Bayesian Non-Parametric Dependent Models for Time-Indexed Functional Data

[Up] [Top]

Documentation for package ‘growfunctions’ version 0.16

Help Pages

growfunctions-package Bayesian Non-Parametric Models for Estimating a Set of Denoised, Latent Functions From an Observed Collection of Domain-Indexed Time-Series
cluster_plot Plot estimated functions for experimental units faceted by cluster versus data to assess fit.
cps Monthly employment counts from 1990 - 2013 from the Current Population Survey
fit_compare Side-by-side plot panels that compare latent function values to data for different estimation models
gen_informative_sample Generate a finite population and take an informative single or two-stage sample.
gmrfdpcountPost Run a Bayesian functional data model under an instrinsic GMRF prior whose precision parameters employ a DP prior for a COUNT data response type where: y ~ poisson(E*exp(Psi)) Psi ~ N(gamma,tau_e^-1) which is a Poisson-lognormal model
gmrfdpgrow Bayesian instrinsic Gaussian Markov Random Field model for dependent time-indexed functions
gmrfdpgrow.default Bayesian instrinsic Gaussian Markov Random Field model for dependent time-indexed functions
gmrfdpPost Run a Bayesian functional data model under an instrinsic GMRF prior whose precision parameters employ a DP prior
gpBFixPost Run a Bayesian functional data model under a GP prior with a fixed clustering structure that co-samples latent functions, 'bb_i'.
gpdpbPost Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior
gpdpgrow Bayesian non-parametric dependent Gaussian process model for time-indexed functional data
gpdpgrow.default Bayesian non-parametric dependent Gaussian process model for time-indexed functional data
gpdpPost Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior
gpFixPost Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior
gpPost Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior
growfunctions Bayesian Non-Parametric Models for Estimating a Set of Denoised, Latent Functions From an Observed Collection of Domain-Indexed Time-Series
informative_plot Plot credible intervals for parameters to compare ignoring with weighting an informative sample
MSPE Compute normalized mean squared prediction error based on accuracy to impute missing data values
package-growfunctions Bayesian Non-Parametric Models for Estimating a Set of Denoised, Latent Functions From an Observed Collection of Domain-Indexed Time-Series
plot_cluster Plot estimated functions, faceted by cluster numbers, for a known clustering
predict_functions Use the model-estimated covariance parameters from gpdpgrow() or gmrdpgrow to predict the function at future time points.
predict_functions.gmrfdpgrow Use the model-estimated iGMRF precision parameters from gmrfdpgrow() to predict the iGMRF function at future time points. Inputs the 'gmrfdpgrow' object of estimated parameters.
predict_functions.gpdpgrow Use the model-estimated GP covariance parameters from gpdpgrow() to predict the GP function at future time points. Inputs the 'gpdpgrow' object of estimated parameters.
predict_plot Plot estimated functions both at estimated and predicted time points with 95% credible intervals.
samples Produce samples of MCMC output
samples.gmrfdpgrow Produce samples of MCMC output
samples.gpdpgrow Produce samples of MCMC output