class GoodnessOfFit_CS extends Error

The GoodnessOfFit_CS class is used to fit data to probability distributions. Choosing the number of 'intervals' can be challenging and can affect the result: Suggestions: each interval should have 'E_i = n*p_i >= 5' and intervals >= sqrt (n). It uses the Chi-square goodness of fit test with equal width intervals.

See also

www.eg.bucknell.edu/~xmeng/Course/CS6337/Note/master/node66.html Compute the following for each interval and sum over all intervals. (O_i - E_i)^2 / E_i where O_i and E_i are the observed and expected counts for interval 'i', respectively.

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  1. new GoodnessOfFit_CS(d: VectorD, dmin: Double, dmax: Double, intervals: Int = 10)

    d

    the sample data points/vector

    dmin

    the minimum value for d

    dmax

    the maximum value for d

    intervals

    the number of intervals for the data's histogram

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  8. def fit(rv: Variate, met: Metric = pearson): Boolean

    Perform a Chi-square goodness of fit test, matching the histogram of the given data 'd' with the random variable's probability function 'pf' (pdf).

    Perform a Chi-square goodness of fit test, matching the histogram of the given data 'd' with the random variable's probability function 'pf' (pdf).

    rv

    the random variate to test

    met

    the discrepancy metric to use (defaults to pearson)

  9. def fit2(d2: VectorD, met: Metric = pearson): Boolean

    Perform a Chi-square goodness of fit test, matching the histograms of the given data 'd' with an alternative data-set/vector 'd2'.

    Perform a Chi-square goodness of fit test, matching the histograms of the given data 'd' with an alternative data-set/vector 'd2'.

    d2

    the alternate data set

    met

    the discrepancy metric to use (defaults to pearson)

  10. def fit3(d2: VectorD, met: Metric = pearson): Boolean

    Perform a Chi-square goodness of fit test, matching the counts of the given data 'd' with an alternative data-set/vector 'd2'.

    Perform a Chi-square goodness of fit test, matching the counts of the given data 'd' with an alternative data-set/vector 'd2'.

    d2

    the alternate data set

    met

    the discrepancy metric to use (defaults to pearson)

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