mc.t: Monte Carlo Method for Indirect Effect in a Simple Mediation...

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

Description

In this method α and β are assumed to follow a t distribution with df = n - 2 and df = n - 3 respectively.

Usage

mc.t(R = 20000L, alphahat, sehatalphahat, betahat, sehatbetahat, n)

Arguments

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) .
n	Integer. Sample size.

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.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(), vm_sev_sem_mc.mvn()

Examples

thetahatstar <- mc.t(
  R = 20000L, alphahat = 0.338593, sehatalphahat = 0.12236736,
  betahat = 0.451039, sehatbetahat = 0.14597405,
  n = 20
)
hist(thetahatstar)

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