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Improving model fit (MI and Score Test) #19
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library(lavaan)
#> This is lavaan 0.6-14
#> lavaan is FREE software! Please report any bugs.
HS.model <- '
visual =~ x1 + b1*x2 + b1*x3
textual =~ x4 + b2*x5 + b2*x6
speed =~ x7 + x8 + x9
'
fit <- cfa(HS.model, data=HolzingerSwineford1939)
## Modification Indices ------------------
modificationIndices(fit, sort. = TRUE, minimum.value = 10)
#> lhs op rhs mi epc sepc.lv sepc.all sepc.nox
#> 32 visual =~ x9 36.229 0.569 0.511 0.507 0.507
#> 78 x7 ~~ x8 33.738 0.547 0.547 0.883 0.883
#> 30 visual =~ x7 18.679 -0.420 -0.377 -0.347 -0.347
#> 80 x8 ~~ x9 14.953 -0.429 -0.429 -0.817 -0.817
#> 67 x4 ~~ x6 14.566 -0.212 -0.212 -0.607 -0.607
#> 66 x4 ~~ x5 11.398 0.185 0.185 0.419 0.419
## Lagrange Multiplier test --------------
lavTestScore(fit)
#> $test
#>
#> total score test:
#>
#> test X2 df p.value
#> 1 score 12.013 2 0.002
#>
#> $uni
#>
#> univariate score tests:
#>
#> lhs op rhs X2 df p.value
#> 1 .p2. == .p3. 2.877 1 0.090
#> 2 .p5. == .p6. 9.136 1 0.003
parTable(fit)[c("lhs", "op", "rhs", "label", "plabel")]
#> lhs op rhs label plabel
#> 1 visual =~ x1 .p1.
#> 2 visual =~ x2 b1 .p2.
#> 3 visual =~ x3 b1 .p3.
#> 4 textual =~ x4 .p4.
#> 5 textual =~ x5 b2 .p5.
#> 6 textual =~ x6 b2 .p6.
#> 7 speed =~ x7 .p7.
#> 8 speed =~ x8 .p8.
#> 9 speed =~ x9 .p9.
#> 10 x1 ~~ x1 .p10.
#> 11 x2 ~~ x2 .p11.
#> 12 x3 ~~ x3 .p12.
#> 13 x4 ~~ x4 .p13.
#> 14 x5 ~~ x5 .p14.
#> 15 x6 ~~ x6 .p15.
#> 16 x7 ~~ x7 .p16.
#> 17 x8 ~~ x8 .p17.
#> 18 x9 ~~ x9 .p18.
#> 19 visual ~~ visual .p19.
#> 20 textual ~~ textual .p20.
#> 21 speed ~~ speed .p21.
#> 22 visual ~~ textual .p22.
#> 23 visual ~~ speed .p23.
#> 24 textual ~~ speed .p24.
#> 25 .p2. == .p3.
#> 26 .p5. == .p6. Created on 2023-04-03 with reprex v2.0.2 |
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