Version: 0.0.1
Title: Variance-Covariance Matrices and Standard Errors
Author: Michael Chirico
Maintainer: Michael Chirico <MichaelChirico4@gmail.com>
Depends: R (≥ 3.4.0)
Description: Methods for faster extraction (about 5x faster in a few test cases) of variance-covariance matrices and standard errors from models. Methods in the 'stats' package tend to rely on the summary method, which may waste time computing other summary statistics which are summarily ignored.
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL: https://github.com/MichaelChirico/vcov
NeedsCompilation: no
Packaged: 2017-07-21 16:00:28 UTC; michael
Repository: CRAN
Date/Publication: 2017-07-22 16:09:54 UTC

Variance-Covariance Matrices and Standard Errors

Description

This package is designed to produce variance-covariance matrices and standard errors as directly/efficiently as possible from fit models. Default methods (e.g., in stats) tend to first compute the summary object for a model, from which the matrix is extracted. The catch is that the summary itself often involves several other extraneous computations. The summary methods are typically fast for most purposes, but falter in this regard when a user wishes to compute standard errors of perhaps thousands of models (as may happen, for example, when bootstrapping).

Fast Covariance Matrix and Standard Error Computation

Description

Skip wasted object summary steps computed by base R when computing covariance matrices and standard errors of common model objects.

Usage

  Vcov(object, ...)

  ## S3 method for class 'lm'
Vcov(object, ...)

  ## S3 method for class 'glm'
Vcov(object, dispersion = NULL, ...)
  
  se(object, ...)

Arguments

object

A fitted model object.

...

Additional arguments for method functions. For the glm method this can be used to pass a dispersion parameter.

dispersion

The dispersion parameter for the family used. Either a single numerical value or NULL (the default), in which case it is inferred from obj. For details, see summary.glm.

See Also

summary.glm

Examples

  # data taken from ?lm
  ctl = c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
  trt = c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
  group = gl(2, 10, 20, labels = c("Ctl","Trt"))
  weight = c(ctl, trt)
  reg_lm = lm(weight ~ group)
  Vcov(reg_lm)
  se(reg_lm)

  # data taken from ?glm
  counts = c(18,17,15,20,10,20,25,13,12)
  outcome = gl(3,1,9)
  treatment = gl(3,3)
  reg_glm = glm(counts ~ outcome + treatment, family = poisson)
  Vcov(reg_glm)
  se(reg_glm)

Runs a set of tests.

Description

Runs a set of tests to check vcov is working correctly.

Usage

  test.vcov(...)

Arguments

...

Currently no arguments.

Details

Runs a series of tests.

Examples

  test.vcov()