@c -----------------------------------------------------------------------------
@c File     : descriptive.de.texi
@c License  : GNU General Public License (GPL)
@c Language : German
@c Original : descriptive.texi revision 01.08.2011
@c Date     : 08.11.2010
@c Revision : 16.10.2011
@c 
@c This file is part of Maxima -- GPL CAS based on DOE-MACSYMA
@c -----------------------------------------------------------------------------

@menu
* Introduction to descriptive::
* Functions and Variables for data manipulation::
* Functions and Variables for descriptive statistics::
* Functions and Variables for specific multivariate descriptive statistics::
* Functions and Variables for statistical graphs::
@end menu

@c -----------------------------------------------------------------------------
@node Introduction to descriptive, Functions and Variables for data manipulation, Package descriptive, Package descriptive
@section Introduction to descriptive
@c -----------------------------------------------------------------------------

Package @code{descriptive} contains a set of functions for making descriptive 
statistical computations and graphing.  Together with the source code there are 
three data sets in your Maxima tree: @code{pidigits.data}, @code{wind.data} and 
@code{biomed.data}.

Any statistics manual can be used as a reference to the functions in package 
@code{descriptive}.

For comments, bugs or suggestions, please contact me at 
@var{'mario AT edu DOT xunta DOT es'}.

Here is a simple example on how the descriptive functions in @code{descriptive} 
do they work, depending on the nature of their arguments, lists or matrices,

@example
(%i1) load (descriptive)$
@group
(%i2) /* univariate sample */   mean ([a, b, c]);
                            c + b + a
(%o2)                       ---------
                                3
@end group
@group
(%i3) matrix ([a, b], [c, d], [e, f]);
                            [ a  b ]
                            [      ]
(%o3)                       [ c  d ]
                            [      ]
                            [ e  f ]
@end group
@group
(%i4) /* multivariate sample */ mean (%);
                      e + c + a  f + d + b
(%o4)                [---------, ---------]
                          3          3
@end group
@end example

Note that in multivariate samples the mean is calculated for each column.

In case of several samples with possible different sizes, the Maxima function 
@code{map} can be used to get the desired results for each sample,

@example
(%i1) load (descriptive)$
@group
(%i2) map (mean, [[a, b, c], [d, e]]);
                        c + b + a  e + d
(%o2)                  [---------, -----]
                            3        2
@end group
@end example

In this case, two samples of sizes 3 and 2 were stored into a list.

Univariate samples must be stored in lists like

@example
@group
(%i1) s1 : [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5];
(%o1)           [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]
@end group
@end example

and multivariate samples in matrices as in

@example
@group
(%i1) s2 : matrix ([13.17, 9.29], [14.71, 16.88], [18.50, 16.88],
             [10.58, 6.63], [13.33, 13.25], [13.21,  8.12]);
                        [ 13.17  9.29  ]
                        [              ]
                        [ 14.71  16.88 ]
                        [              ]
                        [ 18.5   16.88 ]
(%o1)                   [              ]
                        [ 10.58  6.63  ]
                        [              ]
                        [ 13.33  13.25 ]
                        [              ]
                        [ 13.21  8.12  ]
@end group
@end example

In this case, the number of columns equals the random variable dimension and 
the number of rows is the sample size.

Data can be introduced by hand, but big samples are usually stored in plain 
text files.  For example, file @code{pidigits.data} contains the first 100 
digits of number @code{%pi}:
@example
@group
      3
      1
      4
      1
      5
      9
      2
      6
      5
      3 ...
@end group
@end example

In order to load these digits in Maxima,

@example
(%i1) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i2) length (s1);
(%o2)                          100
@end group
@end example

