Bysmxmss: 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 intercept and slopes. Data longitudinally measured missing value and having batched information. Fits using MCMC on longitudinal data set

Usage

Bysmxmss(m, tmax, timepoints, group, chains, iter, data)

Arguments

m	Starting number of column from where repeated observations begin
tmax	Maximum batch of visits considered as repeated measurements
timepoints	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
data	High dimensional longitudinal data

Value

Gives posterior means, standard deviation.

Author(s)

Atanu Bhattacharjee and Akash Pawar

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

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data(repdat)

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