# sim_lvm: Simulating data with Latent Variable Modeling In LAWBL: Latent (Variable) Analysis with Bayesian Learning

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```sim_lvm( N = 1000, K = 3, ipf = 8, cpf = 2, lam = 0.7, lac = 0.3, phi = 0.5, ph1 = -1, ecr = 0, ome_out = FALSE, cati = NULL, noc = c(4), misp = 0, rseed = 333, necw = K, necb = K, add_ind = c(), add_la = 0.5, add_phi = 0, zero_it = 0, digits = 4 ) ```

## Arguments

 `N` Sample size. `K` Number of factors. `ipf` Items per factor. `cpf` Cross-loadings per factor. `lam` Number of formal iterations for posterior sampling. `lac` Number of iterations to update the sampling information. `phi` Homogeneous correlations between any two factors. `ph1` Correlation between factor 1 and 2 (if it's different from `phi`. `ecr` Residual correlation (local dependence). `ome_out` Output factor score or not. `cati` The set of categorical (polytomous) items in sequence number (i.e., 1 to J); `NULL` for no and -1 for all (default is `NULL`). `noc` Number of categories for categorical items `misp` Proportion of missingness. `rseed` An integer for the random seed. `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. `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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# for continuous data with cross-loadings and local dependence effect .3 out <- sim_lvm(N=1000,K=3,ipf=6,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,ipf=6,lam = .7, lac=.4,cati=-1,noc=4,misp=.1) summary(out\$dat) out\$MLA out\$ofd_ind ```

LAWBL documentation built on April 2, 2021, 1:05 a.m.