sim_lvm: Simulating data with Latent Variable Modeling

In LAWBL: Latent (Variable) Analysis with Bayesian Learning

Simulating data with Latent Variable Modeling

Description

sim_lvm can simulate data based on factor analysis or item response models with different response formats (continuous or categorical), loading patterns and residual covariance (local dependence) structures.

Usage

sim_lvm(
  N = 1000,
  mla = NULL,
  K = 3,
  J = 18,
  cpf = 0,
  lam = 0.7,
  lac = 0.3,
  phi = 0.3,
  ph12 = -1,
  ecr = 0,
  P = 0,
  b = 0.3,
  K1 = 0,
  ph1 = 0.2,
  b1 = 0.3,
  ilvl = NULL,
  cati = NULL,
  noc = c(4),
  misp = 0,
  ome_out = FALSE,
  necw = K,
  necb = K,
  add_ind = c(),
  add_la = 0.5,
  add_phi = 0,
  zero_it = 0,
  rseed = 333,
  digits = 4
)

Arguments

N	Sample size.
mla	Population loading matrix.
K	Number of factors (if mla=NULL).
J	Number of items (if mla=NULL).
cpf	Number of cross-loadings per factor (if mla=NULL).
lam	Number of formal iterations for posterior sampling.
lac	Number of iterations to update the sampling information.
phi	Homogeneous correlations between any two factors.
ph12	Correlation between factor 1 and 2 (if it's different from phi.
ecr	Residual correlation (local dependence).
P	Number of observable predictors (for MIMIC model).
b	Coefficients of observable predictors (for MIMIC model).
K1	Number of latent predictors (for MIMIC model).
ph1	Correlations between latent predictors (for MIMIC model).
b1	Coefficients of latent predictors (for MIMIC model).
ilvl	Specified levels of all items (i.e., need to specify Item 1 to J+P); Any value smaller than 2 is considered as continuous item.
cati	The set of polytomous items in sequence number (i.e., can be any number set in between 1 and J+P); NULL for no and -1 for all (if ilvl=NULL).
noc	Number of levels for polytomous items.
misp	Proportion of missingness.
ome_out	Output factor score or not.
necw	Number of within-factor local dependence.
necb	Number of between-factor local dependence.
add_ind	(Additional) minor factor with cross-loadings.
add_la	Value of cross-loadings on (Additional) minor factor.
add_phi	Correlations between (Additional) minor factor and other factors.
zero_it	Surplus items with zero loading.
rseed	An integer for the random seed.
digits	Number of significant digits to print when printing numeric values.

Value

An object of class list containing the data, loading, and factorial correlation matrix.

Examples

# for continuous data with cross-loadings and local dependence effect .3
out <- sim_lvm(N=1000,K=3,J=18,lam = .7, lac=.3,ecr=.3)
summary(out$dat)
out$MLA
out$ofd_ind

# for categorical data with cross-loadings .4 and 10% missingness
out <- sim_lvm(N=1000,K=3,J=18,lam = .7, lac=.4,cati=-1,noc=4,misp=.1)
summary(out$dat)
out$MLA
out$ofd_ind

LAWBL documentation built on May 16, 2022, 9:06 a.m.