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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: 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
        
    plot generated by sample program
    .
    . 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
        

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

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