mvn_mnar_30_fit.sem_simulation: Fit Simple Mediation Model for Data Generated from a...

Description Usage Arguments Author(s) See Also

View source: R/mvn_mnar_30_unstd_fit.sem.R

Description

Fit Simple Mediation Model for Data Generated from a Multivariate Normal Distribution with Data Missing Not at Random - 30% - Structural Equation Modeling (Simulation)

Usage

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mvn_mnar_30_fit.sem_simulation(
  dir = getwd(),
  all = TRUE,
  taskid = NULL,
  overwrite = FALSE,
  par = TRUE,
  ncores = NULL,
  blas_threads = TRUE,
  mc = TRUE,
  lb = FALSE,
  cl_eval = FALSE,
  cl_export = FALSE,
  cl_expr,
  cl_vars
)

Arguments

dir

Character string. Directory where results of mvn_dat_* are stored.

all

Logical. Process all results.

taskid

Numeric. Task ID.

overwrite

Logical. Overwrite existing results.

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.

cl_eval

Logical. Execute parallel::clusterEvalQ() using cl_expr. Ignored if mc = TRUE.

cl_export

Logical. Execute parallel::clusterExport() using cl_vars. Ignored if mc = TRUE.

cl_expr

Expression. Expression passed to parallel::clusterEvalQ() Ignored if mc = TRUE.

cl_vars

Character vector. Names of objects to pass to parallel::clusterExport() Ignored if mc = TRUE.

Author(s)

Ivan Jacob Agaloos Pesigan

See Also

Other model fit functions: beta_fit.ols_simulation_summary(), beta_fit.ols_simulation(), beta_fit.ols_task_summary(), beta_fit.ols_task(), beta_fit.ols(), beta_fit.sem.mlr_simulation_summary(), beta_fit.sem.mlr_simulation(), beta_fit.sem.mlr_task_summary(), beta_fit.sem.mlr_task(), beta_fit.sem.mlr(), beta_std_fit.sem.mlr_simulation_summary(), beta_std_fit.sem.mlr_simulation(), beta_std_fit.sem.mlr_task_summary(), beta_std_fit.sem.mlr_task(), beta_std_fit.sem.mlr(), exp_fit.ols_simulation_summary(), exp_fit.ols_simulation(), exp_fit.ols_task_summary(), exp_fit.ols_task(), exp_fit.ols(), exp_fit.sem.mlr_simulation_summary(), exp_fit.sem.mlr_simulation(), exp_fit.sem.mlr_task_summary(), exp_fit.sem.mlr_task(), exp_fit.sem.mlr(), exp_std_fit.sem.mlr_simulation_summary(), exp_std_fit.sem.mlr_simulation(), exp_std_fit.sem.mlr_task_summary(), exp_std_fit.sem.mlr_task(), exp_std_fit.sem.mlr(), fit.cov(), fit.ols(), fit.sem.mlr(), fit.sem(), mvn_fit.ols_simulation_summary(), mvn_fit.ols_simulation(), mvn_fit.ols_task_summary(), mvn_fit.ols_task(), mvn_fit.ols(), mvn_fit.sem_simulation_summary(), mvn_fit.sem_simulation(), mvn_fit.sem_task_summary(), mvn_fit.sem_task(), mvn_fit.sem(), mvn_mar_10_fit.sem_simulation_summary(), mvn_mar_10_fit.sem_simulation(), mvn_mar_10_fit.sem_task_summary(), mvn_mar_10_fit.sem_task(), mvn_mar_10_fit.sem(), mvn_mar_20_fit.sem_simulation_summary(), mvn_mar_20_fit.sem_simulation(), mvn_mar_20_fit.sem_task_summary(), mvn_mar_20_fit.sem_task(), mvn_mar_20_fit.sem(), mvn_mar_30_fit.sem_simulation_summary(), mvn_mar_30_fit.sem_simulation(), mvn_mar_30_fit.sem_task_summary(), mvn_mar_30_fit.sem_task(), mvn_mar_30_fit.sem(), mvn_mcar_10_fit.sem_simulation_summary(), mvn_mcar_10_fit.sem_simulation(), mvn_mcar_10_fit.sem_task_summary(), mvn_mcar_10_fit.sem_task(), mvn_mcar_10_fit.sem(), mvn_mcar_20_fit.sem_simulation_summary(), mvn_mcar_20_fit.sem_simulation(), mvn_mcar_20_fit.sem_task_summary(), mvn_mcar_20_fit.sem_task(), mvn_mcar_20_fit.sem(), mvn_mcar_30_fit.sem_simulation_summary(), mvn_mcar_30_fit.sem_simulation(), mvn_mcar_30_fit.sem_task_summary(), mvn_mcar_30_fit.sem_task(), mvn_mcar_30_fit.sem(), mvn_mnar_10_fit.sem_simulation_summary(), mvn_mnar_10_fit.sem_simulation(), mvn_mnar_10_fit.sem_task_summary(), mvn_mnar_10_fit.sem_task(), mvn_mnar_10_fit.sem(), mvn_mnar_20_fit.sem_simulation_summary(), mvn_mnar_20_fit.sem_simulation(), mvn_mnar_20_fit.sem_task_summary(), mvn_mnar_20_fit.sem_task(), mvn_mnar_20_fit.sem(), mvn_mnar_30_fit.sem_simulation_summary(), mvn_mnar_30_fit.sem_task_summary(), mvn_mnar_30_fit.sem_task(), mvn_mnar_30_fit.sem(), mvn_std_fit.sem_simulation_summary(), mvn_std_fit.sem_simulation(), mvn_std_fit.sem_task_summary(), mvn_std_fit.sem_task(), mvn_std_fit.sem(), vm_mod_fit.ols_simulation_summary(), vm_mod_fit.ols_simulation(), vm_mod_fit.ols_task_summary(), vm_mod_fit.ols_task(), vm_mod_fit.ols(), vm_mod_fit.sem.mlr_simulation_summary(), vm_mod_fit.sem.mlr_simulation(), vm_mod_fit.sem.mlr_task_summary(), vm_mod_fit.sem.mlr_task(), vm_mod_fit.sem.mlr(), vm_mod_std_fit.sem.mlr_simulation_summary(), vm_mod_std_fit.sem.mlr_simulation(), vm_mod_std_fit.sem.mlr_task_summary(), vm_mod_std_fit.sem.mlr_task(), vm_mod_std_fit.sem.mlr(), vm_sev_fit.ols_simulation_summary(), vm_sev_fit.ols_simulation(), vm_sev_fit.ols_task_summary(), vm_sev_fit.ols_task(), vm_sev_fit.ols(), vm_sev_fit.sem.mlr_simulation_summary(), vm_sev_fit.sem.mlr_simulation(), vm_sev_fit.sem.mlr_task_summary(), vm_sev_fit.sem.mlr_task(), vm_sev_fit.sem.mlr(), vm_sev_std_fit.sem.mlr_simulation_summary(), vm_sev_std_fit.sem.mlr_simulation(), vm_sev_std_fit.sem.mlr_task_summary(), vm_sev_std_fit.sem.mlr_task(), vm_sev_std_fit.sem.mlr()


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