NAG Library Function Document

nag_regsn_ridge_opt (g02kac)

nag_regsn_ridge_opt (Nag_OrderType order, Integer n, Integer m, const double x[], Integer pdx, const Integer isx[], Integer ip, double tau, const double y[], double *h, Nag_PredictError opt, Integer *niter, double tol, double *nep, Nag_EstimatesOption orig, double b[], double vif[], double res[], double *rss, Integer *df, Nag_OptionLOO optloo, double perr[], NagError *fail)

3 Description

A linear model has the form:

y = c + X β + ε,

where

$y$ is an $n$ by $1$ matrix of values of a dependent variable;
$c$ is a scalar intercept term;
$X$ is an $n$ by $m$ matrix of values of independent variables;
$β$ is an $m$ by $1$ matrix of unknown values of parameters;
$ε$ is an $n$ by $1$ matrix of unknown random errors such that variance of $ε = σ^{2} I$ .

Let

\tilde{X}

be the mean-centred

X

and

\tilde{y}

the mean-centred

y

. Furthermore,

\tilde{X}

is scaled such that the diagonal elements of the cross product matrix

{\tilde{X}}^{T} \tilde{X}

are one. The linear model now takes the form:

\tilde{y} = \tilde{X} \tilde{β} + ε .

Ridge regression estimates the parameters

\tilde{β}

in a penalised least squares sense by finding the

\tilde{b}

that minimizes

{‖\tilde{X} \tilde{b} - \tilde{y}‖}^{2} + h {‖\tilde{b}‖}^{2}, h > 0,

where

‖\cdot‖

denotes the

ℓ_{2}

-norm and

h

is a scalar regularization or ridge parameter. For a given value of

h

, the parameter estimates

\tilde{b}

are found by evaluating

\tilde{b} = {({\tilde{X}}^{T} \tilde{X} + h I)}^{- 1} {\tilde{X}}^{T} \tilde{y} .

Note that if

h = 0

the ridge regression solution is equivalent to the ordinary least squares solution.

Rather than calculate the inverse of (

{\tilde{X}}^{T} \tilde{X} + h I

) directly, nag_regsn_ridge_opt (g02kac) uses the singular value decomposition (SVD) of

\tilde{X}

. After decomposing

\tilde{X}

into

U D V^{T}

where

U

and

V

are orthogonal matrices and

D

is a diagonal matrix, the parameter estimates become

\tilde{b} = V {(D^{T} D + h I)}^{- 1} D U^{T} \tilde{y} .

A consequence of introducing the ridge parameter is that the effective number of parameters,

γ

, in the model is given by the sum of diagonal elements of

D^{T} D {(D^{T} D + h I)}^{- 1},

see Moody (1992) for details.

Any multi-collinearity in the design matrix

X

may be highlighted by calculating the variance inflation factors for the fitted model. The

j

th variance inflation factor,

v_{j}

, is a scaled version of the multiple correlation coefficient between independent variable

j

and the other independent variables,

R_{j}

, and is given by

v_{j} = \frac{1}{1 - R_{j}}, j = 1, 2, \dots, m .

The

m

variance inflation factors are calculated as the diagonal elements of the matrix:

{({\tilde{X}}^{T} \tilde{X} + h I)}^{- 1} {\tilde{X}}^{T} \tilde{X} {({\tilde{X}}^{T} \tilde{X} + h I)}^{- 1},

which, using the SVD of

\tilde{X}

, is equivalent to the diagonal elements of the matrix:

V {(D^{T} D + h I)}^{- 1} D^{T} D {(D^{T} D + h I)}^{- 1} V^{T} .

Although parameter estimates

\tilde{b}

are calculated by using

\tilde{X}

, it is usual to report the parameter estimates

b

associated with

X

. These are calculated from

\tilde{b}

, and the means and scalings of

X

. Optionally, either

\tilde{b}

b

may be calculated.

