Generate a summary for a constrained Gaussian Graphical Model (GGM) similar to lavaan-formated summary
# S3 method for GGM
summary(object, ...)A constrained GGM fitted model object.
Currently ignored.
Shown the lavaan-formatted summary to console
sem0 <- SEMrun(sachs$graph, log(sachs$pkc), algo = "cggm")
#> DAG conversion : TRUE
#> GGM (de-biased nodewise L1) solver ended normally after 9 iterations
#>
#> deviance/df: 66.94041 srmr: 0.089629
#>
summary(sem0$fit)
#> GGM (de-biased nodewise L1) solver ended normally after 0 iterations
#>
#> Estimator ML
#> Optimization method GGM
#>
#> Number of free parameters 27
#>
#> Number of observations 1766
#>
#> Model Test User Model
#>
#> Test statistic (Deviance) 2409.855
#> Degrees of freedom (df) 36
#> Deviance/df 66.94
#> Standardized Root Mean Square Residual (srmr) 0.09
#>
#> Parameter Estimates:
#>
#> Regressions:
#>
#> lhs op rhs est se z_test pvalue ci.lower ci.uppper
#> 1 PKC ~ PIP2 0.019 0.025 0.753 0.451 -0.030 0.068
#> 2 PKC ~ Plcg 0.014 0.025 0.541 0.588 -0.036 0.063
#> 3 Mek ~ PKA 0.010 0.018 0.547 0.584 -0.025 0.044
#> 4 Mek ~ PKC -0.005 0.017 -0.262 0.793 -0.039 0.030
#> 5 Mek ~ Raf 0.682 0.018 38.894 0.000 0.648 0.717
#> 6 Raf ~ PKA -0.123 0.024 -5.225 0.000 -0.170 -0.077
#> 7 Raf ~ PKC -0.037 0.024 -1.548 0.122 -0.083 0.010
#> 8 PIP2 ~ PIP3 0.488 0.020 24.675 0.000 0.450 0.527
#> 9 PIP2 ~ Plcg 0.226 0.020 11.408 0.000 0.187 0.265
#> 10 Plcg ~ PIP3 0.207 0.023 8.895 0.000 0.161 0.253
#> 11 Jnk ~ PKA 0.092 0.024 3.903 0.000 0.046 0.138
#> 12 Jnk ~ PKC 0.127 0.024 5.402 0.000 0.081 0.173
#> 13 P38 ~ PKA -0.017 0.018 -0.930 0.352 -0.053 0.019
#> 14 P38 ~ PKC 0.645 0.018 35.465 0.000 0.610 0.681
#> 15 Akt ~ PKA 0.545 0.020 27.426 0.000 0.506 0.584
#> 16 Akt ~ PIP3 -0.064 0.020 -3.208 0.001 -0.103 -0.025
#> 17 Erk ~ PKA 0.455 0.021 21.475 0.000 0.414 0.497
#> 18 Erk ~ Mek -0.020 0.021 -0.931 0.352 -0.061 0.022
#>
#> Variances:
#>
#> PKA PIP3 PKC Mek Raf PIP2 Plcg Jnk P38 Akt Erk
#> 1.000 1.000 0.999 0.535 0.983 0.663 0.957 0.974 0.583 0.691 0.790