sim_lvm | R Documentation |

`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.

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 )

`N` |
Sample size. |

`mla` |
Population loading matrix. |

`K` |
Number of factors (if |

`J` |
Number of items (if |

`cpf` |
Number of cross-loadings per factor (if |

`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 |

`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 |

`cati` |
The set of polytomous items in sequence number (i.e., can be any number set
in between 1 and |

`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. |

An object of class `list`

containing the data, loading, and factorial correlation matrix.

# 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

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