vm_sev_sem_mc.mvn: Monte Carlo Method Assuming Multivariate Normal Distribution...

In jeksterslabds/jeksterslabRmedsimple: Jekster's Lab Simple Mediation Model Utilities

Description

Monte Carlo Method Assuming Multivariate Normal Distribution for Indirect Effect in a Simple Mediation Model for Data Generated Using the Vale and Maurelli (1983) Approach (Skewness = 3, Kurtosis = 21)

Usage

vm_sev_sem_mc.mvn(
  taskid,
  R = 20000L,
  alphahat,
  sehatalphahat,
  betahat,
  sehatbetahat
)

Arguments

taskid	Numeric. Task ID.
R	Integer. Monte Carlo replications.
alphahat	Numeric. Estimated slope of path from x to m ≤ft( \hat{α} \right) .
sehatalphahat	Numeric. Estimated standard error of slope of path from x to m ≤ft( \widehat{se}_{\hat{α}} \right) .
betahat	Numeric. Estimated slope of path from m to y ≤ft( \hat{β} \right) .
sehatbetahat	Numeric. Estimated standard error of slope of path from m to y ≤ft( \widehat{se}_{\hat{β}} \right) .

Author(s)

Ivan Jacob Agaloos Pesigan

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.mvn.tb(), 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()

Examples

taskid <- 1
data <- vm_sev_dat(taskid = taskid)
fit.sem.mlr(data = data, minimal = TRUE)

fit <- vm_sev_fit.sem.mlr(data = data, taskid = taskid)
thetahatstar <- vm_sev_sem_mc.mvn(
  taskid = taskid, R = 20000L,
  alphahat = fit["alphahat"], sehatalphahat = fit["sehatalphahat"],
  betahat = fit["betahat"], sehatbetahat = fit["sehatbetahat"]
)
hist(thetahatstar)

jeksterslabds/jeksterslabRmedsimple documentation built on Oct. 16, 2020, 11:30 a.m.