eco (version 3.1-7)

summary.ecoNP: Summarizing the Results for the Bayesian Nonparametric Model for Ecological Inference in 2x2 Tables

Description

summary method for class ecoNP.

Usage

# S3 method for ecoNP
summary(object, CI = c(2.5, 97.5), param = FALSE,
          units = FALSE, subset = NULL, ...)
  # S3 method for summary.ecoNP
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

object

An output object from ecoNP.

CI

A vector of lower and upper bounds for the Bayesian credible intervals used to summarize the results. The default is the equal tail 95 percent credible interval.

x

An object of class summary.ecoNP.

digits

the number of significant digits to use when printing.

param

Logical. If TRUE, the posterior estimates of the population parameters will be provided. The default value is FALSE.

units

Logical. If TRUE, the in-sample predictions for each unit or for a subset of units will be provided. The default value is FALSE.

subset

A numeric vector indicating the subset of the units whose in-sample predications to be provided when units is TRUE. The default value is NULL where the in-sample predictions for each unit will be provided.

...

further arguments passed to or from other methods.

Value

summary.ecoNP yields an object of class summary.ecoNP containing the following elements:

call

The call from ecoNP.

n.obs

The number of units.

n.draws

The number of Monte Carlo samples.

agg.table

Aggregate posterior estimates of the marginal means of \(W_1\) and \(W_2\) using \(X\) and \(N\) as weights.

If param = TRUE, the following elements are also included:

param.table

Posterior estimates of model parameters: population mean estimates of \(W_1\) and \(W_2\). If subset is specified, only a subset of the population parameters are included.

If unit = TRUE, the following elements are also included:

W1.table

Unit-level posterior estimates for \(W_1\).

W2.table

Unit-level posterior estimates for \(W_2\).

This object can be printed by print.summary.ecoNP

See Also

ecoNP, predict.eco