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Dataplot Vol 2 Vol 1


    Let Subcommand
    Compute the test sensitivity between two binary variables.
    Given two variables with n parired observations where each variable has exactly two possible outcomes, we can generate the following 2x2 table:

        Variable 2  
      Variable 1 Success Failure Row Total

      Success N11 N12 N11 + N12
      Failure N21 N22 N21 + N22

      Column Total N11 + N21 N12 + N22 N

    The parameters N11, N12, N21, and N22 denote the counts for each category.

    Success and failure can denote any binary response. Dataplot expects "success" to be coded as "1" and "failure" to be coded as "0". Some typical examples would be:

    1. Variable 1 denotes whether or not a patient has a disease (1 denotes disease is present, 0 denotes disease not present). Variable 2 denotes the result of a test to detect the disease (1 denotes a positive result and 0 denotes a negative result).

    2. Variable 1 denotes whether an object is present or not (1 denotes present, 0 denotes absent). Variable 2 denotes a detection device (1 denotes object detected and 0 denotes object not detected).

    In these examples, the "ground truth" is typically given as variable 1 while some estimator of the ground truth is given as variable 2.

    The test sensitivity is then N11/(N11+N12). That is, the test sensitivity is the conditional probability that variable 2 is a success given that variable 1 is a success. In the context of the first example above, the test sensitivity is the probability that the test detects the disease given that the disease is present.

    LET <par> = TEST SENSITIVITY <y1> <y2>
                            <SUBSET/EXCEPT/FOR qualification>
    where <y1> is the first response variable;
                <y2> is the second response variable;
                <par> is a parameter where the computed test sensitivity is stored;
    and where the <SUBSET/EXCEPT/FOR qualification> is optional.
    The two variables must have the same number of elements.
    There are two ways you can define the response variables:

    1. Raw data - in this case, the variables contain 0's and 1's.

      If the data is not coded as 0's and 1's, Dataplot will check for the number of distinct values. If there are two distinct values, the minimum value is converted to 0's and the maximum value is converted to 1's. If there is a single distinct value, it is converted to 0's if it is less than 0.5 and to 1's if it is greater than or equal to 0.5. If there are more than two distinct values, an error is returned.

    2. Summary data - if there are two observations, the data is assummed to be the 2x2 summary table. That is,

        Y1(1) = N11
        Y1(2) = N21
        Y2(1) = N12
        Y2(2) = N22
    Dataplot statistics can be used in 20+ commands. For details, enter

Related Commands: Reference:
    Fleiss, Levin, and Paik (2003), "Statistical Methods for Rates and Proportions", Third Edition, Wiley, chapter 1.
    Categorical Data Analysis
Implementation Date:
    let n = 1
    let p = 0.2
    let y1 = binomial rand numb for i = 1 1 100
    let p = 0.1
    let y2 = binomial rand numb for i = 1 1 100
    let p = 0.4
    let y1 = binomial rand numb for i = 101 1 200
    let p = 0.08
    let y2 = binomial rand numb for i = 101 1 200
    let p = 0.15
    let y1 = binomial rand numb for i = 201 1 300
    let p = 0.18
    let y2 = binomial rand numb for i = 201 1 300
    let p = 0.6
    let y1 = binomial rand numb for i = 301 1 400
    let p = 0.45
    let y2 = binomial rand numb for i = 301 1 400
    let p = 0.3
    let y1 = binomial rand numb for i = 401 1 500
    let p = 0.1
    let y2 = binomial rand numb for i = 401 1 500
    let x = sequence 1 100 1 5
    let a = test sensitivity y1 y2 subset x = 1
    tabulate test sensitivity y1 y2 x
    label case asis
    xlimits 1 5
    major xtic mark number 5
    minor xtic mark number 0
    xtic mark offset 0.5 0.5
    ytic mark offset 0.05 0.05
    y1label Test Sensitivity
    x1label Group ID
    character x blank
    line blank solid
    test sensitivity plot y1 y2 x
    plot generated by sample program

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Date created: 04/13/2007
Last updated: 10/07/2016

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