hdmnarjg: Missing not at random by MCMC

In longit: High Dimensional Longitudinal Data Analysis Using MCMC

Description

Missing not at random by MCMC

Usage

hdmnarjg(m, n, treatment, n.chains, n.iter, dat)

Arguments

m	Starting column number of repeated observations
n	Ending column number of the repeated observations
treatment	Variable/column name containing the Treatment observations
n.chains	Number of MCMC chains
n.iter	Number of MCMC iterations
dat	Data set containing treatment column and repeated observations

Value

Results containing a data table listing the means and sigma results

Author(s)

Atanu Bhattacharjee, Akash Pawar and Bhrigu Kumar Rajbongshi

References

Bhattacharjee, A. (2020). Bayesian Approaches in Oncology Using R and OpenBUGS. CRC Press.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. CRC press.

Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2012). Applied longitudinal analysis (Vol. 998). John Wiley & Sons.

Examples

##
data(gh)
hdmnarjg(m=1,n=3,treatment="Treatment",n.chains=2,n.iter=10,dat=gh)
##

longit documentation built on April 15, 2021, 9:06 a.m.