UPSnnltd {USPS} | R Documentation |
For a given number of patient clusters in baseline X-covariate space, UPSnnltd() characterizes the distribution of Nearest Neighbor "Local Treatemnt Differences" (LTDs) on a specified Y-outcome variable.
nnobj <- UPSnnltd(numclust)
numclust |
Number of clusters in baseline X-covariate space. |
Multiple calls to UPSnnltd(n) for varying numbers of clusters, n, are typically made after first invoking UPShclus() to hierarchically cluster patients in X-space and then invoking UPSaccum() to specify a Y outcome variable and a two-level treatment factor t. UPSnnltd(n) then determines the LTD Distribution corresponding to n clusters and, optionally, displays this distribution in a "Snowball" plot.
An output list object of class UPSnnltd:
hiclus |
Name of clustering object created by UPShclus(). |
dframe |
Name of data.frame containing X, t & Y variables. |
trtm |
Name of treatment factor variable. |
yvar |
Name of outcome Y variable. |
numclust |
Number of clusters requested. |
actclust |
Number of clusters actually produced. |
scedas |
Scedasticity assumption: "homo" or "hete" |
PStdif |
Character string describing the treatment difference. |
nnhbindf |
Vector containing cluster number for each patient. |
rawmean |
Unadjusted outcome mean by treatment group. |
rawvars |
Unadjusted outcome variance by treatment group. |
rawfreq |
Number of patients by treatment group. |
ratdif |
Unadjusted mean outcome difference between treatments. |
ratsde |
Standard error of unadjusted mean treatment difference. |
binmean |
Unadjusted mean outcome by cluster and treatment. |
binvars |
Unadjusted variance by cluster and treatment. |
binfreq |
Number of patients by bin and treatment. |
awbdif |
Across cluster average difference with cluster size weights. |
awbsde |
Standard error of awbdif. |
wwbdif |
Across cluster average difference, inverse variance weights. |
wwbsde |
Standard error of wwbdif. |
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. |
youtype |
"contin"uous => only next eight outputs; "factor" => only last three outputs. |
aovdiff |
ANOVA summary for treatment main effect only. |
form2 |
Formula for outcome differences due to bins and to treatment nested within bins. |
bindiff |
ANOVA summary for treatment nested within cluster. |
sig2 |
Estimate of error mean square in nested model. |
pbindif |
Unadjusted treatment difference by cluster. |
pbinsde |
Standard error of the unadjusted difference by cluster. |
pbinsiz |
Cluster radii measure: square root of total number of patients. |
symsiz |
Symbol size of largest possible Snowball in a UPSnnltd() plot with 1 cluster. |
factab |
Marginal table of counts by Y-factor level and treatment. |
cumchi |
Cumulative Chi-Square statistic for interaction in the three-way, nested table. |
cumdf |
Degrees of-Freedom for the Cumulative Chi-Squared. |
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 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.
UPSivadj
, UPSaccum
and UPSgraph
.
data(lindner) UPSxvars <- c("stent", "height", "female", "diabetic", "acutemi", "ejecfrac", "ves1proc") UPSharch <- UPShclus(lindner, UPSxvars) UPSaccum(UPSharch, lindner, abcix, lifepres, faclev=1, scedas="homo", accobj="ABClife") lif070nn <- UPSnnltd(70) lif070nn plot(lif070nn)