boot_lav: Bootstrap SEM Parameter Estimates

In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Description

Perform nonparametric and parametric bootstrapping with percentile and bias corrected confidence intervals on SEM parameter estimates.

Usage

boot_lav(
  data,
  model,
  raw_data = TRUE,
  mean_vector = NULL,
  n = NULL,
  B = 2000,
  cluster = TRUE,
  cores = NULL,
  nb = FALSE,
  ...
)

Arguments

data	Matrix of sample data to bootstrap.
model	Specified model to fit following lavaan notation.
raw_data	Logical. If TRUE, data is a sample raw data. If FALSE, data is a sample variance-covariance matrix.
mean_vector	A sample mean vector. Ignored if raw_data = TRUE.
n	Sample size. Ignored if raw_data = TRUE.
B	Number of bootstrap resamples.
cluster	Logical. If TRUE, parallelize computations using a cluster.
cores	Integer. Number of cores to use. Defaults to total number of threads minus 1.
nb	Logical. If TRUE, performs nonparametric bootstrapping. Ignored if raw_data = FALSE.
...	Arguments to pass to fitFUN.

See Also

Other bootstrap functions: boot_fit(), boot_nb(), boot_pb_med_simple(), boot_pb_vm(), boot_pb()

jeksterslabds/jeksterslabRds documentation built on July 16, 2020, 3:41 p.m.