NAG Library Routine Document

G02BYF

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Parameters

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

1 Purpose

G02BYF computes a partial correlation/variance-covariance matrix from a correlation or variance-covariance matrix computed by G02BXF.

2 Specification

SUBROUTINE G02BYF (

M, NY, NX, ISZ, R, LDR, P, LDP, WK, IFAIL)

INTEGER	M, NY, NX, ISZ(M), LDR, LDP, IFAIL
REAL (KIND=nag_wp)	R(LDR,M), P(LDP,NY), WK(NYNX+NX(NX+1)/2)

3 Description

Partial correlation can be used to explore the association between pairs of random variables in the presence of other variables. For three variables,

y_{1}

y_{2}

and

x_{3}

, the partial correlation coefficient between

y_{1}

and

y_{2}

given

x_{3}

is computed as:

\frac{r_{12} - r_{13} r_{23}}{\sqrt{(1 - r_{13}^{2}) (1 - r_{23}^{2})}},

where

r_{i j}

is the product-moment correlation coefficient between variables with subscripts

i

and

j

. The partial correlation coefficient is a measure of the linear association between

y_{1}

and

y_{2}

having eliminated the effect due to both

y_{1}

and

y_{2}

being linearly associated with

x_{3}

. That is, it is a measure of association between

y_{1}

and

y_{2}

conditional upon fixed values of

x_{3}

. Like the full correlation coefficients the partial correlation coefficient takes a value in the range (

- 1, 1

) with the value

0

indicating no association.

In general, let a set of variables be partitioned into two groups

Y

and

X

with

n_{y}

variables in

Y

and

n_{x}

variables in

X

and let the variance-covariance matrix of all

n_{y} + n_{x}

variables be partitioned into,

[\begin{array}{l} Σ_{x x} & Σ_{x y} \\ Σ_{y x} & Σ_{y y} \end{array}] .

The variance-covariance of

Y

conditional on fixed values of the

X

variables is given by:

Σ_{y ∣ x} = Σ_{y y} - Σ_{y x} Σ_{x x}^{- 1} Σ_{x y} .

The partial correlation matrix is then computed by standardizing

Σ_{y ∣ x}

diag {(Σ_{y ∣ x})}^{- \frac{1}{2}} Σ_{y ∣ x} diag {(Σ_{y ∣ x})}^{- \frac{1}{2}} .

To test the hypothesis that a partial correlation is zero under the assumption that the data has an approximately Normal distribution a test similar to the test for the full correlation coefficient can be used. If

r

is the computed partial correlation coefficient then the appropriate

t

statistic is

r \sqrt{\frac{n - n_{x} - 2}{1 - r^{2}}},

which has approximately a Student's

t

-distribution with

n - n_{x} - 2

degrees of freedom, where

n

is the number of observations from which the full correlation coefficients were computed.

4 References

Krzanowski W J (1990) Principles of Multivariate Analysis Oxford University Press

Morrison D F (1967) Multivariate Statistical Methods McGraw–Hill

Osborn J F (1979) Statistical Exercises in Medical Research Blackwell

Snedecor G W and Cochran W G (1967) Statistical Methods Iowa State University Press

5 Parameters

1: $M$ – INTEGERInput

On entry: the number of variables in the variance-covariance/correlation matrix given in R.

Constraint:

M \geq 3

2: $NY$ – INTEGERInput

On entry: the number of

Y

variables,

n_{y}

, for which partial correlation coefficients are to be computed.

Constraint:

NY \geq 2

3: $NX$ – INTEGERInput

On entry: the number of

X

variables,

n_{x}

, which are to be considered as fixed.

Constraints:

$NX \geq 1$ ;
$NY + NX \leq M$ .

4: $ISZ (M)$ – INTEGER arrayInput

On entry: indicates which variables belong to set

X

and

Y

$ISZ (i) < 0$: The $i$ th variable is a $Y$ variable, for $i = 1, 2, \dots, M$ .
$ISZ (i) > 0$: The $i$ th variable is a $X$ variable.
$ISZ (i) = 0$: The $i$ th variable is not included in the computations.

