Gstat2_11Results | visualperception

> dat <- read.csv("kyomi.csv",header=T)
> dat
kyomi1 kyomi2 kyomi3 oya1 oya2 oya3 ses1 ses2 ses3
1 3 3 1 4 5 3 2 3 3
2 2 3 2 4 4 5 3 3 5
3 5 3 3 5 4 3 1 1 4
4 1 2 1 1 3 5 1 1 1
5 1 1 2 2 1 1 1 3 2
6 1 1 1 2 1 2 1 1 1
7 1 1 2 4 1 2 1 1 1
8 1 3 2 2 2 3 1 2 1
9 3 1 3 1 2 3 1 2 3
10 1 3 2 5 4 2 1 3 5
11 1 3 3 4 2 2 5 2 4
12 1 4 1 3 3 4 3 3 3
13 5 3 1 1 5 3 1 2 4
14 2 1 3 3 2 1 2 1 1
15 3 5 4 4 4 1 1 3 1
16 2 1 2 1 1 1 2 1 1
17 2 1 1 1 1 2 2 1 1
18 5 1 4 2 3 1 2 1 2
19 4 5 2 3 3 1 2 2 3
20 1 5 1 3 1 1 2 2 2
21 2 4 1 1 1 3 1 5 2
22 2 1 1 3 3 2 1 1 3
23 1 2 1 3 3 3 3 2 4
24 4 2 3 5 4 3 1 1 1
25 2 4 1 1 1 1 5 2 3
26 3 3 3 3 4 4 2 4 1
27 1 2 3 2 4 1 4 1 2
28 1 2 1 5 2 5 1 2 4
29 1 2 3 4 3 4 1 3 2
30 3 2 3 1 2 3 2 2 1
31 5 2 1 4 1 4 1 1 2
32 4 3 5 1 4 3 4 4 5
33 1 1 1 3 3 1 1 2 3
34 2 3 1 2 2 2 2 3 2
35 2 5 1 1 1 1 5 5 2
36 5 4 5 5 3 2 3 5 3
37 1 1 1 2 4 4 1 2 1
38 5 2 1 1 1 1 2 3 2
39 2 2 2 4 4 3 5 2 2
40 3 5 1 2 2 4 1 1 3
41 2 1 1 2 5 1 4 3 2
42 3 2 1 2 2 2 5 2 2
43 3 4 2 2 5 4 4 3 4
44 1 1 3 1 4 1 3 4 5
45 5 3 5 2 3 4 2 3 4
46 1 2 1 1 1 3 2 5 2
47 4 3 4 4 3 5 5 2 5
48 3 2 3 4 4 3 2 1 1
49 4 4 1 2 3 1 1 3 5
50 2 1 1 3 1 1 1 3 2
51 1 1 1 1 2 2 2 1 2
52 1 1 2 2 5 2 1 1 1
53 5 1 5 5 4 3 2 2 1
54 4 4 5 5 4 5 5 3 4
55 1 4 1 3 1 2 3 3 3
56 3 1 1 1 3 1 5 5 5
57 2 3 5 1 1 1 1 2 2
58 1 1 2 5 1 1 2 1 1
59 2 5 5 1 1 3 5 2 3
60 2 5 4 4 3 1 2 2 3
61 4 3 3 4 3 1 2 2 1
62 4 2 2 3 2 4 3 2 1
63 3 3 3 4 4 4 3 4 5
64 4 2 1 3 4 3 2 3 3
65 3 5 5 3 3 4 4 5 4
66 4 1 3 1 5 3 5 1 3
67 4 2 4 3 5 2 3 2 2
68 3 3 5 4 2 4 5 5 4
69 4 5 5 1 4 2 4 1 4
70 1 1 1 2 2 2 1 1 2
71 1 3 4 3 3 5 3 3 1
72 1 3 5 2 1 1 2 3 3
73 2 5 1 2 2 1 4 1 5
74 3 2 3 2 4 2 3 2 4
75 4 4 5 3 2 5 4 2 3
76 5 5 4 1 1 3 4 1 4
77 4 3 4 5 5 5 1 4 5
78 3 1 2 3 3 5 1 1 2
79 2 1 4 2 1 5 1 1 1
80 5 4 4 1 3 3 4 2 2
81 1 1 2 2 1 2 4 3 4
82 1 3 3 4 3 1 5 3 4
83 3 1 1 4 3 3 4 2 2
84 2 1 4 2 2 3 1 5 2
85 1 4 2 3 3 4 4 3 4
86 2 4 1 2 2 3 1 4 1
87 1 1 1 1 3 4 2 3 2
88 3 2 3 5 3 1 1 1 1
89 4 4 4 1 1 1 5 4 2
90 1 1 1 5 3 4 3 1 4
91 2 2 4 2 4 1 2 3 2
92 4 4 4 1 1 2 5 5 5
93 5 5 5 2 3 5 3 4 5
94 3 1 1 1 1 1 1 1 3
95 2 3 1 3 4 4 5 5 5
96 1 1 2 3 3 4 1 1 1
97 2 1 4 1 1 2 1 1 3
98 3 2 4 5 2 3 4 3 2
99 2 4 5 4 2 2 5 3 1
100 3 3 2 5 1 5 3 3 1
101 2 2 2 1 1 1 1 2 2
102 1 1 1 5 5 3 1 3 2
103 4 3 1 5 5 3 3 4 4
104 1 5 5 1 1 4 5 1 1
105 5 3 2 5 3 1 1 1 3
