BysmxHPD: Bayesian mixed effect model for high dimensional longitduinal...

In longit: High Dimensional Longitudinal Data Analysis Using MCMC

Description

Bayesian mixed effect model with random intercept and slopes provides inference with highest posterior density interval (HPDI). Data longitudinally measured missing value and having batched information. Fits using MCMC on longitudinal data set

Usage

BysmxHPD(m, tmax, t, group, chains, iter, out, data)

Arguments

m	Starting number of column from where repeated observations begin
tmax	Ending number of columns till where the repeated observations ends
t	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
iter	Number of iterations to be performed
out	DIC/HPD outcome
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(msrep)
BysmxHPD(m=c(4,8,12),tmax=4,t="Age",group="Gender",chains=4,iter=1000,out="hpD",data=msrep)
##

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