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corrcoef


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 -- R = corrcoef (X)
 -- R = corrcoef (X, Y)
 -- [R, P] = corrcoef (...)
 -- [R, P, LCI, HCI] = corrcoef (...)
 -- [...] = corrcoef (..., PARAM, VALUE, ...)
     Compute a matrix of correlation coefficients.

     X is an array where each column contains a variable and each row is
     an observation.

     If a second input Y (of the same size as X) is given then calculate
     the correlation coefficients between X and Y.

     R is a matrix of Pearson's product moment correlation coefficients
     for each pair of variables.

     P is a matrix of pair-wise p-values testing for the null hypothesis
     of a correlation coefficient of zero.

     LCI and HCI are matrices containing, respectively, the lower and
     higher bounds of the 95% confidence interval of each correlation
     coefficient.

     PARAM, VALUE are pairs of optional parameters and values.  Valid
     options are:

     "alpha"
          Confidence level used for the definition of the bounds of the
          confidence interval, LCI and HCI.  Default is 0.05, i.e., 95%
          confidence interval.

     "rows"
          Determine processing of NaN values.  Acceptable values are
          "all", "complete", and "pairwise".  Default is "all".  With
          "complete", only the rows without NaN values are considered.
          With "pairwise", the selection of NaN-free rows is made for
          each pair of variables.

     See also: corr, cov, cor_test.


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Compute a matrix of correlation coefficients.



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crosstab


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 -- T = crosstab (X, Y)
     Create a cross-tabulation (contingency table) T from data vectors.

     The inputs X, Y must be vectors of equal length with a data type of
     numeric, logical, or char.

     Currently, only 1- and 2-dimensional tables are supported.


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Create a cross-tabulation (contingency table) T from data vectors.



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mad


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 -- mad (X)
 -- mad (X, OPT)
 -- mad (X, OPT, DIM)
     Compute the mean or median absolute deviation of the elements of X.

     The mean absolute deviation is defined as

          MAD = mean (abs (X - mean (X)))

     The median absolute deviation is defined as

          MAD = median (abs (X - median (X)))

     If X is a matrix, compute 'mad' for each column and return results
     in a row vector.  For a multi-dimensional array, the calculation is
     done over the first non-singleton dimension.

     The optional argument OPT determines whether mean or median
     absolute deviation is calculated.  The default is 0 which
     corresponds to mean absolute deviation; A value of 1 corresponds to
     median absolute deviation.

     If the optional argument DIM is given, operate along this
     dimension.

     As a measure of dispersion, 'mad' is less affected by outliers than
     'std'.

     See also: bounds, range, iqr, std, mean, median.


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Compute the mean or median absolute deviation of the elements of X.