106 5 2 3 5 5 4 2 4 5
107 2 2 3 3 3 4 3 3 4
108 2 1 2 2 2 2 3 2 2
109 3 4 3 2 2 3 2 5 4
110 3 3 5 5 5 1 4 1 2
111 3 2 3 4 4 3 3 5 4
112 5 4 4 4 3 5 2 1 1
113 4 5 4 4 2 2 3 5 4
114 1 2 2 3 1 3 4 2 3
115 1 1 2 1 5 2 2 3 5
116 5 5 5 2 1 2 1 3 2
117 1 2 1 1 2 4 3 2 2
118 3 5 5 3 2 3 5 5 5
119 3 3 3 2 1 2 3 3 3
120 2 4 1 5 4 3 1 4 2
121 2 1 1 4 3 3 4 2 3
122 2 1 1 2 2 2 4 5 3
123 3 1 4 1 2 1 2 2 4
124 5 4 4 1 1 2 2 3 3
125 1 4 5 5 5 4 3 3 2
126 1 1 2 1 1 1 3 4 1
127 5 1 3 3 1 3 1 1 1
128 5 2 1 3 2 2 3 4 2
129 3 1 4 1 2 4 2 2 2
130 1 1 1 1 1 1 1 2 2
131 2 2 1 1 1 4 5 4 3
132 2 4 1 2 1 4 5 4 2
133 2 4 5 4 5 5 5 1 2
134 4 2 1 3 4 1 2 1 1
135 5 5 5 2 5 5 5 4 3
136 4 2 4 4 2 2 5 3 3
137 2 2 1 5 5 3 2 4 2
138 1 3 4 1 3 1 1 1 1
139 1 2 2 5 5 3 2 4 2
140 1 1 1 1 1 1 1 1 1
141 5 2 5 2 3 3 3 2 1
142 1 1 1 4 1 1 1 1 1
143 2 1 1 1 1 1 2 1 3
144 3 2 2 1 2 1 3 4 3
145 5 5 3 4 5 2 3 4 4
146 2 2 3 2 2 3 4 5 5
147 1 4 1 5 2 2 5 5 5
148 5 3 5 3 3 5 4 3 4
149 1 2 4 4 4 1 3 1 4
150 1 1 1 1 1 1 1 1 1
151 1 1 1 1 2 2 5 4 3
152 2 4 2 2 3 2 5 1 3
153 1 3 1 1 3 3 3 1 2
154 5 5 4 5 5 3 4 3 2
155 3 5 4 3 4 2 4 3 2
156 2 2 1 5 4 5 1 1 1
157 2 5 4 2 1 4 1 4 1
158 3 2 3 3 1 3 3 1 1
159 3 2 1 5 4 3 3 2 1
160 2 4 5 5 5 5 4 4 4
161 3 3 3 3 1 2 2 5 1
162 3 3 5 1 2 4 4 5 5
163 2 1 5 1 2 5 3 4 2
164 4 5 3 2 1 3 2 2 1
165 4 2 2 3 5 4 2 1 2
166 3 2 1 4 1 1 3 3 1
167 4 1 1 3 2 1 1 2 1
168 1 2 3 4 2 1 3 2 2
169 3 5 3 4 4 3 3 5 4
170 3 1 3 3 2 1 4 3 3
171 4 3 5 4 1 2 4 1 2
172 3 4 5 3 4 2 5 5 4
173 2 2 1 1 3 2 2 1 1
174 2 2 1 3 1 1 3 1 1
175 3 1 1 1 1 2 1 1 2
176 1 3 3 2 5 5 1 3 2
177 5 5 4 3 3 3 5 2 2
178 1 5 4 5 4 3 5 3 2
179 5 4 2 4 1 3 4 3 4
180 2 3 4 4 1 3 1 3 3
181 4 1 1 3 4 1 1 1 1
182 2 4 5 2 1 2 1 1 3
183 2 2 4 1 4 1 1 1 1
184 1 5 1 1 1 2 1 1 1
185 4 1 1 1 1 1 4 2 2
186 2 4 3 4 5 2 2 2 1
187 2 3 4 2 3 2 1 2 1
188 3 2 2 4 5 2 1 1 1
189 1 1 2 1 5 3 1 3 1
190 1 1 2 4 4 2 3 4 3
191 1 2 3 2 2 4 3 2 1
192 1 1 3 1 1 3 1 1 1
193 4 4 2 4 3 2 2 5 1
194 1 1 1 2 1 2 4 2 2
195 1 4 1 2 3 4 3 1 5
196 4 5 3 3 4 1 3 4 5
197 1 1 2 2 1 4 1 1 2
198 1 1 1 4 1 5 1 1 1
199 1 1 2 3 1 2 1 1 1
200 3 1 1 1 3 3 5 4 5
> attach(dat)
>
> install.packages("sem")
URL 'http://cran.ism.ac.jp/bin/macosx/el-capitan/contrib/3.4/sem_3.1-9.tgz' を試しています
Content type 'application/x-gzip' length 731830 bytes (714 KB)
==================================================
downloaded 714 KB

