An extension of lavaan::cfa().
Usage
CFA(
data,
model = "A =~ a[1:5]; B =~ b[c(1,3,5)]; C =~ c1 + c2 + c3",
estimator = "ML",
highorder = "",
orthogonal = FALSE,
missing = "listwise",
digits = 3,
file = NULL
)Arguments
- data
Data frame.
- model
Model formula. See examples.
- estimator
The estimator to be used (for details, see lavaan options). Defaults to
"ML". Can be one of the following:"ML"Maximum Likelihood (can be extended to
"MLM","MLMV","MLMVS","MLF", or"MLR"for robust standard errors and robust test statistics)"GLS"Generalized Least Squares
"WLS"Weighted Least Squares
"ULS"Unweighted Least Squares
"DWLS"Diagonally Weighted Least Squares
"DLS"Distributionally-weighted Least Squares
- highorder
High-order factor. Defaults to
"".- orthogonal
Defaults to
FALSE. IfTRUE, all covariances among latent variables are set to zero.- missing
Defaults to
"listwise". Alternative is"fiml"("Full Information Maximum Likelihood").- digits
Number of decimal places of output. Defaults to
3.- file
File name of MS Word (
.doc).
Value
A list of results returned by lavaan::cfa().
Examples
data.cfa=lavaan::HolzingerSwineford1939
CFA(data.cfa, "Visual =~ x[1:3]; Textual =~ x[c(4,5,6)]; Speed =~ x7 + x8 + x9")
#>
#> Model Syntax (lavaan):
#> Visual =~ x1 + x2 + x3
#> Textual =~ x4 + x5 + x6
#> Speed =~ x7 + x8 + x9
#>
#> Fit Measures (lavaan):
#> χ²(24, N = 301) = 85.306, p = 9e-09 ***
#> χ²/df = 3.554
#> AIC = 7517.490 (Akaike Information Criterion)
#> BIC = 7595.339 (Bayesian Information Criterion)
#> CFI = 0.931 (Comparative Fit Index)
#> TLI = 0.896 (Tucker-Lewis Index; Non-Normed Fit Index, NNFI)
#> NFI = 0.907 (Normed Fit Index)
#> IFI = 0.931 (Incremental Fit Index)
#> GFI = 0.943 (Goodness-of-Fit Index)
#> AGFI = 0.894 (Adjusted Goodness-of-Fit Index)
#> RMSEA = 0.092, 90% CI [0.071, 0.114] (Root Mean Square Error of Approximation)
#> SRMR = 0.065 (Standardized Root Mean Square Residual)
#>
#> Model Estimates (lavaan):
#> ──────────────────────────────────────────────────────────────────────
#> Estimate S.E. z p LLCI ULCI Beta
#> ──────────────────────────────────────────────────────────────────────
#> Latent Variables:
#> Visual =~ x1 0.900 (0.081) 11.128 <.001 *** 0.741 1.058 0.772
#> Visual =~ x2 0.498 (0.077) 6.429 <.001 *** 0.346 0.650 0.424
#> Visual =~ x3 0.656 (0.074) 8.817 <.001 *** 0.510 0.802 0.581
#> Textual =~ x4 0.990 (0.057) 17.474 <.001 *** 0.879 1.101 0.852
#> Textual =~ x5 1.102 (0.063) 17.576 <.001 *** 0.979 1.224 0.855
#> Textual =~ x6 0.917 (0.054) 17.082 <.001 *** 0.811 1.022 0.838
#> Speed =~ x7 0.619 (0.070) 8.903 <.001 *** 0.483 0.756 0.570
#> Speed =~ x8 0.731 (0.066) 11.090 <.001 *** 0.602 0.860 0.723
#> Speed =~ x9 0.670 (0.065) 10.305 <.001 *** 0.543 0.797 0.665
#> ──────────────────────────────────────────────────────────────────────
#> Note. Raw (Standard) Confidence Interval (CI) and SE.
