help mlogitroc
-------------------------------------------------------------------------------
 
Title

 mlogitroc  - Multiclass ROC Curves and AUC from Multinomial Logistic Regressio
> n


Syntax

        mlogitroc classvar var1 var2 ...  varp [if ...]


Description

    mlogitroc generates multiclass ROC curves for classification accuracy
    based on multinomial logistic regression using mlogit.  The algorithm
    begins by running mlogit B=100 times using bootstrapped records for each
    run while the original class labels are intact.  Class prediction is then
    performed for records not sampled during bootstrapping, and accuracy for
    the left out records is determined as the fraction of correct class
    membership predictions.  This results in B=100 realizations of the
    accuracy for the alternative distribution.  Next, B=100 mlogit runs are
    made again, but this time after shuffling class labels of all records
    prior to modeling, which results in B=100 realizations of null accuracy.
    Smoothed probability distributions are obtained for the B=100 alternative
    and null accuracy values using kernel density estimation (KDE, Gaussian
    kernel) to obtain 100 smoothed realizations for alternative and null
    accuracy.  The false positive rate (FPR), true positive rate (TPR), and
    area under the curve (AUC) are determined from the smooth pdfs derived
    from KDE.  Twoway scatter plots of the smoothed pdfs are constructed,
    followed by plotting the ROC curve.

    The default number of maximum iterations for reaching convergence is 20,
    which should be adequate for most runs.  A smaller value of maxiter=20
    helps terminate an mlogit run that will not converge - which may be
    common when using shuffled class labels to generate null accuracy values.

    Note: This algorithm was not designed for stepwise modeling, and assumes
    that the user(s) pre-select the informative input variables prior to
    modeling.

Input variables

    classvar must contain positive integer-based class labels such as
    1,2,...,C, where C is the total number of classes.

    var1 var2 ...  varp are the p independent variables.

Examples

    ---------------------------------------------------------------------------
        . mlogitroc y x1 x2 x3
        . mlogitroc outcome age gender treatment1yes2n0
        . mlogitroc class celltype time concentration heattemp
        . mlogitroc cluster weight bmi gene1exp gene2exp
        . mlogitroc outcome age treatment1yes2n0 if gender==2
        . mlogitroc cluster weight bmi gene1exp gene2exp if age>40
    ---------------------------------------------------------------------------


References

    This algorithm for multiclass ROC curve generation was introduced and
    used for the following papers:

    Peterson, L.E., Hoogeveen, R.C., Pownall, H.J., Morrisett, J.D.
    Classification Analysis of Surface-enhanced Laser Desorption/Ionization
    Mass Spectral Serum Profiles for Prostate Cancer.  International Joint
    Conference on Neural Networks (IJCNN06), July 2006.  IEEE Press,
    Piscataway(NJ), 2006.

    Peterson, L.E., Coleman, M.A. Machine learning-based receiver operating
    characteristic (ROC) curves for crisp and fuzzy classification of DNA
    microarrays in cancer research. Int. J. of Approximate Reasoning. 47:
    17-36, 2008.

Also see

      Help:  [R] mlogit, [R] kdensity, [R] summarize, [G] twoway