On the other hand, file @code{wind.data} contains daily average wind speeds at 
5 meteorological stations in the Republic of Ireland (This is part of a data 
set taken at 12 meteorological stations.  The original file is freely 
downloadable from the StatLib Data Repository and its analysis is discused in 
Haslett, J., Raftery, A. E. (1989) @var{Space-time Modelling with Long-memory 
Dependence: Assessing Ireland's Wind Power Resource, with Discussion}.  Applied 
Statistics 38, 1-50).  This loads the data:

@example
(%i1) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i2) length (s2);
(%o2)                          100
@end group
@group
(%i3) s2 [%]; /* last record */
(%o3)            [3.58, 6.0, 4.58, 7.62, 11.25]
@end group
@end example

Some samples contain non numeric data.  As an example, file @code{biomed.data} 
(which is part of another bigger one downloaded from the StatLib Data 
Repository) contains four blood measures taken from two groups of patients, 
@code{A} and @code{B}, of different ages,

@example
(%i1) s3 : read_matrix (file_search ("biomed.data"))$
@group
(%i2) length (s3);
(%o2)                          100
@end group
@group
(%i3) s3 [1]; /* first record */
(%o3)            [A, 30, 167.0, 89.0, 25.6, 364]
@end group
@end example

The first individual belongs to group @code{A}, is 30 years old and his/her 
blood measures were 167.0, 89.0, 25.6 and 364.

One must take care when working with categorical data.  In the next example, 
symbol @code{a} is asigned a value in some previous moment and then a sample 
with categorical value @code{a} is taken,

@example
(%i1) a : 1$
@group
(%i2) matrix ([a, 3], [b, 5]);
                            [ 1  3 ]
(%o2)                       [      ]
                            [ b  5 ]
@end group
@end example

@c -----------------------------------------------------------------------------
@node Functions and Variables for data manipulation, Functions and Variables for descriptive statistics, Introduction to descriptive, Package descriptive
@section Functions and Variables for data manipulation
@c -----------------------------------------------------------------------------


@c --- 29.12.2012 --------------------------------------------------------------
@deffn {Function} build_sample (@var{list})
@deffnx {Function} build_sample (@var{matrix})
Builds a sample from a table of absolute frequencies.
The input table can be a matrix or a list of lists, all of
them of equal size. The number of columns or the length of
the lists must be greater than 1. The last element of each
row or list is interpreted as the absolute frequency.
The output is always a sample in matrix form.

Examples:

Univariate frequency table.

@c ===beg===
@c load (descriptive)$
@c sam1: build_sample([[6,1], [j,2], [2,1]]);
@c mean(sam1);
@c barsplot(sam1) $
@c ===end===
@example
(%i1) load (descriptive)$
(%i2) sam1: build_sample([[6,1], [j,2], [2,1]]);
                       [ 6 ]
                       [   ]
                       [ j ]
(%o2)                  [   ]
                       [ j ]
                       [   ]
                       [ 2 ]
(%i3) mean(sam1);
                      2 j + 8
(%o3)                [-------]
                         4
(%i4) barsplot(sam1) $
@end example

Multivariate frequency table.

@c ===beg===
@c load (descriptive)$
@c sam2: build_sample([[6,3,1], [5,6,2], [u,2,1],[6,8,2]]) ;
@c cov(sam2);
@c barsplot(sam2, grouping=stacked) $
@c ===end===
@example
(%i1) load (descriptive)$
(%i2) sam2: build_sample([[6,3,1], [5,6,2], [u,2,1],[6,8,2]]) ;
                           [ 6  3 ]
                           [      ]
                           [ 5  6 ]
                           [      ]
                           [ 5  6 ]
(%o2)                      [      ]
                           [ u  2 ]
                           [      ]
                           [ 6  8 ]
                           [      ]
                           [ 6  8 ]
(%i3) cov(sam2);
       [   2                 2                            ]
       [  u  + 158   (u + 28)     2 u + 174   11 (u + 28) ]
       [  -------- - ---------    --------- - ----------- ]
(%o3)  [     6          36            6           12      ]
       [                                                  ]
       [ 2 u + 174   11 (u + 28)            21            ]
       [ --------- - -----------            --            ]
       [     6           12                 4             ]
(%i4) barsplot(sam2, grouping=stacked) $
@end example
@end deffn



