Dataplot Vol 2 Vol 1

# RLP

Name:
RLP (LET)
Type:
Let Subcommand
Purpose:
Given a response variable containing z-scores and an associated variable containing the material-id, compute the relative laboratory performance (RLP) of a variable.
Description:
One scenario for proficiency testing described in the ISO 13528 standard is for the case where there are multiple rounds of testing. Given the proficiency data

 Z - a variable containing the response data in z-score units MATID - a variable containing the material-id which the plot is generated ROUNDID - a variable containing the round-id LABID - a variable containing the lab-id

For ISO 13528 multi-round proficiency studies, the relative laboratory performance (RLP) for a given laboratory with N z-scores (Zi) is defined as

$$\mbox{RLP} = \sqrt{ \frac{\sum_{i=1}^{N}{Z_{i}^{2}}} {\mbox{NMAT}}}$$

where NMAT is the number of materials. An RLP near 1 indicates average performance and an RLP greater than 1.5 indicates that the laboratory may be problematic. An advantage of this statistic is that z-scores of opposite sign do not cancel each other out. A disadvantage is that this statistic is suspectible to outliers in the z-scores.

The RLP statistic is discussed in Uhlig and Lischer (1998). The RLP statistic is an examples of a combination score (i.e., the statistic is a combination of many individual z-scores). Although the ISO 13528 standard recommends against using combination scores, these can be helpful in judging the overall performance of a laboratory. These combination scores can be used to identify laboratories that are potentially problematic. These laboratories can then be examined more carefully. For example: is the poor performance due to one or a few outliers? is the lab consistently high or consistently low? does the laboratory need to carefully examine their procedures?

This statistic is used to compute the RLP for a single laboratory. Note that the material-id variable is only used to determine the number of materials (NMAT in the above formula).

The most typical use of this statistic is with the TABULATE command or the STATISTIC PLOT command where the group-id variable is the laboratory-id variable. For example, the command

RLP PLOT Z MATID LABID

can be used to generate a plot of the RLP values for each laboratory.

Syntax:
LET <par> = RLP <z> <matid>             <SUBSET/EXCEPT/FOR qualification>
where <z> is the response variable containing z-scores;
<matid> is a variable containing the material-id's;
<par> is a parameter where the computed rlp is saved;
and where the <SUBSET/EXCEPT/FOR qualification> is optional.

The SUBSET clause can be used to specify a specific laboratory for which to compute the statistic.

Examples:
LET A = RLP Z MAT
LET A = RLP Z MAT SUBSET LAB = 23
TABULATE RLP Z MATID LABID
RLP PLOT Z MATID LABID
Note:
The ISO 13528 standard defines a number of methods for computing the z-scores (enter HELP ISO 13528 ZSCORE, HELP ISO 13528 ZPRIME SCORE, and HELP ISO 13528 ZETA SCORE for details). For this reason, the ISO 13528 RLP PLOT command does not automatically compute the z-scores from the original response data.
Note:
In some applications it may be desired to cap the value of outliers. This is most common when the response variable is a z-score or some other standardized score.

To specify this value, enter the command

LET CAPVALUE = <value>

where <value> is typically 3 or 4 (if the reponse data are z-scores or z-score type data). Note that the value represents an absolute value. For example, if CAPVALUE is 4, values greater than 4 will be set to 4 and values less than -4 will be set to -4.

Note:
Dataplot statistics can be used in a number of commands. For details, enter

Default:
None
Synonyms:
None
Related Commands:
 ISO 13528 RLP PLOT = Generate a plot of relative laboratory ISO 13528 PLOT = Generate an ISO 13528 plot. ISO 13528 ZSCORE PLOT = Generate an ISO 13528 plot. ISO 13528 CONTROL CHART = Generate an ISO 13528 control chart. ISO 13528 ZSCORE = Compute z-scores as defined in the ISO 13528 standard. ISO 13528 ZPRIME SCORE = Compute z-prime scores as defined in the ISO 13528 standard. ISO 13528 ZETA SCORE = Compute zeta scores as defined in the ISO 13528 standard. TABULATE = Compute a statistic for one or more group variables. STATISTIC PLOT = Generate a statistic versus index plot.
References:
Uhlig and Lischer (1998), "Statistically-based Performance Characteristics in Laboratory Performance Studies", Analyst, 123, pp. 167-172.

