Measures of Precision and Shrinkage for Ridge Regression

Calculates measures of precision based on the size of the estimated covariance matrices of the parameters and shrinkage of the parameters in a ridge regression model. function does. ~~

Three measures of (inverse) precision based on the “size” of the covariance matrix of the parameters are calculated. Let \(V_k\) be the covariance matrix for a given ridge constant, and let \(\lambda_i , i= 1, \dots p\) be its eigenvalues

\(\log | V_k | = \log \prod \lambda\) or \(|V_k|^{1/p} =(\prod \lambda)^{1/p}\) measures the linearized volume of the covariance ellipsoid and corresponds conceptually to Wilks' Lambda criterion
\( trace( V_k ) = \sum \lambda\) corresponds conceptually to Pillai's trace criterion
\( \lambda_1 = max (\lambda)\) corresponds to Roy's largest root criterion.

Usage

precision(object, det.fun, normalize, ...)

Arguments

object: An object of class ridge or lm
det.fun: Function to be applied to the determinants of the covariance matrices, one of c("log","root").
normalize: If TRUE the length of the coefficient vector is normalized to a maximum of 1.0.
...: Other arguments (currently unused)

Value

A data.frame with the following columns

lambda: The ridge constant
df: The equivalent effective degrees of freedom
det: The det.fun function of the determinant of the covariance matrix
trace: The trace of the covariance matrix
max.eig: Maximum eigen value of the covariance matrix
norm.beta: The root mean square of the estimated coefficients, possibly normalized

Note

Models fit by lm and ridge use a different scaling for the predictors, so the results of precision for an lm model will not correspond to those for ridge with ridge constant = 0.

Author

Michael Friendly

Examples


longley.y <- longley[, "Employed"]
longley.X <- data.matrix(longley[, c(2:6,1)])

lambda <- c(0, 0.005, 0.01, 0.02, 0.04, 0.08)
lridge <- ridge(longley.y, longley.X, lambda=lambda)
clr <- c("black", rainbow(length(lambda)-1, start=.6, end=.1))
coef(lridge)
#>              GNP Unemployed Armed.Forces  Population     Year GNP.deflator
#> 0.000 -3.4471925  -1.827886   -0.6962102 -0.34419721 8.431972   0.15737965
#> 0.005 -1.0424783  -1.491395   -0.6234680 -0.93558040 6.566532  -0.04175039
#> 0.010 -0.1797967  -1.361047   -0.5881396 -1.00316772 5.656287  -0.02612152
#> 0.020  0.4994945  -1.245137   -0.5476331 -0.86755299 4.626116   0.09766305
#> 0.040  0.9059471  -1.155229   -0.5039108 -0.52347060 3.576502   0.32123994
#> 0.080  1.0907048  -1.086421   -0.4582525 -0.08596324 2.641649   0.57025165

(pdat <- precision(lridge))
#>       lambda       df       det      trace    max.eig norm.beta
#> 0.000  0.000 6.000000 -12.92710 18.1189511 15.4191000 1.0000000
#> 0.005  0.005 5.415118 -14.41144  6.8209398  4.6064698 0.7406376
#> 0.010  0.010 5.135429 -15.41069  4.0422816  2.1806533 0.6365441
#> 0.020  0.020 4.818103 -16.82581  2.2180382  1.0254551 0.5282452
#> 0.040  0.040 4.477853 -18.69819  1.1647170  0.5807883 0.4232699
#> 0.080  0.080 4.127782 -21.05065  0.5873002  0.2599108 0.3372722
# plot log |Var(b)| vs. length(beta)
with(pdat, {
  plot(norm.beta, det, type="b", 
  cex.lab=1.25, pch=16, cex=1.5, col=clr, lwd=2,
  xlab='shrinkage: ||b|| / max(||b||)',
  ylab='variance: log |Var(b)|')
  text(norm.beta, det, lambda, cex=1.25, pos=c(rep(2,length(lambda)-1),4))
  text(min(norm.beta), max(det), "Variance vs. Shrinkage", cex=1.5, pos=4)
  })


# plot trace[Var(b)] vs. length(beta)
with(pdat, {
  plot(norm.beta, trace, type="b",
  cex.lab=1.25, pch=16, cex=1.5, col=clr, lwd=2,
  xlab='shrinkage: ||b|| / max(||b||)',
  ylab='variance: trace [Var(b)]')
  text(norm.beta, trace, lambda, cex=1.25, pos=c(2, rep(4,length(lambda)-1)))
#  text(min(norm.beta), max(det), "Variance vs. Shrinkage", cex=1.5, pos=4)
  })