@c --- 16.10.2011 --------------------------------------------------------------
@anchor{continuous_freq}
@deffn  {Function} continuous_freq (@var{list})
@deffnx {Function} continuous_freq (@var{list}, @var{m})

The argument of @code{continuous_freq} must be a list of numbers.
Divides the range in intervals and counts how many values
are inside them.  The second argument is optional and either
equals the number of classes we want, @code{10} by default, or
equals a list containing the class limits and the number of
classes we want, or a list containing only the limits.
Argument @var{list} must be a list of (2 or 3) real numbers.
If sample values are all equal, this function returns only
one class of amplitude 2.

Examples:

Optional argument indicates the number of classes we want.
The first list in the output contains the interval limits, and
the second the corresponding counts: there are 16 digits inside 
the interval @code{[0, 1.8]}, 24 digits in @code{(1.8, 3.6]}, and so on.

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
(%i3) continuous_freq (s1, 5);
(%o3) [[0, 1.8, 3.6, 5.4, 7.2, 9.0], [16, 24, 18, 17, 25]]
@end example

Optional argument indicates we want 7 classes with limits
@code{-2} and @code{12}:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
(%i3) continuous_freq (s1, [-2,12,7]);
(%o3) [[- 2, 0, 2, 4, 6, 8, 10, 12], [8, 20, 22, 17, 20, 13, 0]]
@end example

Optional argument indicates we want the default number of classes with limits
@code{-2} and @code{12}:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
(%i3) continuous_freq (s1, [-2,12]);
@group
                3  4  11  18     32  39  46  53
(%o3)  [[- 2, - -, -, --, --, 5, --, --, --, --, 12], 
                5  5  5   5      5   5   5   5
               [0, 8, 20, 12, 18, 9, 8, 25, 0, 0]]
@end group
@end example
@end deffn

@c -----------------------------------------------------------------------------
@anchor{discrete_freq}
@deffn {Function} discrete_freq (@var{list})

Counts absolute frequencies in discrete samples, both numeric and categorical.
Its unique argument is a list,

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
(%i3) discrete_freq (s1);
@group
(%o3) [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 
                             [8, 8, 12, 12, 10, 8, 9, 8, 12, 13]]
@end group
@end example

The first list gives the sample values and the second their absolute 
frequencies.  Commands @code{? col} and @code{? transpose} should help you to 
understand the last input.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{subsample}
@deffn  {Function} subsample (@var{data_matrix}, @var{predicate_function})
@deffnx {Function} subsample (@var{data_matrix}, @var{predicate_function}, @var{col_num1}, @var{col_num2}, @dots{})

This is a sort of variant of the Maxima @code{submatrix} function.  The first 
argument is the data matrix, the second is a predicate function and optional 
additional arguments are the numbers of the columns to be taken.  Its behaviour 
is better understood with examples.

These are multivariate records in which the wind speed in the first 
meteorological station were greater than 18.  See that in the lambda expression 
the @var{i}-th component is refered to as @code{v[i]}.

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
(%i3) subsample (s2, lambda([v], v[1] > 18));
@group
              [ 19.38  15.37  15.12  23.09  25.25 ]
              [                                   ]
              [ 18.29  18.66  19.08  26.08  27.63 ]
(%o3)         [                                   ]
              [ 20.25  21.46  19.95  27.71  23.38 ]
              [                                   ]
              [ 18.79  18.96  14.46  26.38  21.84 ]
@end group
@end example

In the following example, we request only the first, second and fifth 
components of those records with wind speeds greater or equal than 16 in 
station number 1 and less than 25 knots in station number 4.  The sample
contains only data from stations 1, 2 and 5.  In this case, the predicate 
function is defined as an ordinary Maxima function.

