vignettes/Tabulating_results.Rmd
Tabulating_results.RmdtidySEM tabulates the results of different types of
models in the same uniform way. This facilitates parsing the output into
Tables and Figures for publication. The function to tabulate output is
table_results(). The function to tabulate fit indices is
table_fit().
Let’s use a classic lavaan tutorial example for a
multiple group model, using the HolzingerSwineford1939
data. The tidySEM package has a function
measurement() to generate measurement models automatically.
It guesses which latent variable an observed variable belongs to by
splitting the names (by default, at the last _ symbol), so
it helps to rename the variables:
df <- HolzingerSwineford1939
names(df)[7:15] <- paste0(rep(c("vis", "tex", "spe"), each = 3), "_", rep(1:3, 3))
df |>
subset(select = c("school", "vis_1", "vis_2", "vis_3", "tex_1", "tex_2", "tex_3", "spe_1",
"spe_2", "spe_3")) -> dfNow, let’s construct the model.
df |>
tidy_sem() |>
measurement() -> modelNow, let’s run the model in lavaan. You can either use
lavaan to run it,
model |>
estimate_lavaan() -> fit_lavThe results can be tabulated using table_results():
table_results(fit_lav)
#> label est_sig se pval confint
#> 1 vis.BY.vis_1 1.00 0.00 <NA> [1.00, 1.00]
#> 2 vis.BY.vis_2 0.55*** 0.10 0.00 [0.36, 0.75]
#> 3 vis.BY.vis_3 0.73*** 0.11 0.00 [0.52, 0.94]
#> 4 tex.BY.tex_1 1.00 0.00 <NA> [1.00, 1.00]
#> 5 tex.BY.tex_2 1.11*** 0.07 0.00 [0.98, 1.24]
#> 6 tex.BY.tex_3 0.93*** 0.06 0.00 [0.82, 1.03]
#> 7 spe.BY.spe_1 1.00 0.00 <NA> [1.00, 1.00]
#> 8 spe.BY.spe_2 1.18*** 0.16 0.00 [0.86, 1.50]
#> 9 spe.BY.spe_3 1.08*** 0.15 0.00 [0.79, 1.38]
#> 10 Variances.vis_1 0.55*** 0.11 0.00 [0.33, 0.77]
#> 11 Variances.vis_2 1.13*** 0.10 0.00 [0.93, 1.33]
#> 12 Variances.vis_3 0.84*** 0.09 0.00 [0.67, 1.02]
#> 13 Variances.tex_1 0.37*** 0.05 0.00 [0.28, 0.46]
#> 14 Variances.tex_2 0.45*** 0.06 0.00 [0.33, 0.56]
#> 15 Variances.tex_3 0.36*** 0.04 0.00 [0.27, 0.44]
#> 16 Variances.spe_1 0.80*** 0.08 0.00 [0.64, 0.96]
#> 17 Variances.spe_2 0.49*** 0.07 0.00 [0.34, 0.63]
#> 18 Variances.spe_3 0.57*** 0.07 0.00 [0.43, 0.70]
#> 19 Variances.vis 0.81*** 0.15 0.00 [0.52, 1.09]
#> 20 Variances.tex 0.98*** 0.11 0.00 [0.76, 1.20]
#> 21 Variances.spe 0.38*** 0.09 0.00 [0.21, 0.55]
#> 22 vis.WITH.tex 0.41*** 0.07 0.00 [0.26, 0.55]
#> 23 vis.WITH.spe 0.26*** 0.06 0.00 [0.15, 0.37]
