get_smoothmeans: Function to compute the estimated smoothed time series means.

In nategarton13/CrimeDLM.RPackage: Methods for analysis of crime data using dynamic linear models

Description

get_smoothmeans computes the time series means absent of seasonal effects.

Usage

get_smoothmeans(states, crime_types = c("burglary", "robbery"))

Arguments

states	A three dimensional array of sampled states such as that given by get_states.
crime_types	Character vector specifying the types of crimes to include in the analysis. The function requires at least two crime types.

Value

Returns a list with two objects: smoothmeans and crime_types. smoothmeans is a three dimensional array where dimension one corresponds to the sample/iteration, dimension two corresponds to the number of time points, and dimension three corresponds to the crime type. crime_types returns the crime_types argument specified in the function. This serves as a reminder of the ordering of crime types, which is important for interpreting the smoothmeans output as well as making sure that the order of crime types is consistent between other functions.

Examples

 ## Not run: 
 cov_samples <- run_mcmc(data = chicago, chains = 2, adapt_delta = 0.8)
 state_samples <- get_states(mcmc_samples = cov_samples$samples, data = chicago)
 smoothmeans <- get_smoothmeans(states = state_samples$state_samples)
 
## End(Not run)

nategarton13/CrimeDLM.RPackage documentation built on Aug. 8, 2020, 7:49 p.m.