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
(%i3) g(x):= x[1] >= 16 and x[4] < 25$
(%i4) subsample (s2, g, 1, 2, 5);
@group
                     [ 19.38  15.37  25.25 ]
                     [                     ]
                     [ 17.33  14.67  19.58 ]
(%o4)                [                     ]
                     [ 16.92  13.21  21.21 ]
                     [                     ]
                     [ 17.25  18.46  23.87 ]
@end group
@end example

Here is an example with the categorical variables of @code{biomed.data}.
We want the records corresponding to those patients in group @code{B}
who are older than 38 years.

@example
(%i1) load (descriptive)$
(%i2) s3 : read_matrix (file_search ("biomed.data"))$
(%i3) h(u):= u[1] = B and u[2] > 38 $
(%i4) subsample (s3, h);
@group
                [ B  39  28.0  102.3  17.1  146 ]
                [                               ]
                [ B  39  21.0  92.4   10.3  197 ]
                [                               ]
                [ B  39  23.0  111.5  10.0  133 ]
                [                               ]
                [ B  39  26.0  92.6   12.3  196 ]
(%o4)           [                               ]
                [ B  39  25.0  98.7   10.0  174 ]
                [                               ]
                [ B  39  21.0  93.2   5.9   181 ]
                [                               ]
                [ B  39  18.0  95.0   11.3  66  ]
                [                               ]
                [ B  39  39.0  88.5   7.6   168 ]
@end group
@end example

Probably, the statistical analysis will involve only the blood measures,

@example
(%i1) load (descriptive)$
(%i2) s3 : read_matrix (file_search ("biomed.data"))$
(%i3) subsample (s3, lambda([v], v[1] = B and v[2] > 38),
                 3, 4, 5, 6);
@group
                   [ 28.0  102.3  17.1  146 ]
                   [                        ]
                   [ 21.0  92.4   10.3  197 ]
                   [                        ]
                   [ 23.0  111.5  10.0  133 ]
                   [                        ]
                   [ 26.0  92.6   12.3  196 ]
(%o3)              [                        ]
                   [ 25.0  98.7   10.0  174 ]
                   [                        ]
                   [ 21.0  93.2   5.9   181 ]
                   [                        ]
                   [ 18.0  95.0   11.3  66  ]
                   [                        ]
                   [ 39.0  88.5   7.6   168 ]
@end group
@end example

This is the multivariate mean of @code{s3},

@example
(%i1) load (descriptive)$
(%i2) s3 : read_matrix (file_search ("biomed.data"))$
(%i3) mean (s3);
@group
       65 B + 35 A  317          6 NA + 8144.999999999999
(%o3) [-----------, ---, 87.178, ------------------------, 
           100      10                     100
                                                    3 NA + 19587
                                            18.123, ------------]
                                                        100
@end group
@end example

Here, the first component is meaningless, since @code{A} and @code{B} are 
categorical, the second component is the mean age of individuals in rational 
form, and the fourth and last values exhibit some strange behaviour.  This is 
because symbol @code{NA} is used here to indicate @var{non available} data, 
and the two means are nonsense.  A possible solution would be to take out from 
the matrix those rows with @code{NA} symbols, although this deserves some 
loss of information.

@example
(%i1) load (descriptive)$
(%i2) s3 : read_matrix (file_search ("biomed.data"))$
(%i3) g(v):= v[4] # NA and v[6] # NA $
(%i4) mean (subsample (s3, g, 3, 4, 5, 6));
@group
(%o4) [79.4923076923077, 86.2032967032967, 16.93186813186813, 
                                                            2514
                                                            ----]
                                                             13
@end group
@end example
@end deffn

@c -----------------------------------------------------------------------------
@need 1400
@node Functions and Variables for descriptive statistics, Functions and Variables for specific multivariate descriptive statistics, Functions and Variables for data manipulation, Package descriptive
@section Functions and Variables for descriptive statistics
@c -----------------------------------------------------------------------------