#> 24 tex.WITH.spe 0.17*** 0.05 0.00 [0.08, 0.27]
#> 25 Means.vis_1 4.94*** 0.07 0.00 [4.80, 5.07]
#> 26 Means.vis_2 6.09*** 0.07 0.00 [5.96, 6.22]
#> 27 Means.vis_3 2.25*** 0.07 0.00 [2.12, 2.38]
#> 28 Means.tex_1 3.06*** 0.07 0.00 [2.93, 3.19]
#> 29 Means.tex_2 4.34*** 0.07 0.00 [4.19, 4.49]
#> 30 Means.tex_3 2.19*** 0.06 0.00 [2.06, 2.31]
#> 31 Means.spe_1 4.19*** 0.06 0.00 [4.06, 4.31]
#> 32 Means.spe_2 5.53*** 0.06 0.00 [5.41, 5.64]
#> 33 Means.spe_3 5.37*** 0.06 0.00 [5.26, 5.49]
#> 34 Means.vis 0.00 0.00 <NA> [0.00, 0.00]
#> 35 Means.tex 0.00 0.00 <NA> [0.00, 0.00]
#> 36 Means.spe 0.00 0.00 <NA> [0.00, 0.00]
table_fit(fit_lav)
#> Name Parameters fmin chisq df pvalue baseline.chisq
#> 1 fit_lav 30 0.1417035 85.30552 24 8.502553e-09 918.8516
#> baseline.df baseline.pvalue cfi tli nnfi rfi nfi
#> 1 36 0 0.9305597 0.8958395 0.8958395 0.8607411 0.9071607
#> pnfi ifi rni LL unrestricted.logl aic bic
#> 1 0.6047738 0.9314908 0.9305597 -3737.745 -3695.092 7535.49 7646.703
#> n bic2 rmsea rmsea.ci.lower rmsea.ci.upper rmsea.ci.level
#> 1 301 7551.56 0.09212148 0.07141849 0.113678 0.9
#> rmsea.pvalue rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
#> 1 0.0006612368 0.05 0.8395529 0.08
#> rmr rmr_nomean srmr srmr_bentler srmr_bentler_nomean crmr
#> 1 0.07502369 0.08218433 0.0595238 0.0595238 0.06520506 0.06520506
#> crmr_nomean srmr_mplus srmr_mplus_nomean cn_05 cn_01 gfi agfi
#> 1 0.07290147 0.0595238 0.06520506 129.4902 152.654 0.9960745 0.9911676
#> pgfi mfi ecvi
#> 1 0.4426998 0.9031773 0.4827426Now, we’ll reproduce the same analysis in ‘OpenMx’. First, we run the model:
model |>
estimate_mx() -> fit_mx
table_results(fit_mx)
table_fit(fit_mx)#> Running model with 30 parameters
#> label est_sig se pval confint
#> 1 Loadings.vis.BY.vis_1 1.00 <NA> <NA> <NA>
#> 2 Loadings.vis.BY.vis_2 0.55*** 0.11 0.00 [0.34, 0.77]
#> 3 Loadings.vis.BY.vis_3 0.73*** 0.12 0.00 [0.50, 0.96]
#> 4 Loadings.tex.BY.tex_1 1.00 <NA> <NA> <NA>
#> 5 Loadings.tex.BY.tex_2 1.11*** 0.06 0.00 [0.99, 1.24]
#> 6 Loadings.tex.BY.tex_3 0.93*** 0.06 0.00 [0.82, 1.04]
#> 7 Loadings.spe.BY.spe_1 1.00 <NA> <NA> <NA>
#> 8 Loadings.spe.BY.spe_2 1.18*** 0.15 0.00 [0.89, 1.47]
#> 9 Loadings.spe.BY.spe_3 1.08*** 0.20 0.00 [0.70, 1.46]
#> 10 Means.vis_1 4.94*** 0.07 0.00 [4.80, 5.07]
#> 11 Means.vis_2 6.09*** 0.07 0.00 [5.96, 6.22]
#> 12 Means.vis_3 2.25*** 0.07 0.00 [2.12, 2.38]
