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 pomp
as.data.frame(x, ...)# S3 method for pfilterd_pomp
as.data.frame(x, ...)
# S3 method for probed_pomp
as.data.frame(x, ...)
# S3 method for kalmand_pomp
as.data.frame(x, ...)
# S3 method for bsmcd_pomp
as.data.frame(x, ...)
# S3 method for pompList
as.data.frame(x, ...)
# S3 method for pfilterList
as.data.frame(x, ...)
# S3 method for abcList
as.data.frame(x, ...)
# S3 method for mif2List
as.data.frame(x, ...)
# S3 method for pmcmcList
as.data.frame(x, ...)
# S4 method for pomp
melt(data, ..., na.rm = FALSE, value.name = "value")
any R object.
additional arguments to be passed to or from methods.
Data set to melt
Should NA values be removed from the data set? This will convert explicit missings to implicit missings.
name of variable used to store values
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.
A ‘pomp’ object can be melted into a data frame.