#>
#> Estimator: ML
#>
CFA(data.cfa, model="
Visual =~ x[1:3]
Textual =~ x[c(4,5,6)]
Speed =~ x7 + x8 + x9
", highorder="Ability")
#>
#> Model Syntax (lavaan):
#> Visual =~ x1 + x2 + x3
#> Textual =~ x4 + x5 + x6
#> Speed =~ x7 + x8 + x9
#> Ability =~ Visual + Textual + Speed
#> Ability ~~ Ability
#>
#> Fit Measures (lavaan):
#> χ²(24, N = 301) = 85.306, p = 9e-09 ***
#> χ²/df = 3.554
#> AIC = 7517.490 (Akaike Information Criterion)
#> BIC = 7595.339 (Bayesian Information Criterion)
#> CFI = 0.931 (Comparative Fit Index)
#> TLI = 0.896 (Tucker-Lewis Index; Non-Normed Fit Index, NNFI)
#> NFI = 0.907 (Normed Fit Index)
#> IFI = 0.931 (Incremental Fit Index)
#> GFI = 0.943 (Goodness-of-Fit Index)
#> AGFI = 0.894 (Adjusted Goodness-of-Fit Index)
#> RMSEA = 0.092, 90% CI [0.071, 0.114] (Root Mean Square Error of Approximation)
#> SRMR = 0.065 (Standardized Root Mean Square Residual)
#>
#> Model Estimates (lavaan):
#> ───────────────────────────────────────────────────────────────────────────
#> Estimate S.E. z p LLCI ULCI Beta
#> ───────────────────────────────────────────────────────────────────────────
#> Latent Variables:
#> Visual =~ x1 0.439 (0.194) 2.257 .024 * 0.058 0.819 0.772
#> Visual =~ x2 0.243 (0.108) 2.253 .024 * 0.032 0.454 0.424
#> Visual =~ x3 0.320 (0.138) 2.326 .020 * 0.050 0.589 0.581
#> Textual =~ x4 0.842 (0.064) 13.251 <.001 *** 0.718 0.967 0.852
#> Textual =~ x5 0.937 (0.071) 13.293 <.001 *** 0.799 1.076 0.855
#> Textual =~ x6 0.780 (0.060) 13.084 <.001 *** 0.663 0.897 0.838
#> Speed =~ x7 0.522 (0.066) 7.908 <.001 *** 0.392 0.651 0.570
#> Speed =~ x8 0.616 (0.067) 9.129 <.001 *** 0.484 0.748 0.723
#> Speed =~ x9 0.564 (0.064) 8.808 <.001 *** 0.439 0.690 0.665
#> Ability =~ Visual 1.791 (0.990) 1.809 .070 . -0.149 3.732 0.873
#> Ability =~ Textual 0.617 (0.129) 4.798 <.001 *** 0.365 0.869 0.525
#> Ability =~ Speed 0.640 (0.143) 4.489 <.001 *** 0.360 0.919 0.539
#> ───────────────────────────────────────────────────────────────────────────
#> Note. Raw (Standard) Confidence Interval (CI) and SE.
