g01dh computes the ranks, Normal scores, an approximation to the Normal scores or the exponential scores as requested by you.

Syntax

C#
public static void g01dh( string scores, string ties, int n, double[] x, double[] r, out int ifail )

Visual Basic
Public Shared Sub g01dh ( _ scores As String, _ ties As String, _ n As Integer, _ x As Double(), _ r As Double(), _ <OutAttribute> ByRef ifail As Integer _ )

Visual C++
public: static void g01dh( String^ scores, String^ ties, int n, array<double>^ x, array<double>^ r, [OutAttribute] int% ifail )

F#
static member g01dh : scores : string * ties : string * n : int * x : float[] * r : float[] * ifail : int byref -> unit

Parameters

scores

Type: System..::..String

On entry: indicates which of the following scores are required.

$scores = "R"$: The ranks.
$scores = "N"$: The Normal scores, that is the expected value of the Normal order statistics.
$scores = "B"$: The Blom version of the Normal scores.
$scores = "T"$: The Tukey version of the Normal scores.
$scores = "V"$: The van der Waerden version of the Normal scores.
$scores = "S"$: The Savage scores, that is the expected value of the exponential order statistics.

Constraint:

scores = "R"

"N"

"B"

"T"

"V"

"S"

ties

Type: System..::..String

On entry: indicates which of the following methods is to be used to assign scores to tied observations.

$ties = "A"$: The average of the scores for tied observations is used.
$ties = "L"$: The lowest score in the group of ties is used.
$ties = "H"$: The highest score in the group of ties is used.
$ties = "N"$: The nonrepeatable random number generator is used to randomly untie any group of tied observations.
$ties = "R"$: The repeatable random number generator is used to randomly untie any group of tied observations.
$ties = "I"$: Any ties are ignored, that is the scores are assigned to tied observations in the order that they appear in the data.

Constraint:

ties = "A"

"L"

"H"

"N"

"R"

"I"

n: Type: System..::..Int32
On entry: $n$ , the number of observations.

Constraint: $n \geq 1$ .

x: Type: array<System..::..Double>[]()[][]
An array of size [n]
On entry: the sample of observations, $x_{i}$ , for $i = 1, 2, \dots, n$ .

r: Type: array<System..::..Double>[]()[][]
An array of size [n]
On exit: contains the scores, $s_{i}$ , for $i = 1, 2, \dots, n$ , as specified by scores.

ifail: Type: System..::..Int32%
On exit: $ifail = 0$ unless the method detects an error or a warning has been flagged (see [Error Indicators and Warnings]).

Description

g01dh computes one of the following scores for a sample of observations,

x_{1}, x_{2}, \dots, x_{n}

Rank Scores

The ranks are assigned to the data in ascending order, that is the

i

th observation has score

s_{i} = k

if it is the

k

th smallest observation in the sample.

Normal Scores

The Normal scores are the expected values of the Normal order statistics from a sample of size

n

. If

x_{i}

is the

k

th smallest observation in the sample, then the score for that observation,

s_{i}

, is

E (Z_{k})

where

Z_{k}

is the

k

th order statistic in a sample of size

n

from a standard Normal distribution and

E

is the expectation operator.

Blom, Tukey and van der Waerden Scores

These scores are approximations to the Normal scores. The scores are obtained by evaluating the inverse cumulative Normal distribution function,

Φ^{- 1} (\cdot)

, at the values of the ranks scaled into the interval

(0, 1)

using different scaling transformations.

The Blom scores use the scaling transformation

\frac{r_{i} - \frac{3}{8}}{n + \frac{1}{4}}

for the rank

r_{i}

, for

i = 1, 2, \dots, n

. Thus the Blom score corresponding to the observation

x_{i}

s_{i} = Φ^{- 1} (\frac{r_{i} - \frac{3}{8}}{n + \frac{1}{4}}) .

The Tukey scores use the scaling transformation

\frac{r_{i} - \frac{1}{3}}{n + \frac{1}{3}}

; the Tukey score corresponding to the observation

x_{i}

s_{i} = Φ^{- 1} (\frac{r_{i} - \frac{1}{3}}{n + \frac{1}{3}}) .

