PandemicLP (version 1.3.0)

print.summary.pandemicEstimated: Summary method for pandemicEstimated objects

Description

The summary method for pandemicEstimated object of class S3 displays a compact summary of the fitted model. See the Details section below for descriptions of the different components of the printed output.

Usage

# S3 method for summary.pandemicEstimated
print(x, ...)
# S3 method for pandemicEstimated
summary(object, probs = c(0.025, 0.5, 0.975), digits = 3, info = TRUE, ...)

Arguments

x

an object of class "summary.pandemicEstimated".

...

Currently unused.

object

An object of S3 class pandemicEstimated-objects.

probs

A numeric vector of quantiles of interest. The default is c(0.025,0.5,0.975).

digits

Number of digits to use for formatting numbers.

info

TRUE or FALSE: more details for output interpretation. The Default is TRUE.

Value

The summary method returns an object of class "summary.pandemicEstimated", which is a list with two arrays of summary statistics and diagnostics and others informations for use by the print method. The print method for summary.pandemicEstimated objects is called for its side effect and just returns its input.

Details

Point estimates

Mean and Quantiles computed from simulations. For models fit using MCMC ("sampling", this is default algorithim of pandemic_model function), the posterior sample is used. For others estimation algorithm see sampling (rstan package).

Convergence and efficiency diagnostics for Markov Chains

Included in the summary are: split effective sample sizes (n_eff), Monte Carlo standard errors (se_mean) and split Rhats.

The Monte Carlo standard error provides relevant information for a posterior sample with more than one chain.

The R-hat convergence diagnostic compares the between- and within-chain estimates for model parameters and other univariate quantities of interest. If chains have not mixed well (ie, the between- and within-chain estimates don't agree), R-hat is larger than 1. We recommend running at least four chains by default and only using the sample if R-hat is less than 1.05.

covidLPconfig

This subsection shows the main input settings used by the fitted model, and indicates whether default settings of the CovidLP app (http://est.ufmg.br/covidlp/home/en/) were used (covidLPconfig = TRUE or FALSE). Check the default settings of the CovidLP app in pandemic_model.

Priors

A list with information about the prior distributions used and model restrictions (if there are any). For more information go to models.

Examples

# NOT RUN {
Y0=load_covid(country_name="Brazil",state_name="SP",last_date='2020-04-25')
output0=pandemic_model(Y0)
s=summary(output0)
s          #print method for summary.pandemicEstimated
names(s)  #see output list elements of summary
# }
# NOT RUN {
# }