UPSaltdd {USPS}R Documentation

Artificial Distribution of LTDs from Random Clusters

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

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

Arguments

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

An output list object of class UPSaltdd:

dframe Name of data.frame containing X, t & Y variables.
trtm Name of treatment factor variable.
yvar Name of outcome Y 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.
reps Number of overall Replications, each with the same number of requested clusters.
pats Number of patients with no NAs in their yvar outcome and trtm factor.
seed Seed for Monte Carlo random number generator.
altdd Matrix of LTDs and relative weights from artificial clusters.
alxmin Minimum artificial LTD value.
alxmax Maximum artificial LTD value.
alymax Maximum weight among artificial LTDs.
altdcdf Vector of artificial LTD x-coordinates for smoothed CDF.
qq Vector of equally spaced CDF values from 0.0 to 1.0.
nnltdd Optional matrix of relevant NN/LTDs and relative weights.
nnlxmin Optional minimum NN/LTD value.
nnlxmax Optional maximum NN/LTD value.
nnlymax Optional maximum weight among NN/LTDs.
nnltdcdf Optional vector of NN/LTD x-coordinates for smoothed CDF.
nq Optional vector of equally spaced CDF values from 0.0 to 1.0.

Author(s)

Bob Obenchain <wizbob@att.net>

References

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

Obenchain RL. (2009) USPSinR.pdf ../R_HOME/library/USPS 41 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.

Examples

    data(lindner)
    abcdf <- UPSaltdd(lindner, abcix, lifepres, faclev=1)
    abcdf
    plot(abcdf)

[Package USPS version 1.2-0 Index]