Package 'GPP'

Title: Gaussian Process Projection
Description: Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.
Authors: Devin P. Brown [aut], David Carlson [aut, cre]
Maintainer: David Carlson <[email protected]>
License: GPL (>= 2)
Version: 0.1
Built: 2025-02-15 03:54:58 UTC
Source: https://github.com/cran/GPP

Help Index

Checks Stan model for convergence, then runs model on actual data.

Description

Return a converged Stan model fit and the recommended noise level.

Usage

autoConverge(
  df,
  controlVars,
  nUntreated,
  obvColName,
  obvName,
  outcomeName,
  starttime,
  timeColName,
  filepath = NULL,
  ncores = NULL,
  iter = 25000,
  epsilon = 0.02,
  noise = 0.1,
  printMod = FALSE,
  shift = 0.05
)

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 time of the counterfactual estimation.
timeColName

The name of the column that includes the time variable.
filepath

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

The number of cores to be used to run the model. Default of NULL will utilize all cores.
iter

Preferred number of iterations. See details.
epsilon

The desired level of convergence, i.e. how close to the 0.95 coverage is acceptable.
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 the model block for the run to the console. See details.
shift

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

Details

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

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

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

We also 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 doParallel::detectCores().

Value

The recommended noise level after convergence.

Author(s)

Devin P. Brown [email protected] and David Carlson [email protected]

See Also

plotGPPfit runMod GPP writeMod

1960-2003 GDP dataset

Description

An example dataset for using GPP to estimate the counterfactual GDP of West Germany assuming no reunification.

Usage

GDPdata

Format

A data frame with 748 rows and 14 columns. For detailed explanations of the exact measures, see https://www.dropbox.com/s/n1bvqb54xrw8vyj/GPSynth.pdf?dl=0:

index
country
year
gdp
infrate
trade
schooling
invest60
invest70
invest80
industry
invest
school
ind

See Also

GPP plotGPPfit writeMod runMod autoConverge

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 [email protected] and David Carlson [email protected]

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)

Plots results of a (converged) model, with true and projected values.

Description

Takes the results of a Gaussian Process Projection fit and generates a linear plot of the actual and predicted counterfactual values

Usage

plotGPPfit(
  fit,
  df,
  obvColName,
  obvName,
  outcomeName,
  starttime,
  timeColName,
  legendLoc = "topleft",
  xlabel = NULL,
  ylabel = NULL,
  actualdatacol = "black",
  preddatacol = "red",
  ...
)

Arguments

fit

The fit results of the GPP stan model.
df

The dataframe used in your model.
obvColName

The column name that includes your observation of interest. Must be a string.
obvName

The name of the specific observation of interest. Must be a string.
outcomeName

The explanatory variable that is subjected to the counterfactual claim.
starttime

The start time of the treatment effect.
timeColName

The name of the column that includes your time variable.
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 graphical parameters.

Value

A plot built in r-base

Author(s)

Devin P. Brown [email protected] and David Carlson [email protected]

See Also

autoConverge GPP runMod writeMod

Runs the model, given the data and treated case (may be a placebo).

Description

Returns a fit of the Stan model for all observations.

Usage

runMod(modText, dataBloc, unit, iter = 25000, filepath = NULL)

Arguments

modText

This is the string that contains your Stan code. Can be written with writeMod.
dataBloc

This is the data that you pass to the Stan code. It is automatically generated when you run autoConverge.
unit

The unit of observation to project.
iter

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

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

Details

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

The fit for the GPP counterfactual Stan model.

Author(s)

Devin P. Brown [email protected] and David Carlson [email protected]

See Also

plotGPPfit writeMod GPP autoConverge

Writes Stan code for GPP model

Description

Returns string of Stan code that can be run to estimate the GPP.

Usage

writeMod(noise, ncov, printMod = FALSE)

Arguments

noise

The desired amount of artificial noise to add to the model.
ncov

The number of covariates to include in the model.
printMod

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

Details

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

Value

A string of Stan code that can be run with runMod

Author(s)

Devin P. Brown [email protected] and David Carlson [email protected]

See Also

plotGPPfit runMod GPP autoConverge

Examples

writeMod(noise = 0.25, ncov = 2)