g02mcc calculates additional parameter estimates following Least Angle Regression (LARS), forward stagewise linear regression or Least Absolute Shrinkage and Selection Operator (LASSO) as performed by g02mac and g02mbc.

2 Specification

#include <nag.h>

void	g02mcc (Integer nstep, Integer ip, const double b[], Integer pdb, const double fitsum[], Nag_LARSTargetType ktype, const double nk[], Integer lnk, double nb[], Integer pdnb, NagError *fail)

The function may be called by the names: g02mcc, nag_correg_lars_param or nag_lars_param.

3 Description

g02mac and g02mbc fit either a LARS, forward stagewise linear regression, LASSO or positive LASSO model to a vector of

n

observed values,

y = {y_{i} : i = 1, 2, \dots, n}

and an

n \times p

design matrix

X

, where the

j

th column of

X

is given by the

j

th independent variable

x_{j}

. The models are fit using the LARS algorithm of Efron et al. (2004).

Figure 1

The full solution path for all four of these models follow a similar pattern where the parameter estimate for a given variable is piecewise linear. One such path, for a LARS model with six variables

(p = 6)

can be seen in Figure 1. Both g02mac and g02mbc return the vector of

p

parameter estimates,

β_{k}

, at

K

points along this path (so

k = 1, 2, \dots, K

). Each point corresponds to a step of the LARS algorithm. The number of steps taken depends on the model being fitted. In the case of a LARS model,

K = p

and each step corresponds to a new variable being included in the model. In the case of the LASSO models, each step corresponds to either a new variable being included in the model or an existing variable being removed from the model; the value of

K

is, therefore, no longer bound by the number of parameters. For forward stagewise linear regression, each step no longer corresponds to the addition or removal of a variable;, therefore, the number of possible steps is often markedly greater than for a corresponding LASSO model.

g02mcc uses the piecewise linear nature of the solution path to predict the parameter estimates,

\tilde{β}

, at a different point on this path. The location of the solution can either be defined in terms of a (fractional) step number or a function of the

L_{1}

norm of the parameter estimates.

4 References

Efron B, Hastie T, Johnstone I and Tibshirani R (2004) Least Angle Regression The Annals of Statistics (Volume 32) 2 407–499

Hastie T, Tibshirani R and Friedman J (2001) The Elements of Statistical Learning: Data Mining, Inference and Prediction Springer (New York)

Tibshirani R (1996) Regression Shrinkage and Selection via the Lasso Journal of the Royal Statistics Society, Series B (Methodological) (Volume 58) 1 267–288

Weisberg S (1985) Applied Linear Regression Wiley

5 Arguments

1: $nstep$ – Integer Input

On entry:

K

, the number of steps carried out in the model fitting process, as returned by g02mac and g02mbc.

Constraint:

nstep \geq 0

2: $ip$ – Integer Input

On entry:

p

, number of parameter estimates, as returned by g02mac and g02mbc.

Constraint:

ip \geq 1

3: $b [\dim]$ – const double Input

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

pdb \times (nstep + 1)

On entry:

β

the parameter estimates, as returned by g02mac and g02mbc, with

b [(k - 1) \times pdb + j - 1] = β_{k j}

, the parameter estimate for the

j

th variable, for

j = 1, 2, \dots, p

, at the

k

th step of the model fitting process.

Constraint: b should be unchanged since the last call to g02mac or g02mbc.

4: $pdb$ – Integer Input

On entry: the stride separating row elements in the two-dimensional data stored in the array b.

Constraint:

pdb \geq ip

5: $fitsum [6 \times (nstep + 1)]$ – const double Input

On entry: summaries of the model fitting process, as returned by g02mac and g02mbc.

Constraint: fitsum should be unchanged since the last call to g02mac or g02mbc..

6: $ktype$ – Nag_LARSTargetType Input

On entry: indicates what target values are held in nk.

$ktype = Nag_LARS_StepNumber$: nk holds (fractional) LARS step numbers.
$ktype = Nag_LARS_ScaledNorm$: nk holds values for $L_{1}$ norm of the (scaled) parameters.
$ktype = Nag_LARS_ProportionScaledNorm$: nk holds ratios with respect to the largest (scaled) $L_{1}$ norm.
$ktype = Nag_LARS_UnscaledNorm$: nk holds values for the $L_{1}$ norm of the (unscaled) parameters.
$ktype = Nag_LARS_ProportionUnscaledNorm$: nk holds ratios with respect to the largest (unscaled) $L_{1}$ norm.

