as.data.frame | R Documentation |
All pomp model objects can be recast as data frames. The contents of the resulting data frame depend on the nature of the object.
## 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, ...)
## S3 method for class 'wpfilterd_pomp'
as.data.frame(x, ...)
x |
any R object. |
... |
additional arguments to be passed to or from methods. |
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, as well as filtering means, prediction means, and prediction variances, if these have been computed.
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.
When object
is a ‘wpfilterd_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.
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