SED navigation bar go to SED home page go to Dataplot home page go to NIST home page SED Home Page SED Staff SED Projects SED Products and Publications Search SED Pages
Dataplot Vol 2 Vol 1

FALSE POSITIVES

Name:
    FALSE POSITIVES (LET)
Type:
    Let Subcommand
Purpose:
    Compute the proportion of false positives between two binary variables.
Description:
    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 proportion of false positives is then N21/N (i.e., the number of cases where variable 1 is a failure and variable 2 is a success). In the context of the first examples above, the test detected the disease when it was in fact not present.

Syntax:
    LET <par> = FALSE POSITIVE <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 false positive proportion is stored;
    and where the <SUBSET/EXCEPT/FOR qualification> is optional.
Examples:
    LET A = FALSE POSITIVE Y1 Y2
    LET A = FALSE POSITIVE Y1 Y2 SUBSET TAG > 2
Note:
    The two variables must have the same number of elements.
Note:
    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
Note:
    This commands returns the proportion of false positives. If you need raw counts or percentages, you can enter the commands

      LET N = SIZE Y1
      LET FALSEPOS = FALSE POSITIVE Y1 Y2
      LET FPCOUNT = N*FALSEPOS
      LET FPPERC = 100*FALSEPOS
Note:
    This command has been extended to support the case for RxC tables where R denotes the number of categories for variable one and C denotes the number of categories for variable two. Note that Dataplot assumes that the categories can be meaningfully ordered (Dataplot assumes a "small" to "large" ordering).

    In this case, if variable one denotes "ground truth" and variable two denotes the estimate of ground truth, then we define:

    1. A correct value is the case where the estimated category is the same as the ground truth category.

      For this case, we do not distinguish between "true positives" and "true negatives" as we do for the 2x2 case.

    2. A false positive is the case where the estimated category is too large.

    3. A false negative is the case where the estimated category is too small.
Note:
    Dataplot statistics can be used in a number of commands. For details, enter

Default:
    None
Synonyms:
    None
Related Commands: Reference:
    Fleiss, Levin, and Paik (2003), "Statistical Methods for Rates and Proportions," Third Edition, Wiley, chapter 1.
Applications:
    Categorical Data Analysis
Implementation Date:
    2007/04
Program:
     
    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 = false positives y1 y2 subset x = 1
    tabulate false positives 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 Proportion of False Positives
    x1label Group ID
    character x blank
    line blank solid
    .
    false positives plot y1 y2 x
        
    plot generated by sample program

Privacy Policy/Security Notice
Disclaimer | FOIA

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

Date created: 04/13/2007
Last updated: 11/16/2015

Please email comments on this WWW page to alan.heckert@nist.gov.