If g02mac was called with

pred = Nag_LARS_None

Nag_LARS_Centered

or g02mbc was called with

pred = Nag_LARS_None

then the model fitting routine did not rescale the independent variables,

X

, prior to fitting the model and, therefore, there is no difference between

ktype = Nag_LARS_ScaledNorm

Nag_LARS_ProportionScaledNorm

and

ktype = Nag_LARS_UnscaledNorm

Nag_LARS_ProportionUnscaledNorm

Constraint:

ktype = Nag_LARS_StepNumber

Nag_LARS_ScaledNorm

Nag_LARS_ProportionScaledNorm

Nag_LARS_UnscaledNorm

Nag_LARS_ProportionUnscaledNorm

7: $nk [lnk]$ – const double Input

On entry: target values used for predicting the new set of parameter estimates.

Constraints:

if $ktype = Nag_LARS_StepNumber$ , $0 \leq nk [i - 1] \leq nstep$ , for $i = 1, 2, \dots, lnk$ ;
if $ktype = Nag_LARS_ScaledNorm$ , $0 \leq nk [i - 1] \leq fitsum [(nstep - 1) \times 6]$ , for $i = 1, 2, \dots, lnk$ ;
if $ktype = Nag_LARS_ProportionScaledNorm$ or $Nag_LARS_ProportionUnscaledNorm$ , $0 \leq nk [i - 1] \leq 1$ , for $i = 1, 2, \dots, lnk$ ;
if $ktype = Nag_LARS_UnscaledNorm$ , $0 \leq nk [i - 1] \leq {‖ β_{K} ‖}_{1}$ , for $i = 1, 2, \dots, lnk$ .

8: $lnk$ – Integer Input

On entry: number of values supplied in nk.

Constraint:

lnk \geq 1

9: $nb [\dim]$ – double Output

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

pdnb \times lnk

On exit:

\tilde{β}

the predicted parameter estimates, with

b [(i - 1) \times pdb + j - 1] = {\tilde{β}}_{i j}

, the parameter estimate for variable

j

j = 1, 2, \dots, p

at the point in the fitting process associated with

nk [i - 1]

i = 1, 2, \dots, lnk

10: $pdnb$ – Integer Input

On entry: the stride separating row elements in the two-dimensional data stored in the array nb.

Constraint:

pdnb \geq ip

11: $fail$ – NagError * Input/Output

The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_ARRAY_SIZE: On entry, $pdb = ⟨ value ⟩$ and $ip = ⟨ value ⟩$
Constraint: $pdb \geq ip$ .

On entry, $pdnb = ⟨ value ⟩$ and $ip = ⟨ value ⟩$ .
Constraint: $pdnb \geq ip$ .
NE_BAD_PARAM: On entry, argument $⟨ value ⟩$ had an illegal value.
NE_INT: On entry, $ip = ⟨ value ⟩$ .
Constraint: $ip \geq 1$ .

On entry, $lnk = ⟨ value ⟩$ .
Constraint: $lnk \geq 1$ .

On entry, $nstep = ⟨ value ⟩$ .
Constraint: $nstep \geq 0$ .
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.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_OUT_OF_RANGE: On entry, $ktype = Nag_LARS_ProportionScaledNorm$ or $Nag_LARS_ProportionUnscaledNorm$ , $nk [⟨ value ⟩] = ⟨ value ⟩$ .
Constraint: $0 \leq nk [i] \leq 1$ , for all $i$ .

On entry, $ktype = Nag_LARS_ScaledNorm$ , $nk [⟨ value ⟩] = ⟨ value ⟩$ , $nstep = ⟨ value ⟩$ and $fitsum [(nstep - 1) \times 6] = ⟨ value ⟩$ .
Constraint: $0 \leq nk [i] \leq fitsum [(nstep - 1) \times 6]$ , for all $i$ .

On entry, $ktype = Nag_LARS_StepNumber$ , $nk [⟨ value ⟩] = ⟨ value ⟩$ and $nstep = ⟨ value ⟩$
Constraint: $0 \leq nk [i] \leq nstep$ , for all $i$ .

On entry, $ktype = Nag_LARS_UnscaledNorm$ , $nk [⟨ value ⟩] = ⟨ value ⟩$ and ${‖ β_{K} ‖}_{1} = ⟨ value ⟩$
Constraint: $0 \leq nk [i] \leq {‖ β_{K} ‖}_{1}$ , for all $i$ .
NE_REAL_ARRAY: b has been corrupted since the last call to g02mac or g02mbc.

fitsum has been corrupted since the last call to g02mac or g02mbc.

7 Accuracy

Not applicable.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

g02mcc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

g02mcc 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 X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

None.

10 Example

This example performs a LARS on a set a simulated dataset with

20

observations and

6

independent variables.

Additional parameter estimates are obtained corresponding to a LARS step number of

0.2, 1.2, 3.2, 4.5

and

5.2

. Where, for example,

4.5

corresponds to the solution halfway between that obtained at step

4

and that obtained at step

5

g02mc: FL CL CPP AD PY MB

NAG CL Interfaceg02mcc (lars_​param)

▸▿ 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 CL Interface
g02mcc (lars_param)