barp: BARP

In jbisbee1/BARP: Mister P with Bayesian Additive Regression Trees

BARP

Description

This function uses Bayesian Additive Regression Trees (BART) to extrapolate survey data to a level of geographic aggregation at which the original survey was not sampled to be representative of.

Usage

barp(
  y,
  x,
  dat,
  census,
  geo.unit,
  algorithm = "BARP",
  proportion = "None",
  cred_int = c(0.025, 0.975),
  BSSD = F,
  nsims = 200,
  setSeed = NULL,
  ...
)

Arguments

y	Outcome of interest. Should be a character of the column name containing the variable of interest.
x	Prognostic covariates. Should be a vector of column names corresponding to the covariates used to predict the outcome variable of interest.
dat	Survey data containing the x and y column names.
census	Census data containing the x column names. If the user provides raw census data, BARP will calculate proportions for each unique bin of x covariates. Otherwise, the researcher must calculate bin proportions and indicate the column name that contains the proportions, either as percentages or as raw counts.
geo.unit	The column name corresponding to the unit at which outcomes should be aggregated.
algorithm	Algorithm for predicting opinions. Can be any algorithm(s) included in the SuperLearner package. If multiple algorithms are listed, predicted opinions are provided for each separately, as well as for the weighted ensemble. Defaults to BARP which implements Bayesian Additive Regression Trees via bartMachine.
proportion	The column name corresponding to the proportions for covariate bins in the Census data. If left to the default None value, BARP assumes raw census data and estimates bin proportions automatically.
cred_int	A vector giving the lower and upper bounds on the credible interval for the predictions.
BSSD	Calculate bootstrapped standard deviation. Defaults to FALSE in which case the standard deviation is generated by BART's default.
nsims	The number of bootstrap simulations.
setSeed	Seed to control random number generation.
...	Additional arguments to be passed to bartMachine or SuperLearner.

Value

Returns an object of class “BARP”, containing a list of the following components:

pred.opn	A data.frame where each row corresponds to the geographic unit of interest and the columns summarize the predicted outcome and the upper and lower bounds for the given credible interval (cred_int).
trees	A bartMachine object. See bartMachine for details.
risk	A data.frame containing the cross-validation risk for each algorithm and the associated weight used in the ensemble predictions. Only useful when multiple algorithms are used.

See Also

bartMachine which this program uses to implement Bayesian Additive Regression Trees.

SuperLearner which this program uses to implement alternative regularizers.

Examples

data("gaymar")
barp <- barp(y = "supp_gaymar",
             x = c("pvote","religcon","age","educ","gXr","stateid","region"),
             dat = svy,
             census = census06,
             geo.unit = "stateid",
             proportion = "n",
             cred_int = c(0.025,0.975))

jbisbee1/BARP documentation built on Jan. 5, 2023, 9:15 a.m.