ISO 13528 (2005), "Statistical Methods for use in proficiency testing by interlaboratory comparisons," First Edition, 2005-09-01.

Applications:
Multiple Round Proficiency Testing
Implementation Date:
2015/2
Program:
. Step 1:   Read the data
.
dimension 40 columns
skip 25
read turner.dat labid z year quarter matid matave
skip 0
let labcoded = code labid
.
. Step 2:   Set plot control setting
.
case asis
title case asis
title offset 2
label case asis
y1label Relative Laboratory Performance
x1label Laboratory
title RLP Versus Laboratory for TURNER.DAT
y1tic mark label decimal 1
tic mark offset units data
x1tic mark offset 2 0
y1tic mark offset 0.2 0.5
ylimits 0 3
.
line blank
character circle
character hw 0.5 0.375
character fill on
.
. Step 3:   Generate plot of RLP vs Lab
.
rlp plot z matid labcoded
line dash
line color blue
drawsdsd 15 1.5 85 1.5
line color red
drawsdsd 15 3.0 85 3.0

.
. Step 4:   Tabulate RLP values for each laboratory
.
set write decimals 4
tabulate rlp z matid labid

The following output is generated
            Cross Tabulate RELATIVE LABORATORY PERFORMANCE

(Response Variables: Z        MATID   )
---------------------------------------------
LABID      |   RELATIVE LABORA
---------------------------------------------
1.0000   |            1.2635
2.0000   |            0.5968
3.0000   |            0.6613
4.0000   |            0.9571
5.0000   |            0.7537
6.0000   |            0.8483
7.0000   |            1.0330
8.0000   |            1.2063
9.0000   |            1.3412
10.0000   |            1.0391
11.0000   |            1.1607
12.0000   |            0.9048
13.0000   |            1.1061
14.0000   |            0.6820
15.0000   |            0.9297
16.0000   |            1.2919
17.0000   |            0.9640
18.0000   |            0.7816
19.0000   |            1.3733
20.0000   |            0.9002
21.0000   |            1.2505
22.0000   |            0.6907
23.0000   |            0.6608
24.0000   |            2.2597
25.0000   |            1.2199
26.0000   |            0.6441
27.0000   |            1.4659
28.0000   |            0.8332
29.0000   |            0.7345
30.0000   |            1.1149
32.0000   |            0.9611
33.0000   |            0.7722
34.0000   |            1.0624
35.0000   |            1.1702
36.0000   |            0.9016
37.0000   |            2.7951
38.0000   |            1.1969
39.0000   |            0.9013
40.0000   |            0.7844
41.0000   |            1.7227
43.0000   |            0.6891
44.0000   |            1.0015
45.0000   |            0.6377
46.0000   |            0.7925
47.0000   |            0.4359
48.0000   |            2.2051
49.0000   |            1.3257
50.0000   |            0.5562
51.0000   |            0.7882
52.0000   |            1.2762
53.0000   |            0.8490
54.0000   |            0.7403
55.0000   |            0.6298
56.0000   |            0.4445
57.0000   |            0.8096
58.0000   |            1.4416
59.0000   |            0.9948
60.0000   |            1.1370
61.0000   |            0.9833
62.0000   |            0.7544
64.0000   |            0.7930
65.0000   |            0.4510
66.0000   |            0.9146
67.0000   |            2.2194
68.0000   |            1.4462
69.0000   |            0.9027
70.0000   |            1.0099
71.0000   |            0.5860
72.0000   |            0.6815
73.0000   |            1.0609
74.0000   |            0.8879
75.0000   |            1.1377
76.0000   |            0.6527
77.0000   |            0.5023
78.0000   |            1.2167
79.0000   |            1.0140
80.0000   |            1.0788
81.0000   |            2.1828
82.0000   |            1.1335
83.0000   |            0.4704
84.0000   |            0.6805
85.0000   |            0.5462
86.0000   |            1.2086
87.0000   |            0.7786


NIST is an agency of the U.S. Commerce Department.

Date created: 02/09/2015
Last updated: 02/09/2015