UPSaltdd: Artificial Distribution of LTDs from Random Clusters

Description Usage Arguments Details Value Author(s) References See Also

Description

For a given number of clusters, UPSaltdd() characterizes the potentially biased distribution of "Local Treatment Differences" (LTDs) in a continuous outcome y-variable between two treatment groups due to Random Clusterings. When the NNobj argument is not NA and specifies an existing UPSnnltd() object, UPSaltdd() also computes a smoothed CDF for the NN/LTD distribution for direct comparison with the Artificial LTD distribution.

Usage

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UPSaltdd(envir, dframe, trtm, yvar, faclev = 3, scedas = "homo",
  NNobj = NA, clus = 50, reps = 10, seed = 12345)

Arguments

envir

name of the working local control classic environment.

dframe

Name of data.frame containing a treatment-factor and the outcome y-variable.

trtm

Name of treatment factor variable with two levels.

yvar

Name of continuous outcome variable.

faclev

Maximum number of different numerical values an outcome variable can assume without automatically being converted into a "factor" variable; faclev=1 causes a binary indicator to be treated as a continuous variable determining an average or proportion.

scedas

Scedasticity assumption: "homo" or "hete"

NNobj

Name of an existing UPSnnltd object or NA.

clus

Number of Random Clusters requested per Replication; ignored when NNobj is not NA.

reps

Number of overall Replications, each with the same number of requested clusters.

seed

Seed for Monte Carlo random number generator.

Details

Multiple calls to UPSaltdd() for different UPSnnltd objects or different numbers of clusters are typically made after first invoking UPSgraph().

Value

Author(s)

Bob Obenchain <[email protected]>

References

Obenchain RL. (2004) Unsupervised Propensity Scoring: NN and IV Plots. Proceedings of the American Statistical Association (on CD) 8 pages.

Obenchain RL. (2011) USPSinR.pdf USPS R-package vignette, 40 pages.

Rosenbaum PR, Rubin RB. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70: 41-55.

Rubin DB. (1980) Bias reduction using Mahalanobis metric matching. Biometrics 36: 293-298.

See Also

UPSnnltd, UPSaccum and UPSgraph.


LocalControl documentation built on May 2, 2019, 7:29 a.m.