The method can adopt one of four criteria to minimize while calculating a suitable value for

h

(a)

Generalized cross-validation (GCV):

\frac{n s}{{(n - γ)}^{2}};

(b)

Unbiased estimate of variance (UEV):

\frac{s}{n - γ};

(c)

Future prediction error (FPE):

\frac{1}{n} (s + \frac{2 γ s}{n - γ});

(d)

Bayesian information criterion (BIC):

\frac{1}{n} (s + \frac{\log (n) γ s}{n - γ});

where

s

is the sum of squares of residuals. However, the function returns all four of the above prediction errors regardless of the one selected to minimize the ridge parameter,

h

. Furthermore, the function will optionally return the leave-one-out cross-validation (LOOCV) prediction error.

4 References

Hastie T, Tibshirani R and Friedman J (2003) The Elements of Statistical Learning: Data Mining, Inference and Prediction Springer Series in Statistics

Moody J.E. (1992) The effective number of parameters: An analysis of generalisation and regularisation in nonlinear learning systems In: Neural Information Processing Systems (eds J E Moody, S J Hanson, and R P Lippmann) 4 847–854 Morgan Kaufmann San Mateo CA

5 Arguments

1: order – Nag_OrderTypeInput

On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by

order = Nag_RowMajor

. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

Nag_ColMajor

2: n – IntegerInput

On entry:

n

, the number of observations.

Constraint:

n > 1

3: m – IntegerInput

On entry: the number of independent variables available in the data matrix

X

Constraint:

m \leq n

4: x[ $\dim$ ] – const doubleInput

Note: the dimension, dim, of the array x must be at least

$\max (1, pdx \times m)$ when $order = Nag_ColMajor$ ;
$\max (1, n \times pdx)$ when $order = Nag_RowMajor$ .

The

(i, j)

th element of the matrix

X

is stored in

$x [(j - 1) \times pdx + i - 1]$ when $order = Nag_ColMajor$ ;
$x [(i - 1) \times pdx + j - 1]$ when $order = Nag_RowMajor$ .

On entry: the values of independent variables in the data matrix

X

5: pdx – IntegerInput

On entry: the stride separating row or column elements (depending on the value of order) in the array x.

Constraints:

if $order = Nag_ColMajor$ , $pdx \geq n$ ;
if $order = Nag_RowMajor$ , $pdx \geq m$ .

6: isx[m] – const IntegerInput

On entry: indicates which

m

independent variables are included in the model.

$isx [j - 1] = 1$: The $j$ th variable in x will be included in the model.
$isx [j - 1] = 0$: Variable $j$ is excluded.

Constraint:

isx [j - 1] = 0 ​ or ​ 1

, for

j = 1, 2, \dots, m

7: ip – IntegerInput

On entry:

m

, the number of independent variables in the model.

Constraints:

$1 \leq ip \leq m$ ;
Exactly ip elements of isx must be equal to $1$ .

8: tau – doubleInput

On entry: singular values less than tau of the SVD of the data matrix

X

will be set equal to zero.

Suggested value:

tau = 0.0

Constraint:

tau \geq 0.0

9: y[n] – const doubleInput

On entry: the

n

values of the dependent variable

y

10: h – double *Input/Output

On entry: an initial value for the ridge regression parameter

h

; used as a starting point for the optimization.

Constraint:

h > 0.0

On exit: h is the optimized value of the ridge regression parameter

h

11: opt – Nag_PredictErrorInput

On entry: the measure of prediction error used to optimize the ridge regression parameter

h

. The value of opt must be set equal to one of:

$opt = Nag_GCV$: Generalized cross-validation (GCV);
$opt = Nag_UEV$: Unbiased estimate of variance (UEV)
$opt = Nag_FPE$: Future prediction error (FPE)
$opt = Nag_BIC$: Bayesian information criteron (BIC).

Constraint:

opt = Nag_GCV

Nag_UEV

Nag_FPE

Nag_BIC

12: niter – Integer *Input/Output

On entry: the maximum number of iterations allowed to optimize the ridge regression parameter

h

Constraint:

niter \geq 1

On exit: the number of iterations used to optimize the ridge regression parameter

h

within tol.

13: tol – doubleInput

On entry: iterations of the ridge regression parameter

h

will halt when consecutive values of

h

lie within tol.

Constraint:

tol > 0.0

14: nep – double *Output

On exit: the number of effective parameters,

γ

, in the model.

