twindata

<p>Simulated data</p><table><tr><td>
        id    </td>
<td> numeric </td>
<td> Twin-pair id </td>
</tr><tr><td>
        zyg    </td>
<td> character </td>
<td> Zygosity (MZ or DZ) </td>
</tr><tr><td>
        twinnum    </td>
<td> numeric </td>
<td> Twin number (1 or 2) </td>
</tr><tr><td>
        agemena    </td>
<td> numeric </td>
<td> Age at menarche (or censoring) </td>
</tr><tr><td>
        status    </td>
<td> logical </td>
<td> Censoring status (observed:=T,censored:=F) </td>
</tr><tr><td>
        bw    </td>
<td> numeric  </td>
<td> Birth weight </td>
</tr><tr><td>
        msmoke    </td>
<td> numeric </td>
<td> Did mother smoke? (yes:=1,no:=0) </td>
</tr></table>

datasets

A general implementation of Structural Equation Models
with latent variables (MLE, 2SLS, and composite likelihood
estimators) with both continuous, censored, and ordinal
outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
Mixture latent variable models and non-linear latent variable models
(Holst and Budtz-Joergensen (2018) <arXiv:1803.06578>).
The package also provides methods for graph exploration (d-separation,
back-door criterion), simulation of general non-linear latent variable
models, and estimation of influence functions for a broad range of
statistical models.

twindata: Twin menarche data

Description

Arguments

Format

id	numeric	Twin-pair id
zyg	character	Zygosity (MZ or DZ)
twinnum	numeric	Twin number (1 or 2)
agemena	numeric	Age at menarche (or censoring)
status	logical	Censoring status (observed:=T,censored:=F)
bw	numeric	Birth weight
msmoke	numeric	Did mother smoke? (yes:=1,no:=0)