NAG Library Function Document
nag_outlier_peirce (g07gac)
1
Purpose
nag_outlier_peirce (g07gac) identifies outlying values using Peirce's criterion.
2
Specification
#include <nag.h> 
#include <nagg07.h> 
void 
nag_outlier_peirce (Integer n,
Integer p,
const double y[],
double mean,
double var,
Integer iout[],
Integer *niout,
Integer ldiff,
double diff[],
double llamb[],
NagError *fail) 

3
Description
nag_outlier_peirce (g07gac) flags outlying values in data using Peirce's criterion. Let
 $y$ denote a vector of $n$ observations (for example the residuals) obtained from a model with $p$ parameters,
 $m$ denote the number of potential outlying values,
 $\mu $ and ${\sigma}^{2}$ denote the mean and variance of $y$ respectively,
 $\stackrel{~}{y}$ denote a vector of length $nm$ constructed by dropping the $m$ values from
$y$ with the largest value of $\left{y}_{i}\mu \right$,
 ${\stackrel{~}{\sigma}}^{2}$ denote the (unknown) variance of $\stackrel{~}{y}$,

$\lambda $ denote the ratio of $\stackrel{~}{\sigma}$ and $\sigma $ with
$\lambda =\frac{\stackrel{~}{\sigma}}{\sigma}$.
Peirce's method flags
${y}_{i}$ as a potential outlier if
$\left{y}_{i}\mu \right\ge x$, where
$x={\sigma}^{2}z$ and
$z$ is obtained from the solution of
where
and
$\Phi $ is the cumulative distribution function for the standard Normal distribution.
As
${\stackrel{~}{\sigma}}^{2}$ is unknown an assumption is made that the relationship between
${\stackrel{~}{\sigma}}^{2}$ and
${\sigma}^{2}$, hence
$\lambda $, depends only on the sum of squares of the rejected observations and the ratio estimated as
which gives
A value for the cutoff
$x$ is calculated iteratively. An initial value of
$R=0.2$ is used and a value of
$\lambda $ is estimated using equation
(1). Equation
(3) is then used to obtain an estimate of
$z$ and then equation
(2) is used to get a new estimate for
$R$. This process is then repeated until the relative change in
$z$ between consecutive iterations is
$\text{}\le \sqrt{\epsilon}$, where
$\epsilon $ is
machine precision.
By construction, the cutoff for testing for $m+1$ potential outliers is less than the cutoff for testing for $m$ potential outliers. Therefore Peirce's criterion is used in sequence with the existence of a single potential outlier being investigated first. If one is found, the existence of two potential outliers is investigated etc.
If one of a duplicate series of observations is flagged as an outlier, then all of them are flagged as outliers.
4
References
Gould B A (1855) On Peirce's criterion for the rejection of doubtful observations, with tables for facilitating its application The Astronomical Journal 45
Peirce B (1852) Criterion for the rejection of doubtful observations The Astronomical Journal 45
5
Arguments
 1:
$\mathbf{n}$ – IntegerInput

On entry: $n$, the number of observations.
Constraint:
${\mathbf{n}}\ge 3$.
 2:
$\mathbf{p}$ – IntegerInput

On entry: $p$, the number of parameters in the model used in obtaining the $y$. If $y$ is an observed set of values, as opposed to the residuals from fitting a model with $p$ parameters, then $p$ should be set to $1$, i.e., as if a model just containing the mean had been used.
Constraint:
$1\le {\mathbf{p}}\le {\mathbf{n}}2$.
 3:
$\mathbf{y}\left[{\mathbf{n}}\right]$ – const doubleInput

On entry: $y$, the data being tested.
 4:
$\mathbf{mean}$ – doubleInput

On entry: if
${\mathbf{var}}>0.0$,
mean must contain
$\mu $, the mean of
$y$, otherwise
mean is not referenced and the mean is calculated from the data supplied in
y.
 5:
$\mathbf{var}$ – doubleInput

