Monte Carlo Method Assuming Multivariate Normal Distribution using Textbook Standard Errors for Indirect Effect in a Standardized Simple Mediation Model for Data Generated from a Multivariate Normal Distribution — mvn_std_mc.mvn.tb • jeksterslabRmedsimple

Monte Carlo Method Assuming Multivariate Normal Distribution using Textbook Standard Errors for Indirect Effect in a Standardized Simple Mediation Model for Data Generated from a Multivariate Normal Distribution

mvn_std_mc.mvn.tb(
  taskid,
  R = 20000L,
  alphahatprime,
  sehatalphahatprimetb,
  betahatprime,
  sehatbetahatprimetb
)

Arguments

taskid	Numeric. Task ID.
R	Integer. Monte Carlo replications.
alphahatprime	Numeric. Estimated standardized slope of path from `x` to `m` \(\left( \hat{\alpha}^{\prime} \right)\) .
sehatalphahatprimetb	Numeric. Estimated textbook standard error of standardized slope of path from `x` to `m` \(\left( \widehat{se}_{\hat{\alpha}}^{\prime} \right)\) .
betahatprime	Numeric. Estimated standardized slope of path from `m` to `y` \(\left( \hat{\beta}^{\prime} \right)\) .
sehatbetahatprimetb	Numeric. Estimated textbook standard error of standardized slope of path from `m` to `y` \(\left( \widehat{se}_{\hat{\beta}}^{\prime} \right)\) .

See also

Other monte carlo method functions: beta_ols_mc.mvn_pcci_simulation(), beta_ols_mc.mvn_pcci_task(), beta_ols_mc.mvn_simulation(), beta_ols_mc.mvn_task(), beta_ols_mc.mvn(), exp_ols_mc.mvn_pcci_simulation(), exp_ols_mc.mvn_pcci_task(), exp_ols_mc.mvn_simulation(), exp_ols_mc.mvn_task(), exp_ols_mc.mvn(), mc.mvn(), mc.t(), mc.wishart(), mvn_mar_10_mc.mvn_pcci_simulation(), mvn_mar_10_mc.mvn_pcci_task(), mvn_mar_10_mc.mvn_simulation(), mvn_mar_10_mc.mvn_task(), mvn_mar_10_mc.mvn(), mvn_mar_20_mc.mvn_pcci_simulation(), mvn_mar_20_mc.mvn_pcci_task(), mvn_mar_20_mc.mvn_simulation(), mvn_mar_20_mc.mvn_task(), mvn_mar_20_mc.mvn(), mvn_mar_30_mc.mvn_pcci_simulation(), mvn_mar_30_mc.mvn_pcci_task(), mvn_mar_30_mc.mvn_simulation(), mvn_mar_30_mc.mvn_task(), mvn_mar_30_mc.mvn(), mvn_mcar_10_mc.mvn_pcci_simulation(), mvn_mcar_10_mc.mvn_pcci_task(), mvn_mcar_10_mc.mvn_simulation(), mvn_mcar_10_mc.mvn_task(), mvn_mcar_10_mc.mvn(), mvn_mcar_20_mc.mvn_pcci_simulation(), mvn_mcar_20_mc.mvn_pcci_task(), mvn_mcar_20_mc.mvn_simulation(), mvn_mcar_20_mc.mvn_task(), mvn_mcar_20_mc.mvn(), mvn_mcar_30_mc.mvn_pcci_simulation(), mvn_mcar_30_mc.mvn_pcci_task(), mvn_mcar_30_mc.mvn_simulation(), mvn_mcar_30_mc.mvn_task(), mvn_mcar_30_mc.mvn(), mvn_mnar_10_mc.mvn_pcci_simulation(), mvn_mnar_10_mc.mvn_pcci_task(), mvn_mnar_10_mc.mvn_simulation(), mvn_mnar_10_mc.mvn_task(), mvn_mnar_10_mc.mvn(), mvn_mnar_20_mc.mvn_pcci_simulation(), mvn_mnar_20_mc.mvn_pcci_task(), mvn_mnar_20_mc.mvn_simulation(), mvn_mnar_20_mc.mvn_task(), mvn_mnar_20_mc.mvn(), mvn_mnar_30_mc.mvn_pcci_simulation(), mvn_mnar_30_mc.mvn_pcci_task(), mvn_mnar_30_mc.mvn_simulation(), mvn_mnar_30_mc.mvn_task(), mvn_mnar_30_mc.mvn(), mvn_ols_mc.mvn_pcci_simulation(), mvn_ols_mc.mvn_pcci_task(), mvn_ols_mc.mvn_simulation(), mvn_ols_mc.mvn_task(), mvn_ols_mc.mvn(), mvn_sem_mc.mvn_pcci_simulation(), mvn_sem_mc.mvn_pcci_task(), mvn_sem_mc.mvn_simulation(), mvn_sem_mc.mvn_task(), mvn_sem_mc.mvn(), mvn_std_mc.mvn.delta_pcci_simulation(), mvn_std_mc.mvn.delta_pcci_task(), mvn_std_mc.mvn.delta_simulation(), mvn_std_mc.mvn.delta_task(), mvn_std_mc.mvn.delta(), mvn_std_mc.mvn.sem_pcci_simulation(), mvn_std_mc.mvn.sem_pcci_task(), mvn_std_mc.mvn.sem_simulation(), mvn_std_mc.mvn.sem_task(), mvn_std_mc.mvn.sem(), mvn_std_mc.mvn.tb_pcci_simulation(), mvn_std_mc.mvn.tb_pcci_task(), mvn_std_mc.mvn.tb_simulation(), mvn_std_mc.mvn.tb_task(), mvn_std_mc.wishart_pcci_simulation(), mvn_std_mc.wishart_pcci_task(), mvn_std_mc.wishart_simulation(), mvn_std_mc.wishart_task(), mvn_std_mc.wishart(), vm_mod_ols_mc.mvn_pcci_simulation(), vm_mod_ols_mc.mvn_pcci_task(), vm_mod_ols_mc.mvn_simulation(), vm_mod_ols_mc.mvn_task(), vm_mod_ols_mc.mvn(), vm_mod_sem_mc.mvn_pcci_simulation(), vm_mod_sem_mc.mvn_pcci_task(), vm_mod_sem_mc.mvn_simulation(), vm_mod_sem_mc.mvn_task(), vm_mod_sem_mc.mvn(), vm_sev_ols_mc.mvn_pcci_simulation(), vm_sev_ols_mc.mvn_pcci_task(), vm_sev_ols_mc.mvn_simulation(), vm_sev_ols_mc.mvn_task(), vm_sev_ols_mc.mvn(), vm_sev_sem_mc.mvn_pcci_simulation(), vm_sev_sem_mc.mvn_pcci_task(), vm_sev_sem_mc.mvn_simulation(), vm_sev_sem_mc.mvn_task(), vm_sev_sem_mc.mvn()

Author

Ivan Jacob Agaloos Pesigan

Examples

taskid <- 1
data <- mvn_dat(taskid = taskid)
fit.ols(data = data, minimal = TRUE)
#> [1] 0.5046604

fit <- mvn_fit.ols(data = data, taskid = taskid)
thetahatstar <- mvn_std_mc.mvn.tb(
  taskid = taskid, R = 20000L,
  alphahatprime = fit["alphahatprime"], sehatalphahatprimetb = fit["sehatalphahatprimetb"],
  betahatprime = fit["betahatprime"], sehatbetahatprimetb = fit["sehatbetahatprimetb"]
)
hist(thetahatstar)