NUsetup: Specify KEY parameters used in NU.Learning to "design"...

In NU.Learning: Nonparametric and Unsupervised Learning from Cross-Sectional Observational Data

Specify KEY parameters used in NU.Learning to "design" analyses of Observational Data.

Description

Invoke NUsetup() to specify the name of the Hierarchical Clustering object output by NUcluster() and the name of the data.frame containing all desired X-covariates, the Treatment/Exposure variable and the Y-Outcome variable. It is ESSENTIAL to save the Environment output by NUsetup() as a named object within the user's .GlobalEnv space.

Usage

NUsetup(hclobj, dframe, trex, yvar)

Arguments

hclobj	Name of a NUcluster() output object created using a cluster::diana or stats::hclust method.
dframe	Name of the data.frame containing all X-covariates, the Treatment/Exposure variable and the Y-Outcome variable.
trex	Name of the numerical Treatment/Exposure variable.
yvar	Name of the numerical Y-Outcome variable.

Value

The environment output by NUsetup() must be saved to the user's .GlobalEnv space. It's contents will be automatically updated by calls to other NU.Learning functions:

aggdf	data.frame with 4 columns and 1 row for each call to ltdagg() or lrcagg().
aggdf$Label	Factor value of "LTD" or "LRC".
aggdf$Blocks	K = integer Number of Clusters requested.
aggdf$LTDmean or aggdf$LRCmean	numerical value of cluster mean of LTD or LRC estimates.
aggdf$LTDstde or aggdf$LRCstde	numerical value of the within-cluster standard deviation.
boxdf	data.frame of 2 variables ...for input to boxplot() by NUcompare().
boxdf$NUstat	LTD or LRC estimate for a single experimental unit from ltdagg() or lrcagg().
boxdf$K	Number of Cluters used in forming the LTD or LRC estimate for each Experimental Unit.
Kmax	Maximum Number of Clusters so that Average Size will be >= 12 experimental units.
LTDmax or LRCmax	Maximum Treatment Effect-Size estimate across Clusters.
LTDmin or LRCmin	Minimum Treatment Effect-Size estimate across Clusters.
NumLevels	Integer number of distinct Levels of the Treatment/Exposure variable: trex.
pars	Character data.frame with 4 columns and 1 row.
pars[1,1]	Name of the diana or hclust object created by NUcluster().
pars[1,2]	Name of data.frame containing the X, Treatment/Exposure and Y variables.
pars[1,3]	Name of Treatment/Exposure variable within data.frame pars[1,2].
pars[1,4]	Name of Y-outcome variable within data.frame pars[1,2].

Author(s)

Bob Obenchain <[e-mail]>

References

Obenchain RL. (2010) Local Control Approach using JMP. Chapter 7 of Analysis of Observational Health Care Data using SAS, Cary, NC:SAS Press, pages 151-192.

Obenchain RL. (2023) NU.Learning_in_R.pdf http://localcontrolstatistics.org

See Also

ltdagg, ivadj and lrcagg.

Examples

  
  # Running takes about 7 seconds...
  data(pci15k)
  xvars  = c("stent", "height", "female", "diabetic", "acutemi", "ejfract", "ves1proc")
  hclobj = NUcluster(pci15k, xvars)
  NUe    = NUsetup(hclobj, pci15k, thin, surv6mo)
  ls.str(NUe)
  

NU.Learning documentation built on Oct. 1, 2023, 1:06 a.m.