#> 13 Means.tex_1 3.06*** 0.07 0.00 [2.93, 3.19]
#> 14 Means.tex_2 4.34*** 0.07 0.00 [4.19, 4.49]
#> 15 Means.tex_3 2.19*** 0.06 0.00 [2.06, 2.31]
#> 16 Means.spe_1 4.19*** 0.06 0.00 [4.06, 4.31]
#> 17 Means.spe_2 5.53*** 0.06 0.00 [5.41, 5.64]
#> 18 Means.spe_3 5.37*** 0.06 0.00 [5.26, 5.49]
#> 19 Variances.vis_1 0.55*** 0.12 0.00 [0.32, 0.78]
#> 20 Variances.vis 0.81*** 0.15 0.00 [0.52, 1.10]
#> 21 Covariances.vis.WITH.tex 0.41*** 0.08 0.00 [0.25, 0.56]
#> 22 Covariances.vis.WITH.spe 0.26*** 0.06 0.00 [0.15, 0.37]
#> 23 Variances.tex 0.98*** 0.11 0.00 [0.76, 1.20]
#> 24 Covariances.tex.WITH.spe 0.17*** 0.05 0.00 [0.08, 0.27]
#> 25 Variances.spe 0.38*** 0.09 0.00 [0.20, 0.56]
#> 26 Variances.vis_2 1.13*** 0.10 0.00 [0.93, 1.34]
#> 27 Variances.vis_3 0.84*** 0.10 0.00 [0.66, 1.03]
#> 28 Variances.tex_1 0.37*** 0.05 0.00 [0.28, 0.47]
#> 29 Variances.tex_2 0.45*** 0.06 0.00 [0.33, 0.56]
#> 30 Variances.tex_3 0.36*** 0.04 0.00 [0.27, 0.44]
#> 31 Variances.spe_1 0.80*** 0.09 0.00 [0.63, 0.97]
#> 32 Variances.spe_2 0.49*** 0.09 0.00 [0.31, 0.67]
#> 33 Variances.spe_3 0.57*** 0.09 0.00 [0.39, 0.74]
#> Minus2LogLikelihood n Parameters observedStatistics df saturatedDoF
#> 1 7475.49 301 30 2709 2679 2655
#> independenceDoF saturatedParameters independenceParameters ChiDoF satDoF
#> 1 2691 54 18 24 2655
#> indDoF RMSEANull modelName AIC BIC saBIC LL
#> 1 2691 0.05 model 7535.49 7646.703 7551.56 -3737.745Now, we’ll reproduce the same analysis in ‘Mplus’. To illustrate the
fact that tidySEM is compatible with existing solutions, we
will specify the syntax for this example manually, using the package
MplusAutomation. This code will only work on your machine
if you have Mplus installed and R can find it. First, we run the
model:
fit_mplus <- mplusModeler(mplusObject(VARIABLE = "grouping IS school (1 = GW 2 = Pas);",
MODEL = c("visual BY vis_1 vis_2 vis_3;",
"textual BY tex_1 tex_2 tex_3;",
"speed BY spe_1 spe_2 spe_3;"),
usevariables = names(df),
rdata = df),
modelout = "example.inp",
run = 1L)
table_results(fit_mplus)
table_results(fit_mplus)
#> Calculated confidence intervals from est and se.
#> label est_sig se pval confint group
#> 1 VISUAL.BY.VIS_1.GW 1.00 <NA> <NA> <NA> GW
#> 2 VISUAL.BY.VIS_2.GW 0.58*** 0.11 0.00 [ 0.36, 0.79] GW
#> 3 VISUAL.BY.VIS_3.GW 0.80*** 0.13 0.00 [ 0.54, 1.05] GW
#> 4 TEXTUAL.BY.TEX_1.GW 1.00 <NA> <NA> <NA> GW
#> 5 TEXTUAL.BY.TEX_2.GW 1.12*** 0.07 0.00 [ 0.99, 1.25] GW
