extrapolate_model: Extrapolate pre-policy data to post-policy era

In lmiratrix/simITS: Analysis via Simulation of Interrupted Time Series (ITS) Data

Extrapolate pre-policy data to post-policy era

Description

This function takes a fitted model and uses it to make post-policy predictions by simulating data.

Usage

extrapolate_model(
  M0,
  dat,
  outcomename = "Y",
  timename = "time",
  t0 = 0,
  R = 400,
  summarize = FALSE,
  smooth = FALSE,
  smoother = smooth_series,
  full_output = FALSE,
  fix_parameters = FALSE,
  ...
)

Arguments

M0	The fit model
dat	Dataframe with data being analyzed.
outcomename	Outcome of interest (name of column).
timename	Name of the time variable (name of column).
t0	Last pre-policy timepoint
R	Number of replications
summarize	Boolean, TRUE means collapse all simulated trajectories into single aggregate. FALSE means return all paths.
smooth	Boolean. TRUE means fit a smoother to the trajectories and look at distribution of smoothed trajectories. FALSE means look at raw data treajectories.
smoother	Function to do smoothing, if smoothing set to TRUE. Default is smooth_series()
full_output	TRUE means smoother returns residuals as well as smoothed series.
fix_parameters	Keep the parameters in the model M0 as fixed; do not add parameter uncertainty.
...	Extra arguments to be passed to smoother (e.g, bandwidth).

Value

Dataframe with columns corresponding to the simulations. If summarize=TRUE, one row per time point in original data. If FALSE, all the details of all the runs are returned.

See Also

process_outcome_model for wrapper function for this method that is easier to use.

Examples

data("mecklenberg" )
mecklenberg = add_lagged_covariates( mecklenberg, "pbail"  )
mecklenberg.pre = dplyr::filter( mecklenberg, month <= 0 )
M0 = fit_model_default( mecklenberg.pre, "pbail" )
res = extrapolate_model( M0, "pbail", mecklenberg, 0, 1,
                         smooth=TRUE)
tail( res )

lmiratrix/simITS documentation built on Sept. 1, 2023, 9:02 p.m.