Constraints:

exactly NY elements of ISZ must be $< 0$ ;
exactly NX elements of ISZ must be $> 0$ .

5: $R (LDR, M)$ – REAL (KIND=nag_wp) arrayInput

On entry: the variance-covariance or correlation matrix for the M variables as given by G02BXF. Only the upper triangle need be given.

Note: the matrix must be a full rank variance-covariance or correlation matrix and so be positive definite. This condition is not directly checked by the routine.

6: $LDR$ – INTEGERInput

On entry: the first dimension of the array R as declared in the (sub)program from which G02BYF is called.

Constraint:

LDR \geq M

7: $P (LDP, NY)$ – REAL (KIND=nag_wp) arrayOutput

On exit: the strict upper triangle of P contains the strict upper triangular part of the

n_{y}

n_{y}

partial correlation matrix. The lower triangle contains the lower triangle of the

n_{y}

n_{y}

partial variance-covariance matrix if the matrix given in R is a variance-covariance matrix. If the matrix given in R is a partial correlation matrix then the variance-covariance matrix is for standardized variables.

8: $LDP$ – INTEGERInput

On entry: the first dimension of the array P as declared in the (sub)program from which G02BYF is called.

Constraint:

LDP \geq NY

9: $WK (NY \times NX + NX \times (NX + 1) / 2)$ – REAL (KIND=nag_wp) arrayWorkspace

10: $IFAIL$ – INTEGERInput/Output

On entry: IFAIL must be set to

0

- 1 ​ or ​ 1

. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.

For environments where it might be inappropriate to halt program execution when an error is detected, the value

- 1 ​ or ​ 1

is recommended. If the output of error messages is undesirable, then the value

1

is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is

0

. When the value $- 1 or 1$ is used it is essential to test the value of IFAIL on exit.

On exit:

IFAIL = 0

unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

If on entry

IFAIL = 0

- 1

, explanatory error messages are output on the current error message unit (as defined by X04AAF).

Errors or warnings detected by the routine:

$IFAIL = 1$

On entry,	$M < 3$ ,
or	$NY < 2$ ,
or	$NX < 1$ ,
or	$NY + NX > M$ ,
or	$LDR < M$ ,
or	$LDP < NY$ .

$IFAIL = 2$

On entry,	there are not exactly NY elements of $ISZ < 0$ ,
or	there are not exactly NX elements of $ISZ > 0$ .

$IFAIL = 3$: On entry, the variance-covariance/correlation matrix of the $X$ variables, $Σ_{x x}$ , is not of full rank. Try removing some of the $X$ variables by setting the appropriate element of $ISZ = 0$ .

$IFAIL = 4$: Either a diagonal element of the partial variance-covariance matrix, $Σ_{y ∣ x}$ , is zero and/or a computed partial correlation coefficient is greater than one. Both indicate that the matrix input in R was not positive definite.

$IFAIL = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.8 in the Essential Introduction for further information.

$IFAIL = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 3.7 in the Essential Introduction for further information.

$IFAIL = - 999$: Dynamic memory allocation failed.
See Section 3.6 in the Essential Introduction for further information.

7 Accuracy

G02BYF computes the partial variance-covariance matrix,

Σ_{y ∣ x}

, by computing the Cholesky factorization of

Σ_{x x}

. If

Σ_{x x}

is not of full rank the computation will fail. For a statement on the accuracy of the Cholesky factorization see F07GDF (DPPTRF).

8 Parallelism and Performance

G02BYF is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

G02BYF 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 routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

Models that represent the linear associations given by partial correlations can be fitted using the multiple regression routine G02DAF.

10 Example

Data, given by Osborn (1979), on the number of deaths, smoke (

mg / m^{3}

) and sulphur dioxide (parts/million) during an intense period of fog is input. The correlations are computed using G02BXF and the partial correlation between deaths and smoke given sulphur dioxide is computed using G02BYF. Both correlation matrices are printed using the routine X04CAF.

NAG Library Routine DocumentG02BYF

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Parameters

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 Routine Document

G02BYF