On entry: if
${\mathbf{var}}>0.0$,
var must contain
${\sigma}^{2}$, the variance of
$y$, otherwise the variance is calculated from the data supplied in
y.
 6:
$\mathbf{iout}\left[{\mathbf{n}}\right]$ – IntegerOutput

On exit: the indices of the values in
y sorted in descending order of the absolute difference from the mean, therefore
$\left{\mathbf{y}}\left[{\mathbf{iout}}\left[\mathit{i}2\right]1\right]\mu \right\ge \left{\mathbf{y}}\left[{\mathbf{iout}}\left[\mathit{i}1\right]1\right]\mu \right$, for
$\mathit{i}=2,3,\dots ,{\mathbf{n}}$.
 7:
$\mathbf{niout}$ – Integer *Output

On exit: the number of potential outliers. The indices for these potential outliers are held in the first
niout elements of
iout. By construction there can be at most
${\mathbf{n}}{\mathbf{p}}1$ values flagged as outliers.
 8:
$\mathbf{ldiff}$ – IntegerInput

On entry: the maximum number of values to be returned in arrays
diff and
llamb.
If
${\mathbf{ldiff}}\le 0$, arrays
diff and
llamb are not referenced and both
diff and
llamb may be
NULL.
 9:
$\mathbf{diff}\left[{\mathbf{ldiff}}\right]$ – doubleOutput

On exit: if
diff is not
NULL,
${\mathbf{diff}}\left[\mathit{i}1\right]$ holds
$\lefty\mu \right{\sigma}^{2}z$ for observation
${\mathbf{y}}\left[{\mathbf{iout}}\left[\mathit{i}1\right]1\right]$, for
$\mathit{i}=1,2,\dots ,\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ldiff}},{\mathbf{niout}}+1,{\mathbf{n}}{\mathbf{p}}1\right)$.
 10:
$\mathbf{llamb}\left[{\mathbf{ldiff}}\right]$ – doubleOutput

On exit: if
llamb is not
NULL,
${\mathbf{llamb}}\left[\mathit{i}1\right]$ holds
$\mathrm{log}\left({\lambda}^{2}\right)$ for observation
${\mathbf{y}}\left[{\mathbf{iout}}\left[\mathit{i}1\right]1\right]$, for
$\mathit{i}=1,2,\dots ,\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{ldiff}},{\mathbf{niout}}+1,{\mathbf{n}}{\mathbf{p}}1\right)$.
 11:
$\mathbf{fail}$ – NagError *Input/Output

The NAG error argument (see
Section 3.7 in How to Use the NAG Library and its Documentation).
6
Error Indicators and Warnings
 NE_ALLOC_FAIL

Dynamic memory allocation failed.
See
Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
 NE_BAD_PARAM

On entry, argument $\u2329\mathit{\text{value}}\u232a$ had an illegal value.
 NE_INT

On entry, ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{n}}\ge 3$.
 NE_INT_2

On entry, ${\mathbf{p}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: $1\le {\mathbf{p}}\le {\mathbf{n}}2$.
 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 2.7.6 in How to Use the NAG Library and its Documentation for further information.
 NE_NO_LICENCE

Your licence key may have expired or may not have been installed correctly.
See
Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
7
Accuracy
Not applicable.
8
Parallelism and Performance
nag_outlier_peirce (g07gac) is not threaded in any implementation.
One problem with Peirce's algorithm as implemented in
nag_outlier_peirce (g07gac) is the assumed relationship between
${\sigma}^{2}$, the variance using the full dataset, and
${\stackrel{~}{\sigma}}^{2}$, the variance with the potential outliers removed. In some cases, for example if the data
$y$ were the residuals from a linear regression, this assumption may not hold as the regression line may change significantly when outlying values have been dropped resulting in a radically different set of residuals. In such cases
nag_outlier_peirce_two_var (g07gbc) should be used instead.
10
Example
This example reads in a series of data and flags any potential outliers.
The dataset used is from Peirce's original paper and consists of fifteen observations on the vertical semidiameter of Venus.
10.1
Program Text
Program Text (g07gace.c)
10.2
Program Data
Program Data (g07gace.d)
10.3
Program Results
Program Results (g07gace.r)