Indeksy modyfikacji
Strukturalne modelowanie równań z lavaan w R
Erin Buchanan
Professor
Aktualizacja słabych modeli
User model versus baseline model:
Comparative Fit Index (CFI) 0.879
Tucker-Lewis Index (TLI) 0.774
Root Mean Square Error of Approximation:
RMSEA 0.128
90 Percent Confidence Interval 0.094 0.164
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.079
Aktualizacja słabych modeli (2)
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
visual =~
x1 1.000 0.777 0.667
x2 0.690 0.124 5.585 0.000 0.536 0.456
x3 0.985 0.160 6.157 0.000 0.766 0.678
speed =~
x7 1.000 0.622 0.572
x8 1.204 0.170 7.090 0.000 0.749 0.741
x9 1.052 0.147 7.142 0.000 0.654 0.649
Aktualizacja słabych modeli (3)
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.x1 0.754 0.110 6.838 0.000 0.754 0.555
.x2 1.094 0.103 10.661 0.000 1.094 0.792
.x3 0.688 0.105 6.557 0.000 0.688 0.540
.x7 0.796 0.082 9.756 0.000 0.796 0.673
.x8 0.461 0.077 6.002 0.000 0.461 0.451
.x9 0.587 0.071 8.273 0.000 0.587 0.578
var(HolzingerSwineford1939$x1)
1.362898
Indeksy modyfikacji
modificationindices(twofactor.fit, sort = TRUE)
lhs op rhs mi epc sepc.lv sepc.all sepc.nox
34 x7 ~~ x8 35.521 0.624 0.624 0.568 0.568
18 visual =~ x9 35.521 0.659 0.512 0.508 0.508
36 x8 ~~ x9 19.041 -0.527 -0.527 -0.517 -0.517
16 visual =~ x7 19.041 -0.503 -0.391 -0.359 -0.359
26 x1 ~~ x9 11.428 0.177 0.177 0.151 0.151
- Dodawać po jednym na raz
- Dodawać parametry, które mają sens
Aktualizacja modelu
34 x7 ~~ x8 35.521 0.624 0.624 0.568 0.568
twofactor.model <- 'visual =~ x1 + x2 + x3
speed =~ x7 + x8 + x9
x7 ~~ x8'
twofactor.fit <- cfa(model = twofactor.model,
data = HolzingerSwineford1939)
summary(twofactor.fit, standardized = TRUE,
fit.measures = TRUE)
User model versus baseline model:
Comparative Fit Index (CFI) 0.976
Tucker-Lewis Index (TLI) 0.949
Czas na ćwiczenia!
Strukturalne modelowanie równań z lavaan w R
