BayesPGM: Fit Bayesian Piecewise Growth Model

In lockEF/BayesianPGMM: Bayesian Piecewise Growth Mixture Modeling

Fit Bayesian Piecewise Growth Model

Description

Estimates a Bayesian piecewise growth model with linear segments, with a given latent number of possible change points. See [1] for methodological details. To fit a mixture model with two or more classes, see BayesPGMM.

Usage

BayesPGM(X,Y,max_cp=2,cp_prior='binomial',binom_prob=0.5,
		scale_prior='uniform', iters_BurnIn=20000,
		iters_sampling=30000,Thin=15,SaveChains=FALSE)

Arguments

X	An array of dimensions N x M, where the ij'th value gives the j'th time point for subject i.
Y	An array of dimension N x M, where the ij'th value gives the outcome for subject i at the j'th time point. Missing measurements should have the value NA.
max_cp	Maximum number of possible changepoints.
cp_prior	Prior for the number of changepoints, with options 'binomial' or 'uniform'. Default is 'binomial'.
binom_prob	Probability for binomial prior, if specified. Default is 0.5.
scale_prior	Prior for the scale parameter for the hierarchical random effects. The default is 'uniform', a scaled uniform prior; the option 'hc' is a scaled half-cauchy prior. See [1] for more details.
iters_BurnIn	(optional) Number of Gibbs sampling iterations to run for the burn in. Default is 20000.
iters_sampling	(optional) Number of Gibbs sampling iterations to run for posterior sampling. Default is 30000.
Thin	(optional) Will save every k'th posterior sample, where k=Thin. Default is 15.
SaveChains	(optional) If TRUE, raw MCMC samples from rjags will be returned.

Details

For more information on the priors implemented in this package, see [1]. For more control over the priors and aspects of posterior computation, the source functions can be modified directly (enter BayesPGMM in the R console to view source code).

Value

Gelman.msrf	Gelman multivariate scale reduction factor to assess convergence, with the potential scale reduction factor (psrf) for each parameter
y.mean	NxM array giving the fitted value at each time point for each subject.
error.sd	The residual standard deviation.
error.sd.CI	95% CI for the residual standard deviation.
DIC	Deviance information criterion for fitted model.
Save	If SaveChains=TRUE in input, raw MCMC samples from rjags
K_prob	Vector of posterior probabilities for the number of changepoints [0,1,...,K]
K	List where K[[k+1]] gives results for the model with k changepoints.
K[[k+1]]$b.mean	Vector giving the coefficient means [intercept,slope,change at cp1,change at cp2,...] for the k-changepoint model
K[[k+1]]$b.sd	Vector giving the coefficient standard deviations for the k-changepoint model
K[[k+1]]$b.mean.CI	95% CI for the coefficient means for the k-changepoint model
K[[k+1]]$b.sd.CI	95% CI for the coefficient standard deviations for the k-changepoint model
K[[k+1]]$cp.mean	Vector giving mean location of each changepoint in the k-changepoint model
K[[k+1]]$cp.sd	Vector giving standard deviation of each changepoint in the k-changepoint model

Author(s)

Eric F. Lock

References

[1] EF Lock, N Kohli & M Bose (2018). Detecting multiple random changepoints in Bayesian piecewise growth mixture models. Psychometrika, 83 (3): 733-750.

Examples

data(SimData)  ##load simple simulated dataset
X=X[1:10,];Y=Y[1:10,]  ###select subjects from first class only
plotPGM(X,Y) ##Plot the data
Results <- BayesPGM(X,Y) ##Fit PGM (can take about 5 minutes)
plotPGM(X,Y,Results) ##Plot results

lockEF/BayesianPGMM documentation built on Sept. 30, 2022, 6:53 a.m.