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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- 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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- 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)
- 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)
- 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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