mlme | R Documentation |
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.
mlme(envir, hcl, LCagg, xvec )
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". |
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").
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. |
Bob Obenchain <wizbob@att.net>
Obenchain RL. LocalControlStrategy-vignette. (2019) LCstrategy_in_R.pdf http://localcontrolstatistics.org
plot.mlme
,print.mlme
,mlme.stats
## Not run: # 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... ## End(Not run)
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