Parametric Bootstrapping Assuming Multivariate Normal Distribution — pb.vm • jeksterslabRmedsimple

Parametric Bootstrapping Assuming Multivariate Normal Distribution

pb.vm(
  muthetahat,
  Sigmathetahat,
  skewnessthetahat,
  kurtosisthetahat,
  n,
  std = FALSE,
  B = 5000,
  par = TRUE,
  ncores = NULL,
  blas_threads = TRUE,
  mc = TRUE,
  lb = FALSE
)

Arguments

muthetahat	Numeric vector. Model-implied mean vector \( \boldsymbol{\mu} \left( \boldsymbol{\hat{\theta}} \right) \) .
Sigmathetahat	Numeric matrix. Model-implied variance-covariance matrix \( \boldsymbol{\Sigma} \left( \boldsymbol{\hat{\theta}} \right) \) .
skewnessthetahat	Numeric vector. Estimated skewness.
kurtosisthetahat	Numeric vector. Estimated excess kurtosis
n	Integer. Sample size.
std	Logical. Standardize the indirect effect \( \hat{\alpha}^{\prime} \hat{\beta}^{\prime} = \hat{\alpha} \hat{\beta} \frac{\hat{\sigma}_x}{\hat{\sigma}_y}\).
B	Integer. Number of bootstrap samples.
par	Logical. If `TRUE`, use multiple cores. If `FALSE`, use `lapply()`.
ncores	Integer. Number of cores to use if `par = TRUE`. If unspecified, defaults to `detectCores() - 1`.
blas_threads	Logical. If `TRUE`, set BLAS threads using `blas_set_num_threads(threads = blas_get_num_procs())`. If `FALSE`, set BLAS threads using `blas_set_num_threads(threads = 1)`. If `par = TRUE`, `blas_threads` is automatically set to `FALSE` to prevent conflicts in parallel processing. This argument is useful when `FUN` can handle implicit parallelism when `par = FALSE`, for example linear algebra operations.
mc	Logical. If `TRUE`, use `parallel::mclapply()`. If `FALSE`, use `parallel::parLapply()` or `parallel::parLapplyLB()`. Ignored if `par = FALSE`.
lb	Logical. If `TRUE` use `parallel::parLapplyLB()`. If `FALSE`, use `parallel::parLapply()`. Ignored if `par = FALSE` and `mc = TRUE`.