@c -----------------------------------------------------------------------------
@anchor{mean}
@deffn  {Function} mean (@var{list})
@deffnx {Function} mean (@var{matrix})

This is the sample mean, defined as
@ifnottex
@example
                       n
                     ====
             _   1   \
             x = -    >    x
                 n   /      i
                     ====
                     i = 1
@end example
@end ifnottex
@tex
$${\bar{x}={1\over{n}}{\sum_{i=1}^{n}{x_{i}}}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) mean (s1);
                               471
(%o3)                          ---
                               100
@end group
@group
(%i4) %, numer;
(%o4)                         4.71
@end group
(%i5) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i6) mean (s2);
(%o6)     [9.9485, 10.1607, 10.8685, 15.7166, 14.8441]
@end group
@end example
@end deffn

@c -----------------------------------------------------------------------------
@anchor{var}
@deffn  {Function} var (@var{list})
@deffnx {Function} var (@var{matrix})

This is the sample variance, defined as
@ifnottex
@example
@group
                     n
                   ====
           2   1   \          _ 2
          s  = -    >    (x - x)
               n   /       i
                   ====
                   i = 1
@end group
@end example
@end ifnottex
@tex
$${{1}\over{n}}{\sum_{i=1}^{n}{(x_{i}-\bar{x})^2}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) var (s1), numer;
(%o3)                   8.425899999999999
@end group
@end example

See also function @code{var1}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{var1}
@deffn  {Function} var1 (@var{list})
@deffnx {Function} var1 (@var{matrix})

This is the sample variance, defined as
@ifnottex
@example
@group
                     n
                   ====
               1   \          _ 2
              ---   >    (x - x)
              n-1  /       i
                   ====
                   i = 1
@end group
@end example
@end ifnottex
@tex
$${{1\over{n-1}}{\sum_{i=1}^{n}{(x_{i}-\bar{x})^2}}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) var1 (s1), numer;
(%o3)                    8.5110101010101
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) var1 (s2);
(%o5) [17.39586540404041, 15.13912778787879, 15.63204924242424, 
                            32.50152569696971, 24.66977392929294]
@end group
@end example

See also function @code{var}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{std}
@deffn  {Function} std (@var{list})
@deffnx {Function} std (@var{matrix})

This is the the square root of function @code{var}, the variance with 
denominator @math{n}.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) std (s1), numer;
(%o3)                   2.902740084816414
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) std (s2);
(%o5) [4.149928523480858, 3.871399812729241, 3.933920277534866, 
                            5.672434260526957, 4.941970881136392]
@end group
@end example

See also functions @code{var} and @code{std1}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{std1}
@deffn  {Function} std1 (@var{list})
@deffnx {Function} std1 (@var{matrix})

This is the the square root of function @code{var1}, the variance with 
denominator @math{n-1}.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) std1 (s1), numer;
(%o3)                   2.917363553109228
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) std1 (s2);
(%o5) [4.170835096721089, 3.89090320978032, 3.953738641137555, 
                            5.701010936401517, 4.966867617451963]
@end group
@end example

See also functions @code{var1} and @code{std}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{noncentral_moment}
@deffn  {Function} noncentral_moment (@var{list}, @var{k})
@deffnx {Function} noncentral_moment (@var{matrix}, @var{k})

The non central moment of order @math{k}, defined as
@ifnottex
@example
@group
                       n
                     ====
                 1   \      k
                 -    >    x
                 n   /      i
                     ====
                     i = 1
@end group
@end example
@end ifnottex
@tex
$${{1\over{n}}{\sum_{i=1}^{n}{x_{i}^k}}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) noncentral_moment (s1, 1), numer; /* the mean */
(%o3)                         4.71
@end group
@group
(%i5) s2 : read_matrix (file_search ("wind.data"))$
@end group
@group
(%i6) noncentral_moment (s2, 5);
(%o6) [319793.8724761505, 320532.1923892463,
      391249.5621381556, 2502278.205988911, 1691881.797742255]
@end group
@end example

