Next, letâs build a two-factor MMOD with one latent factor for negative items (nervous, down, depressed), and the other for positive items (happy, calm):
structure2 <- list(
F1 = c('nervous', 'down', 'depressed'),
F2 = c('happy', 'calm')
)
mmod_model2 <- mxMmodModel(data=nlsy97depression,
modelName='2 Factor MMOD',
idvar='pid', timevar='occasion', structure=structure2)
#> Warning in mxMmodModel(data = nlsy97depression, modelName = "2 Factor MMOD", :
#> Missing values detected; omitting them.
mmod_fit2 <- mxRun(mmod_model2)
#> Running 2 Factor MMOD with 45 parameters
(mmod_summary2 <- summary(mmod_fit2))
#> Summary of 2 Factor MMOD
#>
#> free parameters:
#> name matrix row col Estimate
#> 1 2 Factor MMOD.A[22,16] A dnervous_1 F1_1 0.34208391
#> 2 2 Factor MMOD.A[23,16] A ddown_1 F1_1 0.44449759
#> 3 2 Factor MMOD.A[24,16] A ddepressed_1 F1_1 0.34568966
#> 4 2 Factor MMOD.A[25,17] A dhappy_1 F2_1 -0.43364599
#> 5 2 Factor MMOD.A[26,17] A dcalm_1 F2_1 -0.40726033
#> 6 2 Factor MMOD.A[27,18] A dnervous_2 F1_2 -0.15141710
#> 7 2 Factor MMOD.A[28,18] A ddown_2 F1_2 -0.29720493
#> 8 2 Factor MMOD.A[29,18] A ddepressed_2 F1_2 -0.22737277
#> 9 2 Factor MMOD.A[30,19] A dhappy_2 F2_2 -0.31078549
#> 10 2 Factor MMOD.A[31,19] A dcalm_2 F2_2 -0.23510537
#> 11 2 Factor MMOD.A[32,20] A dnervous_3 F1_3 0.27996036
#> 12 2 Factor MMOD.A[33,20] A ddown_3 F1_3 0.45297788
#> 13 2 Factor MMOD.A[34,20] A ddepressed_3 F1_3 0.35787907
#> 14 2 Factor MMOD.A[35,21] A dhappy_3 F2_3 0.46634671
#> 15 2 Factor MMOD.A[36,21] A dcalm_3 F2_3 0.38075384
#> 16 2 Factor MMOD.S[16,17] S F1_1 F2_1 0.78691979
#> 17 2 Factor MMOD.S[16,18] S F1_1 F1_2 0.06014108
#> 18 2 Factor MMOD.S[17,18] S F2_1 F1_2 0.03925727
#> 19 2 Factor MMOD.S[16,19] S F1_1 F2_2 -0.01587564
#> 20 2 Factor MMOD.S[17,19] S F2_1 F2_2 -0.01766206
#> 21 2 Factor MMOD.S[18,19] S F1_2 F2_2 -0.69546524
#> 22 2 Factor MMOD.S[16,20] S F1_1 F1_3 -0.06789042
#> 23 2 Factor MMOD.S[17,20] S F2_1 F1_3 -0.01426543
#> 24 2 Factor MMOD.S[18,20] S F1_2 F1_3 0.02309934
#> 25 2 Factor MMOD.S[19,20] S F2_2 F1_3 -0.03211301
#> 26 2 Factor MMOD.S[16,21] S F1_1 F2_3 0.04478221
#> 27 2 Factor MMOD.S[17,21] S F2_1 F2_3 0.03333399
#> 28 2 Factor MMOD.S[18,21] S F1_2 F2_3 -0.01278190
#> 29 2 Factor MMOD.S[19,21] S F2_2 F2_3 0.03144088
#> 30 2 Factor MMOD.S[20,21] S F1_3 F2_3 -0.68798195
#> 31 2 Factor MMOD.S[22,22] S dnervous_1 dnervous_1 0.16560711
#> 32 2 Factor MMOD.S[23,23] S ddown_1 ddown_1 0.07598566
#> 33 2 Factor MMOD.S[24,24] S ddepressed_1 ddepressed_1 0.10989724