#> 6 TEXTUAL.BY.TEX_3.GW 0.93*** 0.06 0.00 [ 0.82, 1.04] GW
#> 7 SPEED.BY.SPE_1.GW 1.00 <NA> <NA> <NA> GW
#> 8 SPEED.BY.SPE_2.GW 1.13*** 0.14 0.00 [ 0.86, 1.40] GW
#> 9 SPEED.BY.SPE_3.GW 1.01*** 0.16 0.00 [ 0.70, 1.32] GW
#> 10 TEXTUAL.WITH.VISUAL.GW 0.43*** 0.10 0.00 [ 0.23, 0.62] GW
#> 11 SPEED.WITH.VISUAL.GW 0.33*** 0.08 0.00 [ 0.16, 0.49] GW
#> 12 SPEED.WITH.TEXTUAL.GW 0.24** 0.07 0.00 [ 0.09, 0.38] GW
#> 13 Means.VISUAL.GW 0.00 <NA> <NA> <NA> GW
#> 14 Means.TEXTUAL.GW 0.00 <NA> <NA> <NA> GW
#> 15 Means.SPEED.GW 0.00 <NA> <NA> <NA> GW
#> 16 Intercepts.VIS_1.GW 4.85*** 0.09 0.00 [ 4.67, 5.04] GW
#> 17 Intercepts.VIS_2.GW 6.07*** 0.08 0.00 [ 5.92, 6.22] GW
#> 18 Intercepts.VIS_3.GW 2.15*** 0.08 0.00 [ 1.99, 2.32] GW
#> 19 Intercepts.TEX_1.GW 3.35*** 0.09 0.00 [ 3.18, 3.53] GW
#> 20 Intercepts.TEX_2.GW 4.68*** 0.10 0.00 [ 4.49, 4.87] GW
#> 21 Intercepts.TEX_3.GW 2.46*** 0.08 0.00 [ 2.30, 2.63] GW
#> 22 Intercepts.SPE_1.GW 4.07*** 0.08 0.00 [ 3.90, 4.23] GW
#> 23 Intercepts.SPE_2.GW 5.43*** 0.08 0.00 [ 5.27, 5.59] GW
#> 24 Intercepts.SPE_3.GW 5.29*** 0.08 0.00 [ 5.13, 5.44] GW
#> 25 Variances.VISUAL.GW 0.71*** 0.16 0.00 [ 0.39, 1.03] GW
#> 26 Variances.TEXTUAL.GW 0.87*** 0.13 0.00 [ 0.61, 1.13] GW
#> 27 Variances.SPEED.GW 0.50*** 0.12 0.00 [ 0.27, 0.74] GW
#> 28 Residual.Variances.VIS_1.GW 0.65*** 0.13 0.00 [ 0.40, 0.91] GW
#> 29 Residual.Variances.VIS_2.GW 0.96*** 0.13 0.00 [ 0.72, 1.21] GW
#> 30 Residual.Variances.VIS_3.GW 0.64*** 0.11 0.00 [ 0.42, 0.86] GW
#> 31 Residual.Variances.TEX_1.GW 0.34*** 0.06 0.00 [ 0.22, 0.47] GW
#> 32 Residual.Variances.TEX_2.GW 0.38*** 0.07 0.00 [ 0.23, 0.52] GW
#> 33 Residual.Variances.TEX_3.GW 0.44*** 0.07 0.00 [ 0.30, 0.57] GW
#> 34 Residual.Variances.SPE_1.GW 0.62*** 0.10 0.00 [ 0.42, 0.83] GW
#> 35 Residual.Variances.SPE_2.GW 0.43*** 0.10 0.00 [ 0.24, 0.63] GW
#> 36 Residual.Variances.SPE_3.GW 0.52*** 0.10 0.00 [ 0.32, 0.72] GW
#> 37 VISUAL.BY.VIS_1.PAS 1.00 <NA> <NA> <NA> PAS
#> 38 VISUAL.BY.VIS_2.PAS 0.58*** 0.11 0.00 [ 0.36, 0.79] PAS
#> 39 VISUAL.BY.VIS_3.PAS 0.80*** 0.13 0.00 [ 0.54, 1.05] PAS
#> 40 TEXTUAL.BY.TEX_1.PAS 1.00 <NA> <NA> <NA> PAS
#> 41 TEXTUAL.BY.TEX_2.PAS 1.12*** 0.07 0.00 [ 0.99, 1.25] PAS
#> 42 TEXTUAL.BY.TEX_3.PAS 0.93*** 0.06 0.00 [ 0.82, 1.04] PAS
#> 43 SPEED.BY.SPE_1.PAS 1.00 <NA> <NA> <NA> PAS
#> 44 SPEED.BY.SPE_2.PAS 1.13*** 0.14 0.00 [ 0.86, 1.40] PAS
