Bysmxms: Bayesian mixed model with random intercepts and random slopes...

In longit: High Dimensional Longitudinal Data Analysis Using MCMC

Description

Bayesian mixed effect model with random intercepts and slopes with longitudinally measured missing data. Fits using MCMC on longitudinal data set

Usage

Bysmxms(m, n, time, group, chains, n.adapt, data)

Arguments

m	Starting number of column from where repeated observations begin
n	Ending number of columns till where the repeated observations ends
time	Timepoint information on which repeadted observations were taken
group	A categorical variable either 0 or 1. i.e. Gender - 1 male and 0 female
chains	Number of MCMC chains to be performed
n.adapt	Number of iterations to run in the JAGS adaptive phase.
data	High dimensional longitudinal data

Value

Gives posterior means, standard deviation.

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(mesrep)
Bysmxms(m=4,n=7,time="Age",group="Gender",chains=4,n.adapt=100,data=msrep)
##

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