jointly.generate.binary.normal {BinNor} R Documentation

## Generates a mix of binary and normal data

### Description

Generates multiple binary and normal variables simultaneously given marginal characteristics and association structures.

### Usage

```jointly.generate.binary.normal(no.rows, no.bin,
no.nor, prop.vec.bin = NULL, mean.vec.nor = NULL, var.nor = NULL,
sigma_star = NULL, corr.vec = NULL, corr.mat = NULL,
continue.with.warning = TRUE)
```

### Arguments

 `no.rows` Number of rows. `no.bin` Number of binary variables `no.nor` Number of normal variables `prop.vec.bin` Probability vector for binary variables `mean.vec.nor` Vector of means for normal variables `var.nor` Vector of variances for normal variables `sigma_star` Intermediate correlation matrix `corr.vec` Vector of elements below the diagonal of correlation matrix ordered columnwise `corr.mat` Specified correlation matrix `continue.with.warning` TRUE to proceed with the nearest positive definite Σ^*. FALSE to terminate program execution if Σ^* is not positive definite

### Value

 `data ` A matrix of generated data.

`compute.sigma.star`, `validation.corr`, `validation.bin`, `validation.nor`, `nearPD`, `simulation`, `rmvnorm`

### Examples

```
no.rows=100
no.bin=2; no.nor=2
mean.vec.nor=c(3,1); var.nor=c(4,2)
prop.vec.bin=c(0.4,0.7)
corr.vec=c(0.16,0.04,0.38,0.14,0.47,0.68);

cmat = lower.tri.to.corr.mat(corr.vec,4)
sigma.star=compute.sigma.star(no.bin=2, no.nor=2, prop.vec.bin=c(0.4,0.7),
corr.mat=cmat)
mydata=jointly.generate.binary.normal(no.rows,no.bin,no.nor,prop.vec.bin,
mean.vec.nor,var.nor, sigma_star=sigma.star\$sigma_star,
continue.with.warning=TRUE)
```

[Package BinNor version 2.3.3 Index]