get_means: Function to compute the estimated time series means.

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

Description

get_means computes the linear combination of parameters defined to be the data model mean for a given set of sampled states.

Usage

get_means(states, harmonics = 4, crime_types = c("burglary", "robbery"))

Arguments

states	A three dimensional array of sampled states such as that given by get_states.
harmonics	An integer in 1 to 6 specifiying the number of harmonics to model seasonality. Must be consistent with specification in run_mcmc().
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: ts_means and crime_types. ts_means 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 ts_means 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$samples, data = chicago)
 ts_means <- get_means(states = state_samples$state_samples)
 
## End(Not run)

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