help for ^distan^

Calculates similarity and dissimilarity measures - ------------------------------------------------

^distan^ varlist^, st^andar ^di^stan^(^distance-measure^)^ ^av^erage ^sa^ve^(^filename^)^

Description - -----------

^distan^ calculates similarity and dissimilarity measures between observations

Options - -------

^st^andar standarizes the variables in ^varlist^.

^di^stan^(^distance-measure^)^. Euclidian distance is used as default. The following measures are allowed:

^co^variance: covariance between observations.

^cor^relation: correlation coefficient between observations.

^ma^nhattan: Manhattan distance between observations.

^mi^nkowski^[^n^]^: Minkowski metrics of dimension ^n^. Euclidian and Manhattan distances are particular cases of dimension 2 and 1 respectively.

^average^ calculates mean distances if Manhattan, Euclidian or Minkowski distances are specified. This option is specially useful if missing values are present in the original dataset.

^sa^ve^(^filename^)^ saves the results to ^filename^

Remarks - -------

Saved output contains three variables: ^elem1^, ^elem2^ and ^distan^. The first two identify the observation numbers of the original dataset for which the distance ^distan^ was calculated.

Examples - --------

. ^distan x y z, st^ . ^distan x y z, di(mi[3.2]) av sa(c:\dist.dta)^ . ^distan x y z, di(cor)^

Author - ------

Jose Maria Sanchez Saez