as_data_frame: Coerce to data frame

In kidusasfaw/pomp: Statistical Inference for Partially Observed Markov Processes

Description

All pomp model objects can be recast as data frames. The contents of the resulting data frame depend on the nature of the object.

Usage

## S3 method for class 'pomp'
as.data.frame(x, ...)

## S3 method for class 'pfilterd_pomp'
as.data.frame(x, ...)

## S3 method for class 'probed_pomp'
as.data.frame(x, ...)

## S3 method for class 'kalmand_pomp'
as.data.frame(x, ...)

## S3 method for class 'bsmcd_pomp'
as.data.frame(x, ...)

## S3 method for class 'pompList'
as.data.frame(x, ...)

## S3 method for class 'pfilterList'
as.data.frame(x, ...)

## S3 method for class 'abcList'
as.data.frame(x, ...)

## S3 method for class 'mif2List'
as.data.frame(x, ...)

## S3 method for class 'pmcmcList'
as.data.frame(x, ...)

Arguments

x	the object to be coerced
...	ignored

Details

When object is a simple ‘pomp’ object, as(object,"data.frame") or as.data.frame(object) results in a data frame with the times, observables, states (if known), and interpolated covariates (if any).

When object is a ‘pfilterd_pomp’ object, coercion to a data frame results in a data frame with the same content as for a simple ‘pomp’, but with conditional log likelihood and effective sample size estimates included.

When object is a ‘probed_pomp’ object, coercion to a data frame results in a data frame with the values of the probes computed on the data and on simulations.

When object is a ‘kalmand_pomp’ object, coercion to a data frame results in a data frame with prediction means, filter means and forecasts, in addition to the data.

When object is a ‘bsmcd_pomp’ object, coercion to a data frame results in a data frame with samples from the prior and posterior distribution. The .id variable distinguishes them.

kidusasfaw/pomp documentation built on May 20, 2019, 2:59 p.m.