Summarize a parameter table from a modsem model.
summarize_partable.RdSummarize a parameter table from a modsem model.
Usage
summarize_partable(
parTable,
scientific = FALSE,
ci = FALSE,
digits = 3,
loadings = TRUE,
regressions = TRUE,
covariances = TRUE,
intercepts = TRUE,
variances = TRUE
)Arguments
- parTable
A parameter table, typically obtained from a
modsemmodel usingparameter_estimatesorstandardized_estimates.- scientific
Logical, whether to print p-values in scientific notation.
- ci
Logical, whether to include confidence intervals in the output.
- digits
Integer, number of digits to round the estimates to (default is 3).
- loadings
Logical, whether to include factor loadings in the output.
- regressions
Logical, whether to include regression coefficients in the output.
- covariances
Logical, whether to include covariance estimates in the output.
- intercepts
Logical, whether to include intercepts in the output.
- variances
Logical, whether to include variance estimates in the output.
Examples
m1 <- '
# Outer Model
X =~ x1 + x2 + x3
Z =~ z1 + z2 + z3
Y =~ y1 + y2 + y3
# Inner Model
Y ~ X + Z + X:Z
'
# Double centering approach
est_dca <- modsem(m1, oneInt)
std <- standardized_estimates(est_dca, correction = TRUE)
summarize_partable(std)
#> modsem (version 1.0.14)
#>
#> Number of model parameters 64
#> Number of latent variables 4
#> Number of observed variables 18
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.927 0.005 197.805 0.000
#> x2 0.892 0.006 156.888 0.000
#> x3 0.914 0.005 180.631 0.000
#> Z =~
#> z1 0.926 0.005 197.375 0.000
#> z2 0.899 0.005 164.838 0.000
#> z3 0.913 0.005 180.409 0.000
#> Y =~
#> y1 0.969 0.002 493.285 0.000
#> y2 0.955 0.002 390.119 0.000
#> y3 0.962 0.002 435.067 0.000
#> XZ =~
#> x1z1 0.878 0.007 129.129 0.000
#> x2z1 0.836 0.008 105.776 0.000
#> x3z1 0.843 0.008 108.821 0.000
#> x1z2 0.833 0.008 102.749 0.000
#> x2z2 0.804 0.009 90.666 0.000
#> x3z2 0.812 0.009 93.853 0.000
#> x1z3 0.867 0.007 121.949 0.000
#> x2z3 0.829 0.008 102.106 0.000
#> x3z3 0.826 0.008 100.718 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.424 0.016 26.787 0.000
#> Z 0.358 0.016 22.372 0.000
#> XZ 0.444 0.015 28.971 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> .x1z1 ~~
#> .x1z2 0.384 0.018 21.659 0.000
#> .x1z3 0.393 0.019 20.928 0.000
#> .x2z1 0.415 0.017 24.499 0.000
#> .x3z1 0.440 0.017 25.573 0.000
#> .x1z2 ~~
#> .x1z3 0.367 0.018 20.929 0.000
#> .x2z2 0.486 0.014 33.698 0.000
#> .x3z2 0.520 0.014 36.545 0.000
#> .x1z3 ~~
#> .x2z3 0.450 0.016 28.177 0.000
#> .x3z3 0.474 0.016 29.999 0.000
#> .x2z1 ~~
#> .x2z2 0.510 0.014 35.404 0.000
#> .x2z3 0.542 0.014 38.238 0.000
#> .x3z1 0.370 0.016 22.694 0.000
#> .x2z2 ~~
#> .x2z3 0.486 0.015 33.189 0.000
#> .x3z2 0.456 0.014 31.605 0.000
#> .x2z3 ~~
#> .x3z3 0.404 0.015 26.242 0.000
#> .x3z1 ~~
#> .x3z2 0.464 0.015 30.577 0.000
#> .x3z3 0.507 0.015 34.430 0.000
#> .x3z2 ~~
#> .x3z3 0.464 0.015 31.361 0.000
#> X ~~
#> Z 0.201 0.023 8.786 0.000
#> XZ 0.016 0.025 0.628 0.530
#> Z ~~
#> XZ 0.062 0.025 2.462 0.014
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .x1 0.140 0.009 16.115 0.000
#> .x2 0.204 0.010 20.047 0.000
#> .x3 0.165 0.009 17.875 0.000
#> .z1 0.142 0.009 16.321 0.000
#> .z2 0.191 0.010 19.481 0.000
#> .z3 0.167 0.009 18.042 0.000
#> .y1 0.060 0.004 15.807 0.000
#> .y2 0.089 0.005 18.988 0.000
#> .y3 0.075 0.004 17.723 0.000
#> .x1z1 0.229 0.012 19.190 0.000
#> .x2z1 0.301 0.013 22.788 0.000
#> .x3z1 0.289 0.013 22.109 0.000
#> .x1z2 0.306 0.014 22.696 0.000
#> .x2z2 0.353 0.014 24.786 0.000
#> .x3z2 0.341 0.014 24.264 0.000
#> .x1z3 0.249 0.012 20.178 0.000
#> .x2z3 0.313 0.013 23.291 0.000
#> .x3z3 0.317 0.014 23.421 0.000
#> X 1.000 0.000
#> Z 1.000 0.000
#> .Y 0.398 0.016 25.401 0.000
#> XZ 1.049 0.000
#>