See also function @code{central_moment}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{central_moment}
@deffn  {Function} central_moment (@var{list}, @var{k})
@deffnx {Function} central_moment (@var{matrix}, @var{k})

The central moment of order @math{k}, defined as
@ifnottex
@example
@group
                    n
                  ====
              1   \          _ k
              -    >    (x - x)
              n   /       i
                  ====
                  i = 1
@end group
@end example
@end ifnottex
@tex
$${{1\over{n}}{\sum_{i=1}^{n}{(x_{i}-\bar{x})^k}}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) central_moment (s1, 2), numer; /* the variance */
(%o3)                   8.425899999999999
@end group
(%i5) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i6) central_moment (s2, 3);
(%o6) [11.29584771375004, 16.97988248298583, 5.626661952750102,
                             37.5986572057918, 25.85981904394192]
@end group
@end example

See also functions @code{central_moment} and @code{mean}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{cv}
@deffn  {Function} cv (@var{list})
@deffnx {Function} cv (@var{matrix})

The variation coefficient is the quotient between the sample standard deviation 
(@code{std}) and the @code{mean},

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) cv (s1), numer;
(%o3)                   .6193977819764815
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) cv (s2);
(%o5) [.4192426091090204, .3829365309260502, 0.363779605385983, 
                            .3627381836021478, .3346021393989506]
@end group
@end example

See also functions @code{std} and @code{mean}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{mini}
@deffn  {Function} mini (@var{list})
@deffnx {Function} mini (@var{matrix})

This is the minimum value of the sample @var{list},

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) mini (s1);
(%o3)                           0
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) mini (s2);
(%o5)             [0.58, 0.5, 2.67, 5.25, 5.17]
@end group
@end example

See also function @code{maxi}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{maxi}
@deffn  {Function} maxi (@var{list})
@deffnx {Function} maxi (@var{matrix})

This is the maximum value of the sample @var{list},

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) maxi (s1);
(%o3)                           9
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) maxi (s2);
(%o5)          [20.25, 21.46, 20.04, 29.63, 27.63]
@end group
@end example

See also function @code{mini}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{range}
@deffn  {Function} range (@var{list})
@deffnx {Function} range (@var{matrix})

The range is the difference between the extreme values.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) range (s1);
(%o3)                           9
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) range (s2);
(%o5)          [19.67, 20.96, 17.37, 24.38, 22.46]
@end group
@end example
@end deffn

@c -----------------------------------------------------------------------------
@anchor{quantile}
@deffn  {Function} quantile (@var{list}, @var{p})
@deffnx {Function} quantile (@var{matrix}, @var{p})

This is the @var{p}-quantile, with @var{p} a number in @math{[0, 1]}, of the 
sample @var{list}.  Although there are several definitions for the sample 
quantile (Hyndman, R. J., Fan, Y. (1996) @var{Sample quantiles in statistical 
packages}.  American Statistician, 50, 361-365), the one based on linear 
interpolation is implemented in package @code{descriptive}.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) /* 1st and 3rd quartiles */
         [quantile (s1, 1/4), quantile (s1, 3/4)], numer;
(%o3)                      [2.0, 7.25]
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) quantile (s2, 1/4);
(%o5)    [7.2575, 7.477500000000001, 7.82, 11.28, 11.48]
@end group
@end example
@end deffn

@c -----------------------------------------------------------------------------
@anchor{median}
@deffn  {Function} median (@var{list})
@deffnx {Function} median (@var{matrix})

Once the sample is ordered, if the sample size is odd the median is the central 
value, otherwise it is the mean of the two central values.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) median (s1);
                                9
(%o3)                           -
                                2
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) median (s2);
(%o5)         [10.06, 9.855, 10.73, 15.48, 14.105]
@end group
@end example

The median is the 1/2-quantile.

