram(sem01, ignore.double=FALSE, edge.labels="values", digits=2,standardize=TRUE)
Loading required namespace: DiagrammeR
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