![[Stable]](figures/lifecycle-stable.svg)
APIM_sem(
data,
mod_type,
predictor_a,
predictor_p,
outcome_a,
outcome_p,
med_a = NULL,
med_p = NULL,
mod_a = NULL,
mod_p = NULL,
bootstrap = NULL,
standardized = FALSE,
return_result = FALSE,
quite = FALSE
)Arguments
- data
data frame object
- mod_type
options are "simple" (main effect), "med" (mediation), and "mod" (moderation)
- predictor_a
predictor variable name for actor
- predictor_p
predictor variable name for partner
- outcome_a
dependent variable name for actor
- outcome_p
dependent variable name for partner
- med_a
mediation variable name for actor
- med_p
mediation variable name for partner
- mod_a
moderation variable name for actor
- mod_p
moderation variable name for partner
- bootstrap
number of bootstrapping (e.g., 5000). Default is not using bootstrap
- standardized
standardized coefficient
- return_result
return
lavaan::parameterestimates(). Default isFALSE- quite
suppress printing output. Default is
FALSE
Value
data.frame from lavaan::parameterestimates()
Details
Actor-partner interdependence model using SEM approach (with lavaan). Indistinguishable dyads only. Results should be the same as those from Kenny (2015a, 2015b).
References
Kenny, D. A. (2015, October). An interactive tool for the estimation and testing mediation in the Actor-Partner Interdependence Model using structural equation modeling. Computer software. Available from https://davidakenny.shinyapps.io/APIMeM/. Kenny, D. A. (2015, October). An interactive tool for the estimation and testing moderation in the Actor-Partner Interdependence Model using structural equation modeling. Computer software. Available from https://davidakenny.shinyapps.io/APIMoM/. Stas, L, Kenny, D. A., Mayer, A., & Loeys, T. (2018). Giving Dyadic Data Analysis Away: A User-Friendly App for Actor-Partner Interdependence Models. Personal Relationships, 25 (1), 103-119. https://doi.org/10.1111/pere.12230.
Examples
APIM_sem(data = acitelli,
predictor_a = 'Tension_A',
predictor_p = 'Tension_P',
outcome_a = 'Satisfaction_A',
outcome_p = 'Satisfaction_P',
mod_type = 'simple')
#> lavaan 0.6-18 ended normally after 25 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 14
#> Number of equality constraints 6
#>
#> Number of observations 296
#> Number of missing patterns 1
#>
#> Model Test User Model:
#>
#> Test statistic 0.000
#> Degrees of freedom 6
#> P-value (Chi-square) 1.000
#>
#> Model Test Baseline Model:
#>
#> Test statistic 406.856
#> Degrees of freedom 6
#> P-value 0.000
#>
#> User Model versus Baseline Model:
#>
#> Comparative Fit Index (CFI) 1.000
#> Tucker-Lewis Index (TLI) 1.015
#>
#> Robust Comparative Fit Index (CFI) 1.000
#> Robust Tucker-Lewis Index (TLI) 1.015
#>
#> Loglikelihood and Information Criteria:
#>
#> Loglikelihood user model (H0) -837.957
#> Loglikelihood unrestricted model (H1) -837.957
#>
#> Akaike (AIC) 1691.915
#> Bayesian (BIC) 1721.438
#> Sample-size adjusted Bayesian (SABIC) 1696.067
#>
#> Root Mean Square Error of Approximation:
#>
#> RMSEA 0.000
#> 90 Percent confidence interval - lower 0.000
#> 90 Percent confidence interval - upper 0.000
#> P-value H_0: RMSEA <= 0.050 1.000
#> P-value H_0: RMSEA >= 0.080 0.000
#>
#> Robust RMSEA 0.000
#> 90 Percent confidence interval - lower 0.000
#> 90 Percent confidence interval - upper 0.000
#> P-value H_0: Robust RMSEA <= 0.050 1.000
#> P-value H_0: Robust RMSEA >= 0.080 0.000
#>
#> Standardized Root Mean Square Residual:
#>
#> SRMR 0.000
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Observed
#> Observed information based on Hessian
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> y_a ~
#> x_a (a) -0.375 0.022 -16.824 0.000
#> x_p (p) -0.177 0.022 -7.959 0.000
#> y_p ~
#> x_p (a) -0.375 0.022 -16.824 0.000
#> x_a (p) -0.177 0.022 -7.959 0.000
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> x_a ~~
#> x_p (cx) 0.149 0.029 5.190 0.000
#> .y_a ~~
#> .y_p (cy) 0.063 0.009 6.872 0.000
#>
#> Intercepts:
#> Estimate Std.Err z-value P(>|z|)
#> x_a (mx) 2.431 0.032 75.134 0.000
#> x_p (mx) 2.431 0.032 75.134 0.000
#> .y_a (my) 4.948 0.084 58.968 0.000
#> .y_p (my) 4.948 0.084 58.968 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> x_a (vx) 0.471 0.029 16.403 0.000
#> x_p (vx) 0.471 0.029 16.403 0.000
#> .y_a (ve) 0.145 0.009 15.773 0.000
#> .y_p (ve) 0.145 0.009 15.773 0.000
#>
#> Defined Parameters:
#> Estimate Std.Err z-value P(>|z|)
#> k 0.473 0.062 7.613 0.000
#> sum -0.276 0.017 -16.425 0.000
#> cont -0.198 0.029 -6.754 0.000
#>