#>
#> Estimator: ML
#>
data.bfi = na.omit(psych::bfi)
CFA(data.bfi, "E =~ E[1:5]; A =~ A[1:5]; C =~ C[1:5]; N =~ N[1:5]; O =~ O[1:5]")
#>
#> Model Syntax (lavaan):
#> E =~ E1 + E2 + E3 + E4 + E5
#> A =~ A1 + A2 + A3 + A4 + A5
#> C =~ C1 + C2 + C3 + C4 + C5
#> N =~ N1 + N2 + N3 + N4 + N5
#> O =~ O1 + O2 + O3 + O4 + O5
#>
#> Fit Measures (lavaan):
#> χ²(265, N = 2236) = 3843.296, p < 1e-99 ***
#> χ²/df = 14.503
#> AIC = 182698.556 (Akaike Information Criterion)
#> BIC = 183041.303 (Bayesian Information Criterion)
#> CFI = 0.780 (Comparative Fit Index)
#> TLI = 0.751 (Tucker-Lewis Index; Non-Normed Fit Index, NNFI)
#> NFI = 0.768 (Normed Fit Index)
#> IFI = 0.780 (Incremental Fit Index)
#> GFI = 0.862 (Goodness-of-Fit Index)
#> AGFI = 0.830 (Adjusted Goodness-of-Fit Index)
#> RMSEA = 0.078, 90% CI [0.076, 0.080] (Root Mean Square Error of Approximation)
#> SRMR = 0.076 (Standardized Root Mean Square Residual)
#>
#> Model Estimates (lavaan):
#> ──────────────────────────────────────────────────────────────────────────
#> Estimate S.E. z p LLCI ULCI Beta
#> ──────────────────────────────────────────────────────────────────────────
#> Latent Variables:
#> E =~ E1 0.907 (0.035) 26.094 <.001 *** 0.838 0.975 0.560
#> E =~ E2 1.114 (0.033) 33.914 <.001 *** 1.049 1.178 0.694
#> E =~ E3 -0.866 (0.028) -30.936 <.001 *** -0.920 -0.811 -0.645
#> E =~ E4 -1.027 (0.030) -34.530 <.001 *** -1.085 -0.968 -0.704
#> E =~ E5 -0.746 (0.029) -26.147 <.001 *** -0.802 -0.691 -0.561
#> A =~ A1 0.460 (0.032) 14.328 <.001 *** 0.397 0.523 0.330
#> A =~ A2 -0.734 (0.025) -29.758 <.001 *** -0.782 -0.685 -0.634
#> A =~ A3 -0.955 (0.027) -35.954 <.001 *** -1.008 -0.903 -0.741
#> A =~ A4 -0.728 (0.032) -22.664 <.001 *** -0.791 -0.665 -0.503
#> A =~ A5 -0.852 (0.026) -32.282 <.001 *** -0.903 -0.800 -0.678
#> C =~ C1 0.652 (0.028) 23.697 <.001 *** 0.598 0.706 0.536
#> C =~ C2 0.758 (0.029) 25.826 <.001 *** 0.700 0.815 0.578
#> C =~ C3 0.707 (0.029) 24.376 <.001 *** 0.650 0.764 0.550
#> C =~ C4 -0.949 (0.030) -32.012 <.001 *** -1.008 -0.891 -0.697
#> C =~ C5 -1.014 (0.036) -28.108 <.001 *** -1.084 -0.943 -0.622
#> N =~ N1 1.284 (0.029) 43.743 <.001 *** 1.227 1.342 0.821
#> N =~ N2 1.222 (0.029) 41.954 <.001 *** 1.165 1.279 0.796
#> N =~ N3 1.152 (0.031) 36.823 <.001 *** 1.091 1.214 0.722
#> N =~ N4 0.891 (0.033) 27.399 <.001 *** 0.827 0.955 0.571
#> N =~ N5 0.825 (0.035) 23.897 <.001 *** 0.757 0.892 0.509
#> O =~ O1 0.629 (0.027) 23.245 <.001 *** 0.576 0.682 0.562
#> O =~ O2 -0.666 (0.038) -17.641 <.001 *** -0.740 -0.592 -0.431
#> O =~ O3 0.861 (0.029) 29.360 <.001 *** 0.804 0.919 0.722
#> O =~ O4 0.260 (0.029) 8.850 <.001 *** 0.203 0.318 0.221
#> O =~ O5 -0.633 (0.032) -19.604 <.001 *** -0.696 -0.570 -0.476
#> ──────────────────────────────────────────────────────────────────────────
#> Note. Raw (Standard) Confidence Interval (CI) and SE.
#>
#> Estimator: ML
#>