summary for modsem objects
summary.Rdsummary for modsem objects
summary for modsem objects
summary for modsem objects
Usage
# S3 method for class 'modsem_da'
summary(
object,
H0 = TRUE,
verbose = interactive(),
r.squared = TRUE,
fit = FALSE,
adjusted.stat = FALSE,
digits = 3,
scientific = FALSE,
ci = FALSE,
standardized = FALSE,
centered = FALSE,
monte.carlo = FALSE,
mc.reps = 10000,
loadings = TRUE,
regressions = TRUE,
covariances = TRUE,
intercepts = TRUE,
variances = TRUE,
var.interaction = FALSE,
...
)
# S3 method for class 'modsem_mplus'
summary(
object,
scientific = FALSE,
standardized = FALSE,
ci = FALSE,
digits = 3,
loadings = TRUE,
regressions = TRUE,
covariances = TRUE,
intercepts = TRUE,
variances = TRUE,
...
)
# S3 method for class 'modsem_pi'
summary(
object,
H0 = TRUE,
r.squared = TRUE,
adjusted.stat = FALSE,
digits = 3,
scientific = FALSE,
verbose = TRUE,
...
)Arguments
- object
modsem object to summarized
- H0
Should the baseline model be estimated, and used to produce comparative fit?
- verbose
Should messages be printed?
- r.squared
Calculate R-squared.
- fit
Print additional fit measures.
- adjusted.stat
Should sample size corrected/adjustes AIC and BIC be reported?
- digits
Number of digits for printed numerical values
- scientific
Should scientific format be used for p-values?
- ci
print confidence intervals
- standardized
standardize estimates
- centered
Print mean centered estimates.
- monte.carlo
Should Monte Carlo bootstrapped standard errors be used? Only relevant if
standardized = TRUE. IfFALSEdelta method is used instead.- mc.reps
Number of Monte Carlo repetitions. Only relevant if
monte.carlo = TRUE, andstandardized = TRUE.- loadings
print loadings
- regressions
print regressions
- covariances
print covariances
- intercepts
print intercepts
- variances
print variances
- var.interaction
If FALSE variances for interaction terms will be removed from the output.
- ...
arguments passed to lavaan::summary()
Examples
# \dontrun{
m1 <- "
# Outer Model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3
# Inner model
Y ~ X + Z + X:Z
"
est1 <- modsem(m1, oneInt, "qml")
summary(est1, ci = TRUE, scientific = TRUE)
#>
#> modsem (1.0.14) ended normally after 39 iterations
#>
#> Estimator QML
#> Optimization method NLMINB
#> Number of model parameters 31
#>
#> Number of observations 2000
#>
#> Loglikelihood and Information Criteria:
#> Loglikelihood -1.7e+04
#> Akaike (AIC) 3.5e+04
#> Bayesian (BIC) 3.5e+04
#>
#> Fit Measures for Baseline Model (H0):
#> Standard
#> Chi-square 1.8e+01
#> Degrees of Freedom (Chi-square) 24
#> P-value (Chi-square) 0.82554
#> RMSEA 0e+00
#>
#> Loglikelihood -1.8e+04
#> Akaike (AIC) 3.6e+04
#> Bayesian (BIC) 3.6e+04
#>
#> Comparative Fit to H0 (LRT test):
#> Loglikelihood change 3.4e+02
#> Difference test (D) 6.8e+02
#> Degrees of freedom (D) 1
#> P-value (D) < 2.22e-16
#>
#> R-Squared Interaction Model (H1):
#> Y 5.99e-01
#> R-Squared Baseline Model (H0):
#> Y 3.95e-01
#> R-Squared Change (H1 - H0):
#> Y 2.04e-01
#>
#> Parameter Estimates:
#> Coefficients unstandardized
#> Information observed
#> Standard errors standard
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|) ci.lower ci.upper
#> X =~
#> x1 1.000
#> x2 0.804 0.013 63.895 < 2.22e-16 0.779 0.828
#> x3 0.914 0.013 67.708 < 2.22e-16 0.888 0.940
#> Z =~
#> z1 1.000
#> z2 0.810 0.012 65.078 < 2.22e-16 0.786 0.835
#> z3 0.881 0.013 67.606 < 2.22e-16 0.856 0.907
#> Y =~
#> y1 1.000
#> y2 0.798 0.007 107.531 < 2.22e-16 0.784 0.813
#> y3 0.899 0.008 112.559 < 2.22e-16 0.884 0.915
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|) ci.lower ci.upper
#> Y ~
#> X 0.673 0.031 21.673 < 2.22e-16 0.612 0.734
#> Z 0.569 0.030 18.718 < 2.22e-16 0.509 0.628
#> X:Z 0.719 0.028 25.832 < 2.22e-16 0.664 0.773
#>
#> Intercepts:
#> Estimate Std.Error z.value P(>|z|) ci.lower ci.upper
#> .x1 1.023 0.024 42.824 < 2.22e-16 0.976 1.070
#> .x2 1.215 0.020 60.904 < 2.22e-16 1.176 1.255
#> .x3 0.919 0.022 41.418 < 2.22e-16 0.876 0.963
#> .z1 1.011 0.024 41.560 < 2.22e-16 0.964 1.059
#> .z2 1.206 0.020 59.254 < 2.22e-16 1.166 1.245
#> .z3 0.916 0.022 42.048 < 2.22e-16 0.873 0.958
#> .y1 1.036 0.033 31.393 < 2.22e-16 0.972 1.101
#> .y2 1.220 0.027 45.418 < 2.22e-16 1.167 1.272
#> .y3 0.953 0.030 31.798 < 2.22e-16 0.895 1.012
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|) ci.lower ci.upper
#> X ~~
#> Z 0.200 0.024 8.242 < 2.22e-16 0.153 0.248
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|) ci.lower ci.upper
#> .x1 0.158 0.009 18.145 < 2.22e-16 0.141 0.175
#> .x2 0.162 0.007 23.153 < 2.22e-16 0.148 0.176
#> .x3 0.165 0.008 20.766 < 2.22e-16 0.149 0.180
#> .z1 0.167 0.009 18.506 < 2.22e-16 0.149 0.185
#> .z2 0.160 0.007 22.676 < 2.22e-16 0.146 0.173
#> .z3 0.158 0.008 20.771 < 2.22e-16 0.143 0.173
#> .y1 0.160 0.009 18.010 < 2.22e-16 0.142 0.177
#> .y2 0.154 0.007 22.680 < 2.22e-16 0.141 0.168
#> .y3 0.164 0.008 20.679 < 2.22e-16 0.148 0.179
#> X 0.983 0.036 26.974 < 2.22e-16 0.911 1.054
#> Z 1.018 0.038 26.927 < 2.22e-16 0.943 1.092
#> .Y 0.980 0.038 25.906 < 2.22e-16 0.906 1.054
#>
# }