LocalControlStrategy (version 1.4)

mlme: Create a <<Most-Like-Me>> data.frame for a specified X-Confounder vector: xvec

Description

For a Given X-confounder Vector (xvec), sort all experimental units (eUnits) in an ltdagg() or lrcagg() output object into the strictly non-decreasing order of their distances from this X-Vector, which defines the TARGET eUnit: "Me". Plots of mlme() objects and displays of mlme.stats() are then used to Visualize and Summarize "Mini-" << LOCAL effect-size Distributions >> for different Numbers of "Nearest Neighbor" eUnits.

Usage

mlme(envir, hcl, LCagg, xvec )

Value

An output list object of class mlme:

xvec

The xvec vector input to mlme().

Type

Either "LTD" or "LRC".

xvars

Names of the X-Confounder variables specified in LCsetup().

varx

The vector of Variances of the xvars variabes, used in rescaling distances.

outdf

The output data.frame of sorted "Nearest Neighbor" candidate eUnits.

Arguments

envir

Environment output by a call to the LCsetup() function.

hcl

Name of a LCcluster() output object created using a cluster::diana or stats::hclust method.

LCagg

A data.frame object output by ltdagg() or lrcagg() containing LOCAL effect-size Estimates for eUnits within Clusters defined in X-covariate space.

xvec

A suitable vector of the Numerical values for the X-Confounder variables, used in the current CLUSTERING, that define the eUnit: "Me".

Author

Bob Obenchain <[e-mail]>

Details

For example, in demo(radon), the eUnits are 2881 US "Counties", and the LCagg object is of type lrcagg() because radon exposure is a continuous variable. But, in demo(pci15k), the eUnits are 15487 "Patients," and the LCagg object is of type ltdagg() because treatment choice (thin) is Binary (0 = "No", 1 = "Yes").

References

Obenchain RL. LocalControlStrategy-vignette. (2019) LCstrategy_in_R.pdf http://localcontrolstatistics.org

See Also

plot.mlme,print.mlme,mlme.stats

Examples

  if (FALSE) {
  # Long running example...
  data(pci15k)
  xvars <- c("stent", "height", "female", "diabetic", "acutemi", "ejfract", "ves1proc")
  hclobj <- LCcluster(pci15k, xvars)
  LC.env <- LCsetup(hclobj, pci15k, thin, surv6mo)
  surv0500 <- ltdagg(500, LC.env)
  xvec11870 <- c( 0, 162, 1, 1, 0, 57, 1) 
  mlmeC5H <- mlme(envir = LC.env, hcl = hclobj, LCagg = surv0500, xvec = xvec11870 ) 	
  plot(mlmeC5H) # using default "NN" and "breaks" settings...
  }