ダウンロードされたパッケージは、以下にあります
/var/folders/9p/213y__653jvczzlcljd7jbpr0000gp/T//Rtmp957YZs/downloaded_packages
> library(sem)
>
> # 確認的因子分析モデル
>
> model01 <- specifyEquations()
1: kyomi1 = 1*fkyomi # 測定方程式（興味）
2: kyomi2 = b2*fkyomi
3: kyomi3 = b3*fkyomi
4: oya1 = 1*foya # 測定方程式（親）
5: oya2 = b5*foya
6: oya3 = b6*foya
7: ses1 = 1*fses # 測定方程式（SES）
8: ses2 = b8*fses
9: ses3 = b9*fses
10: C(fkyomi,foya) = c1 # [興味] と [親] の相関関係（共分散）
11: C(fkyomi,fses) = c2 # [興味] と [SES] の相関関係（共分散）
12: C(foya, fses) = c3 # [親] と [SES] の相関関係（共分散）
13: V(fkyomi) = v1 # [興味] の分散
14: V(foya) = v2 # [親] の分散
15: V(fses) = v3 # [SES] の分散
16:
Read 15 items
NOTE: adding 9 variances to the model
> fit01 <- sem(model=model01,S=cov(dat),N=nrow(dat))
>
> summary(fit01,fit.indices=c("GFI","AGFI","CFI","NFI","SRMR","RMSEA","AIC"))

Model Chisquare = 23.94655 Df = 24 Pr(>Chisq) = 0.4646573
Goodness-of-fit index = 0.9740396
Adjusted goodness-of-fit index = 0.9513242
RMSEA index = 0 90% CI: (NA, 0.05728029)
Bentler-Bonett NFI = 0.9058854
Bentler CFI = 1
SRMR = 0.04321005
AIC = 65.94655

Normalized Residuals
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.2322090 -0.3184322 -0.0000001 0.0439018 0.5164741 1.5118994

R-square for Endogenous Variables
kyomi1 kyomi2 kyomi3 oya1 oya2 oya3 ses1 ses2 ses3
0.2273 0.4535 0.3444 0.3227 0.4323 0.1288 0.3580 0.3708 0.4599

