longit (version 0.1.0)

BysmxHPD: Bayesian mixed effect model for high dimensional longitduinal data with highest posterior density interval (HPDI).

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.

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

# NOT RUN {
##
data(msrep)
BysmxHPD(m=c(4,8,12),tmax=4,t="Age",group="Gender",chains=4,iter=1000,out="hpD",data=msrep)
##
# }