#> 34 2 Factor MMOD.S[25,25] S dhappy_1 dhappy_1 0.07526441
#> 35 2 Factor MMOD.S[26,26] S dcalm_1 dcalm_1 0.10844859
#> 36 2 Factor MMOD.S[27,27] S dnervous_2 dnervous_2 0.13561792
#> 37 2 Factor MMOD.S[28,28] S ddown_2 ddown_2 0.08318044
#> 38 2 Factor MMOD.S[29,29] S ddepressed_2 ddepressed_2 0.09417501
#> 39 2 Factor MMOD.S[30,30] S dhappy_2 dhappy_2 0.06849531
#> 40 2 Factor MMOD.S[31,31] S dcalm_2 dcalm_2 0.12280189
#> 41 2 Factor MMOD.S[32,32] S dnervous_3 dnervous_3 0.38778330
#> 42 2 Factor MMOD.S[33,33] S ddown_3 ddown_3 0.27207224
#> 43 2 Factor MMOD.S[34,34] S ddepressed_3 ddepressed_3 0.29784132
#> 44 2 Factor MMOD.S[35,35] S dhappy_3 dhappy_3 0.23240185
#> 45 2 Factor MMOD.S[36,36] S dcalm_3 dcalm_3 0.34041584
#> Std.Error A
#> 1 0.006429926
#> 2 0.005817232
#> 3 0.005541267
#> 4 0.005941098
#> 5 0.006126876
#> 6 0.005821508
#> 7 0.006181826
#> 8 0.005478696
#> 9 0.006940047
#> 10 0.006347826
#> 11 0.010462995
#> 12 0.011082682
#> 13 0.009959067
#> 14 0.012211544
#> 15 0.011298501
#> 16 0.008525574
#> 17 0.017062146
#> 18 0.017225826
#> 19 0.016978516
#> 20 0.017103537
#> 21 0.016313765
#> 22 0.017904571
#> 23 0.018094869
#> 24 0.020276675
#> 25 0.020086240
#> 26 0.017996622
#> 27 0.018150032
#> 28 0.020313810
#> 29 0.020158937
#> 30 0.018799567
#> 31 0.003424771
#> 32 0.002732386
#> 33 0.002450655
#> 34 0.002980145
#> 35 0.003024774
#> 36 0.002579910
#> 37 0.002959253
#> 38 0.002264617
#> 39 0.003628395
#> 40 0.002904624
#> 41 0.007729192
#> 42 0.008747754
#> 43 0.007042538
#> 44 0.010048715
#> 45 0.008535637
#>
#> Model Statistics:
#> | Parameters | Degrees of Freedom | Fit (-2lnL units)
#> Model: 45 75 -5917.303
#> Saturated: 120 0 -6755.909
#> Independence: 15 105 25163.633
#> Number of observations/statistics: 6566/120
#>
#> chi-square: ϲ ( df=75 ) = 838.6063, p = 2.030077e-129
#> Information Criteria:
#> | df Penalty | Parameters Penalty | Sample-Size Adjusted
#> AIC: 688.6063 928.6063 929.2412
#> BIC: 179.3818 1234.1410 1091.1423
#> CFI: 0.9759982
#> TLI: 0.9663975 (also known as NNFI)
#> RMSEA: 0.039378 [95% CI (0.03654012, 0.04226008)]
#> Prob(RMSEA <= 0.05): 1
#> timestamp: 2021-05-18 11:24:22
#> Wall clock time: 0.2585125 secs
#> optimizer: SLSQP
#> OpenMx version number: 2.18.1
#> Need help? See help(mxSummary)# Note: This can take a while to draw...
semPlot::semPaths(mmod_fit2, 'est')RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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