UPSaltdd {LocalControl} | 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(
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
- 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.
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. (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
.