The van der Waerden scores use the scaling transformation

\frac{r_{i}}{n + 1}

; the van der Waerden score corresponding to the observation

x_{i}

s_{i} = Φ^{- 1} (\frac{r_{i}}{n + 1}) .

The van der Waerden scores may be used to carry out the van der Waerden test for testing for differences between several population distributions, see Conover (1980).

Savage Scores

The Savage scores are the expected values of the exponential order statistics from a sample of size

n

. They may be used in a test discussed by Savage (1956) and Lehmann (1975). If

x_{i}

is the

k

th smallest observation in the sample, then the score for that observation is

s_{i} = E (Y_{k}) = \frac{1}{n} + \frac{1}{n - 1} + \dots + \frac{1}{n - k + 1},

where

Y_{k}

is the

k

th order statistic in a sample of size

n

from a standard exponential distribution and

E

is the expectation operator.

Ties may be handled in one of five ways. Let

x_{t (i)}

, for

i = 1, 2, \dots, m

, denote

m

tied observations, that is

x_{t (1)} = x_{t (2)} = \dots = x_{t (m)}

with

t (1) < t (2) < \dots < t (m)

. If the rank of

x_{t (1)}

k

, then if ties are ignored the rank of

x_{t (j)}

will be

k + j - 1

. Let the scores ignoring ties be

s_{t (1)}^{*}, s_{t (2)}^{*}, \dots, s_{t (m)}^{*}

. Then the scores,

s_{t (i)}

, for

i = 1, 2, \dots, m

, may be calculated as follows:

– if averages are used, then $s_{t (i)} = \sum_{j = 1}^{m} s_{t (j)}^{*} / m$ ;
– if the lowest score is used, then $s_{t (i)} = s_{t (1)}^{*}$ ;
– if the highest score is used, then $s_{t (i)} = s_{t (m)}^{*}$ ;
– if ties are to be broken randomly, then $s_{t (i)} = s_{t (I)}^{*}$ where $I \in \{random permutation of 1, 2, \dots, m\}$ ;
– if ties are to be ignored, then $s_{t (i)} = s_{t (i)}^{*}$ .

References

Blom G (1958) Statistical Estimates and Transformed Beta-variables Wiley

Conover W J (1980) Practical Nonparametric Statistics Wiley

Lehmann E L (1975) Nonparametrics: Statistical Methods Based on Ranks Holden–Day

Savage I R (1956) Contributions to the theory of rank order statistics – the two-sample case Ann. Math. Statist. 27 590–615

Tukey J W (1962) The future of data analysis Ann. Math. Statist. 33 1–67

Error Indicators and Warnings

Errors or warnings detected by the method:

$ifail = 1$

On entry,	$scores \neq "R"$ , $"N"$ , $"B"$ , $"T"$ , $"V"$ or $"S"$ ,
or	$ties \neq "A"$ , $"L"$ , $"H"$ , $"N"$ , $"R"$ or $"I"$ ,
or	$n < 1$ .

$ifail = -9000$: An error occured, see message report.
$ifail = -8000$: Negative dimension for array $〈value〉$
$ifail = -6000$: Invalid Parameters $〈value〉$

Accuracy

For

scores = "R"

, the results should be accurate to machine precision.

For

scores = "S"

, the results should be accurate to a small multiple of machine precision.

For

scores = "N"

, the results should have a relative accuracy of at least

\max (100 \times ε, 10^{- 8})

where

ε

is the machine precision.

For

scores = "B"

"T"

"V"

, the results should have a relative accuracy of at least

\max (10 \times ε, 10^{- 12})

Parallelism and Performance

None.

Further Comments

If more accurate Normal scores are required g01da should be used with appropriate settings for the input parameter etol.

Example

This example computes and prints the Savage scores for a sample of five observations. The average of the scores of any tied observations is used.

Example program (C#): g01dhe.cs

Example program data: g01dhe.d

Example program results: g01dhe.r

Syntax

Parameters

Description

References

Error Indicators and Warnings

Accuracy

Parallelism and Performance

Further Comments

Example

See Also