15: orig – Nag_EstimatesOptionInput

On entry: if

orig = Nag_EstimatesOrig

, the parameter estimates

b

are calculated for the original data; otherwise

orig = Nag_EstimatesStand

and the parameter estimates

\tilde{b}

are calculated for the standardized data.

Constraint:

orig = Nag_EstimatesOrig

Nag_EstimatesStand

16: b[ $ip + 1$ ] – doubleOutput

On exit: contains the intercept and parameter estimates for the fitted ridge regression model in the order indicated by isx. The first element of b contains the estimate for the intercept;

b [j]

contains the parameter estimate for the

j

th independent variable in the model, for

j = 1, 2, \dots, ip

17: vif[ip] – doubleOutput

On exit: the variance inflation factors in the order indicated by isx. For the

j

th independent variable in the model,

vif [j - 1]

is the value of

v_{j}

, for

j = 1, 2, \dots, ip

18: res[n] – doubleOutput

On exit:

res [i - 1]

is the value of the

i

th residual for the fitted ridge regression model, for

i = 1, 2, \dots, n

19: rss – double *Output

On exit: the sum of squares of residual values.

20: df – Integer *Output

On exit: the degrees of freedom for the residual sum of squares rss.

21: optloo – Nag_OptionLOOInput

On entry: if

optloo = Nag_WantLOO

, the leave-one-out cross-validation estimate of prediction error is calculated; otherwise no such estimate is calculated and

optloo = Nag_NoLOO

Constraint:

optloo = Nag_NoLOO

Nag_WantLOO

22: perr[ $5$ ] – doubleOutput

On exit: the first four elements contain, in this order, the measures of prediction error: GCV, UEV, FPE and BIC.

optloo = Nag_WantLOO

perr [4]

is the LOOCV estimate of prediction error; otherwise

perr [4]

is not referenced.

23: fail – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_2_INT_ARG_CONS: On entry, $ip = ⟨value⟩$ ; $m = ⟨value⟩$ .
Constraint: $1 \leq ip \leq m$ .
NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument $⟨value⟩$ had an illegal value.
NE_INT: On entry, $n = ⟨value⟩$ .
Constraint: $n > 1$ .
On entry, $niter = ⟨value⟩$ .
Constraint: $niter \geq 1$ .
On entry, $pdx = ⟨value⟩$ .
Constraint: $pdx > 0$ .
NE_INT_2: On entry, $m = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: $m \leq n$ .
On entry, $pdx = ⟨value⟩$ ; $n = ⟨value⟩$ .
Constraint: $pdx \geq n$ .
On entry, $pdx = ⟨value⟩$ and $m = ⟨value⟩$ .
Constraint: $pdx \geq m$ .
NE_INT_ARG_CONS: On entry, $ip = ⟨value⟩$ .
Constraint: $sum (isx) = ip$ .
NE_INT_ARRAY_VAL_1_OR_2: On entry, $isx [⟨value⟩] = ⟨value⟩$ .
Constraint: $isx [j - 1] = 0$ or $1$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
NE_REAL: On entry, $h = ⟨value⟩$ .
Constraint: $h > 0.0$ .
On entry, $tau = ⟨value⟩$ .
Constraint: $tau \geq 0.0$ .
On entry, $tol = ⟨value⟩$ .
Constraint: $tol > 0.0$ .
NE_SVD_FAIL: SVD failed to converge.
NW_TOO_MANY_ITER: Maximum number of iterations used.

7 Accuracy

Not applicable.

8 Parallelism and Performance

nag_regsn_ridge_opt (g02kac) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

nag_regsn_ridge_opt (g02kac) makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

Please consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

nag_regsn_ridge_opt (g02kac) allocates internally

\max (5 \times (n - 1), 2 \times ip \times ip) + (n + 3) \times ip + n

elements of double precision storage.

10 Example

This example reads in data from an experiment to model body fat, and a ridge regression is calculated that optimizes GCV prediction error.

NAG Library Function Documentnag_regsn_ridge_opt (g02kac)

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG Library Function Document

nag_regsn_ridge_opt (g02kac)