summary.mlr {MatchLinReg}

Applying diagnostic and calibration functions to mlr objects

Description

Applying a series of diagnostic and calibration functions to a series of matched data sets to determine impact of matching on TE bias, variance and total error, and to select the best matching parameters.

Usage

## S3 method for class 'mlr'
summary(object, power = FALSE
  , power.control = list(rnd = TRUE, d = 0.5, sig.level = 0.05
    , niter = 1000, rnd = TRUE)
  , max.method = c("single-covariate", "covariate-subspace"
    , "absolute")
  , verbose = FALSE, ...
  , orsq.min = 1e-03, orsq.max = 1e0, n.orsq = 100)

Arguments

object

An object of class mlr, typically the result of a call to mlr.

power

Boolean flag indicating whether Monte-Carlo based power analysis must be performed or not.

power.control

A list containing parameters to be passed to mlr.power for power calculation.

max.method

Which constrained bias estimation method must be used in bias-variance trade-off and other analyses?

verbose

Whether progress message must be printed.

...

Parameters to be passed to/from other functions.

orsq.min

Minimum value of omitted R-squared used for combining normalized bias and variance.

orsq.max

Maximum value of omitted R-squared used for combining normalized bias and variance.

n.orsq

Number of values for omitted R-squared to generate in the specified range.

Value

An object of class summary.mlr, with the following elements:

mlr.obj

Same as input.

bias

Matrix of aggregate bias values, one row per calibration index, and three columns: 1) single-covariate maximum, 2) covariate-subspace maximum, and 3) absolute maximum, in that order.

bias.terms

Matrix of biases, one row per calibration index, and one column per candidate omitted term.

variance

Vector of normalized variances, one per each value of calibration index.

power

Matrix of power calculations, one row per calibration index. Each row is identical to output of mlr.power for that calibration index value.

smd

Matrix of standardized mean differences, one row per calibration index, and one column for each included or omitted covariates.

combine.obj

Output of mlr.combine.bias.variance applied to bias and variances at each calibration index value.

Author(s)

Alireza S. Mahani, Mansour T.A. Sharabiani

References

Link to a draft paper, documenting the supporting mathematical framework, will be provided in the next release.