UPSaltdd {USPS} | R Documentation |
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.
UPSaltdd(dframe, trtm, yvar, faclev=3, scedas="homo", NNobj=NA, clus=50, reps=10, seed=12345)
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. |
Multiple calls to UPSaltdd() for different UPSnnltd objects or different numbers of clusters are typically made after first invoking UPSgraph().
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. |
Bob Obenchain <wizbob@att.net>
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.
UPSnnltd
, UPSaccum
and UPSgraph
.
data(lindner) abcdf <- UPSaltdd(lindner, abcix, lifepres, faclev=1) abcdf plot(abcdf)