See also

Other parametric functions: beta_pb.beta_bcaci_simulation(), beta_pb.beta_bcaci_task(), beta_pb.beta_bcci_simulation(), beta_pb.beta_bcci_task(), beta_pb.beta_pcci_simulation(), beta_pb.beta_pcci_task(), beta_pb.beta_simulation(), beta_pb.beta_task(), beta_pb.beta(), beta_pb.mvn_bcaci_simulation(), beta_pb.mvn_bcaci_task(), beta_pb.mvn_bcci_simulation(), beta_pb.mvn_bcci_task(), beta_pb.mvn_pcci_simulation(), beta_pb.mvn_pcci_task(), beta_pb.mvn_simulation(), beta_pb.mvn_task(), beta_pb.mvn(), exp_pb.exp_bcaci_simulation(), exp_pb.exp_bcaci_task(), exp_pb.exp_bcci_simulation(), exp_pb.exp_bcci_task(), exp_pb.exp_pcci_simulation(), exp_pb.exp_pcci_task(), exp_pb.exp_simulation(), exp_pb.exp_task(), exp_pb.exp(), exp_pb.mvn_bcaci_simulation(), exp_pb.mvn_bcaci_task(), exp_pb.mvn_bcci_simulation(), exp_pb.mvn_bcci_task(), exp_pb.mvn_pcci_simulation(), exp_pb.mvn_pcci_task(), exp_pb.mvn_simulation(), exp_pb.mvn_task(), exp_pb.mvn(), mvn_mar_10_pb.mvn_bcci_simulation(), mvn_mar_10_pb.mvn_bcci_task(), mvn_mar_10_pb.mvn_pcci_simulation(), mvn_mar_10_pb.mvn_pcci_task(), mvn_mar_10_pb.mvn_simulation(), mvn_mar_10_pb.mvn_task(), mvn_mar_10_pb.mvn(), mvn_mar_20_pb.mvn_bcci_simulation(), mvn_mar_20_pb.mvn_bcci_task(), mvn_mar_20_pb.mvn_pcci_simulation(), mvn_mar_20_pb.mvn_pcci_task(), mvn_mar_20_pb.mvn_simulation(), mvn_mar_20_pb.mvn_task(), mvn_mar_20_pb.mvn(), mvn_mar_30_pb.mvn_bcci_simulation(), mvn_mar_30_pb.mvn_bcci_task(), mvn_mar_30_pb.mvn_pcci_simulation(), mvn_mar_30_pb.mvn_pcci_task(), mvn_mar_30_pb.mvn_simulation(), mvn_mar_30_pb.mvn_task(), mvn_mar_30_pb.mvn(), mvn_mcar_10_pb.mvn_bcci_simulation(), mvn_mcar_10_pb.mvn_bcci_task(), mvn_mcar_10_pb.mvn_pcci_simulation(), mvn_mcar_10_pb.mvn_pcci_task(), mvn_mcar_10_pb.mvn_simulation(), mvn_mcar_10_pb.mvn_task(), mvn_mcar_10_pb.mvn(), mvn_mcar_20_pb.mvn_bcci_simulation(), mvn_mcar_20_pb.mvn_bcci_task(), mvn_mcar_20_pb.mvn_pcci_simulation(), mvn_mcar_20_pb.mvn_pcci_task(), mvn_mcar_20_pb.mvn_simulation(), mvn_mcar_20_pb.mvn_task(), mvn_mcar_20_pb.mvn(), mvn_mcar_30_pb.mvn_bcci_simulation(), mvn_mcar_30_pb.mvn_bcci_task(), mvn_mcar_30_pb.mvn_pcci_simulation(), mvn_mcar_30_pb.mvn_pcci_task(), mvn_mcar_30_pb.mvn_simulation(), mvn_mcar_30_pb.mvn_task(), mvn_mcar_30_pb.mvn(), mvn_mnar_10_pb.mvn_bcci_simulation(), mvn_mnar_10_pb.mvn_bcci_task(), mvn_mnar_10_pb.mvn_pcci_simulation(), mvn_mnar_10_pb.mvn_pcci_task(), mvn_mnar_10_pb.mvn_simulation(), mvn_mnar_10_pb.mvn_task(), mvn_mnar_10_pb.mvn(), mvn_mnar_20_pb.mvn_bcci_simulation(), mvn_mnar_20_pb.mvn_bcci_task(), mvn_mnar_20_pb.mvn_pcci_simulation(), mvn_mnar_20_pb.mvn_pcci_task(), mvn_mnar_20_pb.mvn_simulation(), mvn_mnar_20_pb.mvn_task(), mvn_mnar_20_pb.mvn(), mvn_mnar_30_pb.mvn_bcci_simulation(), mvn_mnar_30_pb.mvn_bcci_task(), mvn_mnar_30_pb.mvn_pcci_simulation(), mvn_mnar_30_pb.mvn_pcci_task(), mvn_mnar_30_pb.mvn_simulation(), mvn_mnar_30_pb.mvn_task(), mvn_mnar_30_pb.mvn(), mvn_pb.mvn_bcaci_simulation(), mvn_pb.mvn_bcaci_task(), mvn_pb.mvn_bcci_simulation(), mvn_pb.mvn_bcci_task(), mvn_pb.mvn_pcci_simulation(), mvn_pb.mvn_pcci_task(), mvn_pb.mvn_simulation(), mvn_pb.mvn_task(), mvn_pb.mvn(), mvn_std_pb.mvn_bcaci_simulation(), mvn_std_pb.mvn_bcaci_task(), mvn_std_pb.mvn_bcci_simulation(), mvn_std_pb.mvn_bcci_task(), mvn_std_pb.mvn_pcci_simulation(), mvn_std_pb.mvn_pcci_task(), mvn_std_pb.mvn_simulation(), mvn_std_pb.mvn_task(), mvn_std_pb.mvn(), pb.beta(), pb.exp(), pb.mvn(), vm_mod_pb.mvn_bcaci_simulation(), vm_mod_pb.mvn_bcaci_task(), vm_mod_pb.mvn_bcci_simulation(), vm_mod_pb.mvn_bcci_task(), vm_mod_pb.mvn_pcci_simulation(), vm_mod_pb.mvn_pcci_task(), vm_mod_pb.mvn_simulation(), vm_mod_pb.mvn_task(), vm_mod_pb.mvn(), vm_mod_pb.vm_bcaci_simulation(), vm_mod_pb.vm_bcaci_task(), vm_mod_pb.vm_bcci_simulation(), vm_mod_pb.vm_bcci_task(), vm_mod_pb.vm_pcci_simulation(), vm_mod_pb.vm_pcci_task(), vm_mod_pb.vm_simulation(), vm_mod_pb.vm_task(), vm_mod_pb.vm(), vm_mod_std_pb.mvn_bcaci_simulation(), vm_mod_std_pb.mvn_bcaci_task(), vm_mod_std_pb.mvn_bcci_simulation(), vm_mod_std_pb.mvn_bcci_task(), vm_mod_std_pb.mvn_pcci_simulation(), vm_mod_std_pb.mvn_pcci_task(), vm_mod_std_pb.mvn_simulation(), vm_mod_std_pb.mvn_task(), vm_mod_std_pb.mvn(), vm_sev_pb.mvn_bcaci_simulation(), vm_sev_pb.mvn_bcaci_task(), vm_sev_pb.mvn_bcci_simulation(), vm_sev_pb.mvn_bcci_task(), vm_sev_pb.mvn_pcci_simulation(), vm_sev_pb.mvn_pcci_task(), vm_sev_pb.mvn_simulation(), vm_sev_pb.mvn_task(), vm_sev_pb.mvn(), vm_sev_pb.vm_bcaci_simulation(), vm_sev_pb.vm_bcaci_task(), vm_sev_pb.vm_bcci_simulation(), vm_sev_pb.vm_bcci_task(), vm_sev_pb.vm_pcci_simulation(), vm_sev_pb.vm_pcci_task(), vm_sev_pb.vm_simulation(), vm_sev_pb.vm_task(), vm_sev_pb.vm(), vm_sev_std_pb.mvn_bcaci_simulation(), vm_sev_std_pb.mvn_bcaci_task(), vm_sev_std_pb.mvn_bcci_simulation(), vm_sev_std_pb.mvn_bcci_task(), vm_sev_std_pb.mvn_pcci_simulation(), vm_sev_std_pb.mvn_pcci_task(), vm_sev_std_pb.mvn_simulation(), vm_sev_std_pb.mvn_task(), vm_sev_std_pb.mvn()

