NAG FL Interface
g07gaf (outlier_peirce_1var)
1
Purpose
g07gaf identifies outlying values using Peirce's criterion.
2
Specification
Fortran Interface
Subroutine g07gaf ( 
n, p, y, mean, var, iout, niout, ldiff, diff, llamb, ifail) 
Integer, Intent (In) 
:: 
n, p, ldiff 
Integer, Intent (Inout) 
:: 
ifail 
Integer, Intent (Out) 
:: 
iout(n), niout 
Real (Kind=nag_wp), Intent (In) 
:: 
y(n), mean, var 
Real (Kind=nag_wp), Intent (Out) 
:: 
diff(ldiff), llamb(ldiff) 

C Header Interface
#include <nag.h>
void 
g07gaf_ (const Integer *n, const Integer *p, const double y[], const double *mean, const double *var, Integer iout[], Integer *niout, const Integer *ldiff, double diff[], double llamb[], Integer *ifail) 

C++ Header Interface
#include <nag.h> extern "C" {
void 
g07gaf_ (const Integer &n, const Integer &p, const double y[], const double &mean, const double &var, Integer iout[], Integer &niout, const Integer &ldiff, double diff[], double llamb[], Integer &ifail) 
}

The routine may be called by the names g07gaf or nagf_univar_outlier_peirce_1var.
3
Description
g07gaf 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:
$\mathbf{n}$ – Integer
Input

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

2:
$\mathbf{p}$ – Integer
Input

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:
$\mathbf{y}\left({\mathbf{n}}\right)$ – Real (Kind=nag_wp) array
Input

On entry: $y$, the data being tested.

4:
$\mathbf{mean}$ – Real (Kind=nag_wp)
Input

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:
$\mathbf{var}$ – Real (Kind=nag_wp)
Input

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:
$\mathbf{iout}\left({\mathbf{n}}\right)$ – Integer array
Output

On exit: the indices of the values in
y sorted in descending order of the absolute difference from the mean, therefore
$\left{\mathbf{y}}\left({\mathbf{iout}}\left(\mathit{i}1\right)\right)\mu \right\ge \left{\mathbf{y}}\left({\mathbf{iout}}\left(\mathit{i}\right)\right)\mu \right$, 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:
$\mathbf{ldiff}$ – Integer
Input

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.

9:
$\mathbf{diff}\left({\mathbf{ldiff}}\right)$ – Real (Kind=nag_wp) array
Output

On exit: ${\mathbf{diff}}\left(\mathit{i}\right)$ holds $\lefty\mu \right{\sigma}^{2}z$ for observation ${\mathbf{y}}\left({\mathbf{iout}}\left(\mathit{i}\right)\right)$, 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:
$\mathbf{llamb}\left({\mathbf{ldiff}}\right)$ – Real (Kind=nag_wp) array
Output

On exit: ${\mathbf{llamb}}\left(\mathit{i}\right)$ holds $\mathrm{log}\left({\lambda}^{2}\right)$ for observation ${\mathbf{y}}\left({\mathbf{iout}}\left(\mathit{i}\right)\right)$, 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:
$\mathbf{ifail}$ – Integer
Input/Output

On entry:
ifail must be set to
$0$,
$1\text{or}1$. If you are unfamiliar with this argument you should refer to
Section 4 in the Introduction to the NAG Library FL Interface for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
$1\text{or}1$ is recommended. If the output of error messages is undesirable, then the value
$1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is
$0$.
When the value $\mathbf{1}\text{or}\mathbf{1}$ is used it is essential to test the value of ifail on exit.
On exit:
${\mathbf{ifail}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see
Section 6).
6
Error Indicators and Warnings
If on entry
${\mathbf{ifail}}=0$ or
$1$, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Errors or warnings detected by the routine:
 ${\mathbf{ifail}}=1$

On entry, ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{n}}\ge 3$.
 ${\mathbf{ifail}}=2$

On entry, ${\mathbf{p}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: $1\le {\mathbf{p}}\le {\mathbf{n}}2$.
 ${\mathbf{ifail}}=99$
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 7 in the Introduction to the NAG Library FL Interface for further information.
 ${\mathbf{ifail}}=399$
Your licence key may have expired or may not have been installed correctly.
See
Section 8 in the Introduction to the NAG Library FL Interface for further information.
 ${\mathbf{ifail}}=999$
Dynamic memory allocation failed.
See
Section 9 in the Introduction to the NAG Library FL Interface for further information.
7
Accuracy
Not applicable.
8
Parallelism and Performance
g07gaf is not threaded in any implementation.
One problem with Peirce's algorithm as implemented in
g07gaf 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
g07gbf 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
10.2
Program Data
10.3
Program Results