GPP {GPP}

Estimates a counterfactual with uncertainty using Gaussian process projection

Description

Returns a list of a plot object (after making the plot) of estimated counterfactual values after checking for model convergence and adjusting the noise level, and returns the fitted model.

Usage

GPP(
  df,
  controlVars,
  nUntreated,
  obvColName,
  obvName,
  outcomeName,
  starttime,
  timeColName,
  ncores = NULL,
  epsilon = 0.02,
  noise = 0.1,
  printMod = FALSE,
  shift = 0.05,
  iter = 25000,
  filepath = NULL,
  legendLoc = "topleft",
  xlabel = NULL,
  ylabel = NULL,
  actualdatacol = "black",
  preddatacol = "red",
  ...
)

Arguments

df

The dataframe used for the model.

controlVars

String of column names for control variables.

nUntreated

The number of untreated units in the model.

obvColName

The column name that includes the observation subject to the counterfactual.

obvName

The name of the observation subject to the counterfactual.

outcomeName

The outcome variable of interest.

starttime

The start year of the counterfactual estimation.

timeColName

The name of the column that includes the time variable.

ncores

The number of cores to be used to run the model. See details.

epsilon

The desired level of convergence.

noise

The baseline level of noise to be added to the model to prevent overfit. Updates as the model runs.

printMod

Boolean. Defaults FALSE. If TRUE, prints each model block to the console. See details.

shift

The magnitude of adjustment for the noise level per iteration. Defaults to 0.05.

iter

The number of iterations you would like to run. Defaults to 25,000. See details.

filepath

Your preferred place to save the fit data. See Details.

legendLoc

The preferred location of the legend in the final graph. Defaults to "topleft".

xlabel

The label of the x-axis in the final graph. Defaults to input for 'timeColName'.

ylabel

The preferred label of the y-axis in the final graph. Defaults to input for 'outcomeName'.

actualdatacol

The preferred color for plotted line for actual data. Defaults to black.

preddatacol

The preferred color for plotted line for predicted counterfactual data. Defaults to red.

...

Further parameters passed to the plot function.

Details

We recommend using all cores on your machine to speed up model run time. If you are unsure about the number of cores in your machine, see parallel::detectCores().

We recommend keeping printMod as FALSE, otherwise, the function will write the model to the console for every model run on the convergence.

For iterations, check that your model converged (we recommend all r-hats close to 1 and examining traceplots).

We recommend creating a new folder for the file path since the Stan fit creates a large number of files at runtime.

Value

A plot of the actual values and the estimated counterfactual values of the model, and the final model fit.

Author(s)

Devin P. Brown [e-mail] and David Carlson [e-mail]

See Also

plotGPPfit writeMod runMod autoConverge

Examples

data(GDPdata)
out = GPP(df = GDPdata, 
    controlVars = c('invest', 'school', 'ind'),
    nUntreated = length(unique(GDPdata$country))-1, 
    obvColName = 'country', obvName = 'West Germany', 
    outcomeName = 'gdp', starttime = 1989, 
    timeColName = 'year',
    ncores = 2)