Author

Ivan Jacob Agaloos Pesigan

Examples

muthetahat <- mutheta(
  mux = 70.18000,
  deltam = 26.82246,
  deltay = 29.91071,
  taudot = 0.207648,
  beta = 0.451039,
  alpha = 0.338593
)
Sigmathetahat <- Sigmatheta(
  taudot = 0.207648,
  beta = 0.451039,
  alpha = 0.338593,
  sigma2x = 1.293469,
  sigma2epsilonm = 0.9296691,
  sigma2epsilony = 0.9310597
)
skewnessthetahat <- c(
  -0.542156,
  -0.2377815,
  -0.06016235
)
kurtosisthetahat <- c(
  0.2215535,
  -0.4361385,
  -0.6906525
)

# Unstandardized -------------------------------------------------------------
thetahatstar <- pb.vm(
  muthetahat = muthetahat, Sigmathetahat = Sigmathetahat,
  skewnessthetahat = skewnessthetahat, kurtosisthetahat = kurtosisthetahat,
  n = 50, B = 5000, par = FALSE
)
hist(thetahatstar)

# Standardized ---------------------------------------------------------------
thetahatstar <- pb.vm(
  muthetahat = muthetahat, Sigmathetahat = Sigmathetahat,
  skewnessthetahat = skewnessthetahat, kurtosisthetahat = kurtosisthetahat,
  n = 50, std = TRUE, B = 5000, par = FALSE
)
hist(thetahatstar)