Parameter Estimates
Estimate Std Error z value Pr(>|z|)
b2 1.4441531 0.31567453 4.574817 4.766371e-06 kyomi2 <--- fkyomi
b3 1.3089307 0.28948884 4.521524 6.139609e-06 kyomi3 <--- fkyomi
b5 1.1566568 0.33507580 3.451926 5.566008e-04 oya2 <--- foya
b6 0.5889487 0.17931874 3.284368 1.022114e-03 oya3 <--- foya
b8 0.9551493 0.17407662 5.486948 4.089382e-08 ses2 <--- fses
b9 1.0607953 0.19009758 5.580267 2.401497e-08 ses3 <--- fses
c1 0.1874779 0.07701446 2.434321 1.491976e-02 foya <--> fkyomi
c2 0.3164394 0.09062638 3.491691 4.799725e-04 fses <--> fkyomi
c3 0.1818841 0.08711322 2.087905 3.680640e-02 fses <--> foya
v1 0.4287303 0.15154588 2.829046 4.668695e-03 fkyomi <--> fkyomi
v2 0.6296572 0.23002986 2.737284 6.194870e-03 foya <--> foya
v3 0.7309399 0.19792951 3.692931 2.216846e-04 fses <--> fses
V[kyomi1] 1.4576765 0.17295084 8.428271 3.508096e-17 kyomi1 <--> kyomi1
V[kyomi2] 1.0776081 0.19472693 5.533945 3.131077e-08 kyomi2 <--> kyomi2
V[kyomi3] 1.3980179 0.19734743 7.084044 1.400072e-12 kyomi3 <--> kyomi3
V[oya1] 1.3216992 0.22306460 5.925186 3.119447e-09 oya1 <--> oya1
V[oya2] 1.1063288 0.26156758 4.229610 2.340969e-05 oya2 <--> oya2
V[oya3] 1.4768480 0.16543269 8.927184 4.369917e-19 oya3 <--> oya3
V[ses1] 1.3110446 0.17837897 7.349771 1.985462e-13 ses1 <--> ses1
V[ses2] 1.1317491 0.15741076 7.189782 6.489467e-13 ses2 <--> ses2
V[ses3] 0.9660257 0.16166090 5.975630 2.292029e-09 ses3 <--> ses3

Iterations = 33
>
> standardizedCoefficients(fit01)
Std. Estimate
1 0.4767321 kyomi1 <--- fkyomi
2 b2 0.6734083 kyomi2 <--- fkyomi
3 b3 0.5868917 kyomi3 <--- fkyomi
4 0.5680464 oya1 <--- foya
5 b5 0.6574792 oya2 <--- foya
6 b6 0.3589323 oya3 <--- foya
7 0.5982940 ses1 <--- fses
8 b8 0.6088996 ses2 <--- fses
9 b9 0.6781453 ses3 <--- fses
10 c1 0.3608327 foya <--> fkyomi
11 c2 0.5652721 fses <--> fkyomi
12 c3 0.2681031 fses <--> foya
13 v1 1.0000000 fkyomi <--> fkyomi
14 v2 1.0000000 foya <--> foya
15 v3 1.0000000 fses <--> fses
16 V[kyomi1] 0.7727265 kyomi1 <--> kyomi1
17 V[kyomi2] 0.5465213 kyomi2 <--> kyomi2
18 V[kyomi3] 0.6555581 kyomi3 <--> kyomi3
19 V[oya1] 0.6773233 oya1 <--> oya1
20 V[oya2] 0.5677211 oya2 <--> oya2
21 V[oya3] 0.8711676 oya3 <--> oya3
22 V[ses1] 0.6420443 ses1 <--> ses1
23 V[ses2] 0.6292413 ses2 <--> ses2
24 V[ses3] 0.5401189 ses3 <--> ses3
>
>
> # パス図の出力
> pathDiagram(fit01, ignore.double=FALSE, edge.labels="values", digits=2,standardize=TRUE)
Loading required namespace: DiagrammeR
>
> # 多重指標モデル
>
> model02 <- specifyEquations()
1: kyomi1 =　1*fkyomi # 測定方程式（興味）
2: kyomi2 = b2*fkyomi
3: kyomi3 = b3*fkyomi
4: oya1 = 1*foya # 測定方程式（親）
5: oya2 = b5*foya
6: oya3 = b6*foya
7: ses1 = 1*fses # 測定方程式（SES）
8: ses2 = b8*fses
9: ses3 = b9*fses
10: fkyomi = g1*foya + g2*fses # 構造方程式
11: C(foya,fses) = c3 # [親] と [SES] の共分散
12: V(fkyomi) = v1 # [興味] の誤差変数d1の分散
13: V(foya) = v2 # [親] の分散
14: V(fses) = v3 # [SES] の分散
15:
Read 14 items
NOTE: adding 9 variances to the model
> fit02 <- sem(model=model02,S=cov(dat),N=nrow(dat))
>
> summary(fit02,fit.indices=c("GFI","AGFI","CFI","NFI","SRMR","RMSEA","AIC"))

