simITS (version 0.1.1)

make_model_smoother: Make a smoother that fits a model and then smooths residuals

Description

This helper function gives back a function that takes the resulting simulation data from a single iteration of the simulation, and fits 'fit_model' to it, smoothes the residuals, and puts the predictions from 'fit_model' back.

Usage

make_model_smoother(fit_model, covariates)

Arguments

fit_model

A function that takes data, fits a linear model, and returns the fit model. This function needs an option to include (or not) lagged covariates.

covariates

A dataframe with all covariates needed in the model fitting defined by fit_model.

Value

A smoother function that can be passed to the smoothing routines. This function is of the form listed above.

Details

This can be used to build smoothers that smooth using models other than the model being used for extrapolation (e.g., a model without temperature).

Resulting functions have the following parameters: `res` (the data), `t0` (start time), `outcomename`, `post.only` flag (for smoothing only post data or not), and `smooth_k`, a tuning parameter for degree of smoothing.

Examples

# NOT RUN {
data( "newjersey")
modA = make_fit_season_model( ~ temperature )
modB = make_fit_season_model( ~ sin.m + cos.m )
newjersey = add_lagged_covariates( newjersey, "n.warrant", 
                                   covariates = c("sin.m", "cos.m", "temperature") )
smoother = make_model_smoother( fit_model = modA, covariates = newjersey )
class(smoother)

# Pass made function to process_outcome_model()
envelope = process_outcome_model( "n.warrant", newjersey, t0=-8, R = 1,
                                  summarize = TRUE, smooth=TRUE, 
                                  smoother = smoother, smooth_k = 11,
                                  fit.model = modB )

# }