#> 45 SPEED.BY.SPE_3.PAS 1.01*** 0.16 0.00 [ 0.70, 1.32] PAS
#> 46 TEXTUAL.WITH.VISUAL.PAS 0.41*** 0.11 0.00 [ 0.20, 0.62] PAS
#> 47 SPEED.WITH.VISUAL.PAS 0.18** 0.07 0.01 [ 0.05, 0.31] PAS
#> 48 SPEED.WITH.TEXTUAL.PAS 0.18** 0.06 0.00 [ 0.06, 0.30] PAS
#> 49 Means.VISUAL.PAS 0.15 0.13 0.24 [-0.10, 0.40] PAS
#> 50 Means.TEXTUAL.PAS -0.58*** 0.12 0.00 [-0.81, -0.35] PAS
#> 51 Means.SPEED.PAS 0.18 0.09 0.06 [-0.01, 0.36] PAS
#> 52 Intercepts.VIS_1.PAS 4.85*** 0.09 0.00 [ 4.67, 5.04] PAS
#> 53 Intercepts.VIS_2.PAS 6.07*** 0.08 0.00 [ 5.92, 6.22] PAS
#> 54 Intercepts.VIS_3.PAS 2.15*** 0.08 0.00 [ 1.99, 2.32] PAS
#> 55 Intercepts.TEX_1.PAS 3.35*** 0.09 0.00 [ 3.18, 3.53] PAS
#> 56 Intercepts.TEX_2.PAS 4.68*** 0.10 0.00 [ 4.49, 4.87] PAS
#> 57 Intercepts.TEX_3.PAS 2.46*** 0.08 0.00 [ 2.30, 2.63] PAS
#> 58 Intercepts.SPE_1.PAS 4.07*** 0.08 0.00 [ 3.90, 4.23] PAS
#> 59 Intercepts.SPE_2.PAS 5.43*** 0.08 0.00 [ 5.27, 5.59] PAS
#> 60 Intercepts.SPE_3.PAS 5.29*** 0.08 0.00 [ 5.13, 5.44] PAS
#> 61 Variances.VISUAL.PAS 0.80*** 0.19 0.00 [ 0.42, 1.17] PAS
#> 62 Variances.TEXTUAL.PAS 0.88*** 0.13 0.00 [ 0.62, 1.14] PAS
#> 63 Variances.SPEED.PAS 0.32*** 0.08 0.00 [ 0.16, 0.49] PAS
#> 64 Residual.Variances.VIS_1.PAS 0.56*** 0.16 0.00 [ 0.25, 0.86] PAS
#> 65 Residual.Variances.VIS_2.PAS 1.30*** 0.16 0.00 [ 0.98, 1.61] PAS
#> 66 Residual.Variances.VIS_3.PAS 0.94*** 0.15 0.00 [ 0.65, 1.23] PAS
#> 67 Residual.Variances.TEX_1.PAS 0.44*** 0.07 0.00 [ 0.30, 0.59] PAS
#> 68 Residual.Variances.TEX_2.PAS 0.50*** 0.09 0.00 [ 0.33, 0.67] PAS
#> 69 Residual.Variances.TEX_3.PAS 0.26*** 0.05 0.00 [ 0.16, 0.36] PAS
#> 70 Residual.Variances.SPE_1.PAS 0.89*** 0.13 0.00 [ 0.64, 1.14] PAS
#> 71 Residual.Variances.SPE_2.PAS 0.54*** 0.10 0.00 [ 0.35, 0.74] PAS
#> 72 Residual.Variances.SPE_3.PAS 0.65*** 0.10 0.00 [ 0.46, 0.85] PAS
table_fit(fit_mplus)
#> Mplus.version Name AnalysisType DataType Estimator n NGroups
#> 1 8.6 GENERAL INDIVIDUAL ML 301 2
#> NDependentVars NIndependentVars NContinuousLatentVars Parameters ChiSqM_Value
#> 1 9 0 3 48 164.103
#> ChiSqM_DF ChiSqM_PValue ChiSqBaseline_Value ChiSqBaseline_DF
#> 1 60 0 957.769 72
#> ChiSqBaseline_PValue LL UnrestrictedLL CFI TLI AIC BIC
#> 1 0 -3706.323 -3624.272 0.882 0.859 7508.647 7686.588
#> aBIC RMSEA_Estimate RMSEA_90CI_LB RMSEA_90CI_UB RMSEA_pLT05 SRMR
#> 1 7534.359 0.107 0.088 0.127 0 0.087
#> AICC Filename
#> 1 7527.314 example.out