Normalized Residuals
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.2322082 -0.3184324 -0.0000022 0.0439019 0.5164735 1.5118986

R-square for Endogenous Variables
fkyomi kyomi1 kyomi2 kyomi3 oya1 oya2 oya3 ses1 ses2 ses3
0.3667 0.2273 0.4535 0.3444 0.3227 0.4323 0.1288 0.3580 0.3708 0.4599

Parameter Estimates
Estimate Std Error z value Pr(>|z|)
b2 1.4441556 0.31567537 4.574812 4.766469e-06 kyomi2 <--- fkyomi
b3 1.3089318 0.28948937 4.521520 6.139729e-06 kyomi3 <--- fkyomi
b5 1.1566573 0.33507604 3.451925 5.566028e-04 oya2 <--- foya
b6 0.5889492 0.17931891 3.284367 1.022116e-03 oya3 <--- foya
b8 0.9551497 0.17407677 5.486945 4.089448e-08 ses2 <--- fses
b9 1.0607956 0.19009775 5.580264 2.401539e-08 ses3 <--- fses
g1 0.1860656 0.10586104 1.757640 7.880873e-02 fkyomi <--- foya
g2 0.3866210 0.11477710 3.368451 7.559180e-04 fkyomi <--- fses
c3 0.1818841 0.08711318 2.087906 3.680635e-02 fses <--> foya
v1 0.2715043 0.10452381 2.597535 9.389543e-03 fkyomi <--> fkyomi
v2 0.6296565 0.23002970 2.737283 6.194892e-03 foya <--> foya
v3 0.7309394 0.19792947 3.692928 2.216865e-04 fses <--> fses
V[kyomi1] 1.4576780 0.17295092 8.428276 3.507956e-17 kyomi1 <--> kyomi1
V[kyomi2] 1.0776078 0.19472715 5.533937 3.131213e-08 kyomi2 <--> kyomi2
V[kyomi3] 1.3980189 0.19734752 7.084046 1.400055e-12 kyomi3 <--> kyomi3
V[oya1] 1.3216995 0.22306451 5.925190 3.119371e-09 oya1 <--> oya1
V[oya2] 1.1063291 0.26156762 4.229610 2.340966e-05 oya2 <--> oya2
V[oya3] 1.4768484 0.16543275 8.927183 4.369958e-19 oya3 <--> oya3
V[ses1] 1.3110453 0.17837901 7.349773 1.985432e-13 ses1 <--> ses1
V[ses2] 1.1317495 0.15741082 7.189782 6.489496e-13 ses2 <--> ses2
V[ses3] 0.9660261 0.16166096 5.975630 2.292030e-09 ses3 <--> ses3

Iterations = 32
>
> standardizedCoefficients(fit02)
Std. Estimate
1 0.4767315 kyomi1 <--- fkyomi
2 b2 0.6734086 kyomi2 <--- fkyomi
3 b3 0.5868915 kyomi3 <--- fkyomi
4 0.5680461 oya1 <--- foya
5 b5 0.6574791 oya2 <--- foya
6 b6 0.3589323 oya3 <--- foya
7 0.5982937 ses1 <--- fses
8 b8 0.6088995 ses2 <--- fses
9 b9 0.6781452 ses3 <--- fses
10 g1 0.2254895 fkyomi <--- foya
11 g2 0.5048177 fkyomi <--- fses
12 c3 0.2681033 fses <--> foya
13 v1 0.6332766 fkyomi <--> fkyomi
14 v2 1.0000000 foya <--> foya
15 v3 1.0000000 fses <--> fses
16 V[kyomi1] 0.7727270 kyomi1 <--> kyomi1
17 V[kyomi2] 0.5465209 kyomi2 <--> kyomi2
18 V[kyomi3] 0.6555584 kyomi3 <--> kyomi3
19 V[oya1] 0.6773236 oya1 <--> oya1
20 V[oya2] 0.5677212 oya2 <--> oya2
21 V[oya3] 0.8711676 oya3 <--> oya3
22 V[ses1] 0.6420446 ses1 <--> ses1
23 V[ses2] 0.6292414 ses2 <--> ses2
24 V[ses3] 0.5401191 ses3 <--> ses3
>
> # パス図の出力
> pathDiagram(fit02, ignore.double=FALSE, edge.labels="values", digits=2,standardize=TRUE)
>
>