Package 'twangRDC'

Balance tables for twangRDC

Description

bal.table calculates balance tables from a ps.xgb object

Usage

bal.table(
  x,
  type = "overall",
  n = 10,
  decreasing = TRUE,
  which.sort = "adj",
  include.var = FALSE
)

Arguments

x	A ps.xgb object.
type	An optional character string requesting if balance should be summarized overall ("overall") or by strata ("strata"). Default: "overall".
n	An integer specifying the number of rows to print in the balance table. Default: 10.
decreasing	A logical value indicating if the balance table should be sorted in increasing or decreasing order.
which.sort	An optional character string indicating if the balance table should be sorted by the adjusted ("adj") or unadjusted ("unadj") absolute standardized differences. Default: "adj".
include.var	A logical value indicating if the variable corresponding the the maximum absolute standardized difference within strata should be included in the balance table. Only valid when strata=TRUE. Default: FALSE.

Value

Returns a table.

Examples

# See vignette for examples.

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Exact weights from a ps.xgb object

Description

Extracts weights from a ps.xgb object, output with the unique identifier for easy merging.

Usage

get.weights(x)

Arguments

x	An object of class ps.xgb

Value

Returns a data frame.

Examples

# See vignette for examples.

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An example FSRDC dataset

Description

See vignette for detailed description of the data.

Usage

data(nola_south)

Format

A data frame

• tract_id_str. Census tract identifier

• metarea. metropolitan area

• c00_age12. categorical variable for age

• c00_sex. sex

• c00_race. categorical variable for race

• c00_nphu. number of persons in housing unit

• hhid. household identifier

• sim_pik. indicator of PIK assignment

• nola_rec. indicator of record from Orleans Parish

• id. individual identifier

A data frame with 18396 rows and 10 variables

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Plot for a ps.xbg object

Description

ps.xgb.plot produces a figure showing the balance criteria by iteration for a ps.xgb object.

Usage

## S3 method for class 'ps.xgb'
plot(x, ...)

Arguments

x	An object of class ps.xgb
...	Arguments to be passed to other functions

Value

Returns a ggplot object.

Examples

# See vignette for examples.

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Gradient boosted propensity score estimation

Description

ps.xgb calculates propensity scores using gradient boosted logistic regression and diagnoses the resulting propensity scores using a variety of methods

Usage

ps.xgb(
  formula = formula(data),
  strata = NULL,
  data,
  params,
  file = NULL,
  max.steps = Inf,
  iters.per.step = 100,
  id.var,
  min.iter = 1000,
  min.width = NULL,
  verbose = TRUE,
  save.model = FALSE,
  weights = NULL,
  linkage = TRUE
)

Arguments

formula	An object of class formula: a symbolic description of the propensity score model to be fit with the treatment indicator on the left side of the formula and the variables to be balanced on the right side.
strata	An optional factor variable identifying the strata. If specified, balance is optimized within strata.
data	A dataset.
params	xgboost parameters.
file	An optional character string naming a file to save intermediate results.
max.steps	An integer specifying the maximum number of steps to take. Note that max.steps*iters.per.step must be greater than or equal to min.iter. Default: Inf.
iters.per.step	An integer specifying the number of iterations to add to the model at each step of algorithm. Note that max.steps*iters.per.step must be greater than or equal to min.iter. Default: 100.
id.var	A variable that uniquely identifies observations.
min.iter	An integer specifying the minimum number of iterations before checking for convergence. Note that max.steps*iters.per.step must be greater than or equal to min.iter. Default: 1000.
min.width	An integer specifying the minimum number of iterations between the current number of iterations and the optimal value. Default: 5*iters.per.step.
verbose	A logical value indicating if the function should update the user on its progres Default: TRUE.
save.model	A logical value indicating if the xgboost model be saved as part of the output object. Default: FALSE.
weights	An optional variable that identifies user defined weights to be incorporated into the optimization.
linkage	An indicator of whether the weighting should be for linkage failure (or nonresponse) versus comparison group construction. A value of TRUE requests weighting to account for linkage failure, while a value of FALSE requests weighting for comparison group construction. Default: TRUE.

Value

Returns an object of class ps.xgb, a list containing

• bal.tab A table summarizing the balance at the optimal number of iterations.

• es A table summarizing the standardized differences within strata at the optimal number of iterations.

• es.max A table summarizing the maximum absolute standardized difference by strata.

• es.meanA table summarizing the mean absolute standardized difference by strata.

• iter.per.step Saves the value of iters.per.step specified by the user.

• opt.iter The optimal number of iterations.

• strata A list of the strata used in the optimization.

• weight.data A dataset containing the unique ID and the optimal weight for each observation.

References

Dan McCaffrey, G. Ridgeway, Andrew Morral (2004). "Propensity Score Estimation with Boosted Regression for Evaluating Adolescent Substance Abuse Treatment", Psychological Methods 9(4):403-425.

See Also

twang::ps, xgboost

Examples

# See the vignette for examples.

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