See also function @code{quantile}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{qrange}
@deffn  {Function} qrange (@var{list})
@deffnx {Function} qrange (@var{matrix})

The interquartilic range is the difference between the third and first 
quartiles, @code{quantile(@var{list},3/4) - quantile(@var{list},1/4)},

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) qrange (s1);
                               21
(%o3)                          --
                               4
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) qrange (s2);
(%o5) [5.385, 5.572499999999998, 6.022500000000001, 
                            8.729999999999999, 6.649999999999999]
@end group
@end example

See also function @code{quantile}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{mean_deviation}
@deffn  {Function} mean_deviation (@var{list})
@deffnx {Function} mean_deviation (@var{matrix})

The mean deviation, defined as
@ifnottex
@example
@group
                     n
                   ====
               1   \          _
               -    >    |x - x|
               n   /       i
                   ====
                   i = 1
@end group
@end example
@end ifnottex
@tex
$${{1\over{n}}{\sum_{i=1}^{n}{|x_{i}-\bar{x}|}}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) mean_deviation (s1);
                               51
(%o3)                          --
                               20
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) mean_deviation (s2);
(%o5) [3.287959999999999, 3.075342, 3.23907, 4.715664000000001, 
                                               4.028546000000002]
@end group
@end example

See also function @code{mean}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{median_deviation}
@deffn  {Function} median_deviation (@var{list})
@deffnx {Function} median_deviation (@var{matrix})

The median deviation, defined as
@ifnottex
@example
@group
                 n
               ====
           1   \
           -    >    |x - med|
           n   /       i
               ====
               i = 1
@end group
@end example
@end ifnottex
@tex
$${{1\over{n}}{\sum_{i=1}^{n}{|x_{i}-med|}}}$$
@end tex
where @code{med} is the median of @var{list}.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) median_deviation (s1);
                                5
(%o3)                           -
                                2
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) median_deviation (s2);
(%o5)           [2.75, 2.755, 3.08, 4.315, 3.31]
@end group
@end example

See also function @code{mean}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{harmonic_mean}
@deffn  {Function} harmonic_mean (@var{list})
@deffnx {Function} harmonic_mean (@var{matrix})

The harmonic mean, defined as
@ifnottex
@example
@group
                  n
               --------
                n
               ====
               \     1
                >    --
               /     x
               ====   i
               i = 1
@end group
@end example
@end ifnottex
@tex
$${{n}\over{\sum_{i=1}^{n}{{{1}\over{x_{i}}}}}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) y : [5, 7, 2, 5, 9, 5, 6, 4, 9, 2, 4, 2, 5]$
@group
(%i3) harmonic_mean (y), numer;
(%o3)                   3.901858027632205
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) harmonic_mean (s2);
(%o5) [6.948015590052786, 7.391967752360356, 9.055658197151745, 
                            13.44199028193692, 13.01439145898509]
@end group
@end example

See also functions @code{mean} and @code{geometric_mean}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{geometric_mean}
@deffn  {Function} geometric_mean (@var{list})
@deffnx {Function} geometric_mean (@var{matrix})

The geometric mean, defined as
@ifnottex
@example
@group
                 /  n      \ 1/n
                 | /===\   |
                 |  ! !    |
                 |  ! !  x |
                 |  ! !   i|
                 | i = 1   |
                 \         /
@end group
@end example
@end ifnottex
@tex
$$\left(\prod_{i=1}^{n}{x_{i}}\right)^{{{1}\over{n}}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) y : [5, 7, 2, 5, 9, 5, 6, 4, 9, 2, 4, 2, 5]$
@group
(%i3) geometric_mean (y), numer;
(%o3)                   4.454845412337012
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) geometric_mean (s2);
(%o5) [8.82476274347979, 9.22652604739361, 10.0442675714889, 
                            14.61274126349021, 13.96184163444275]
@end group
@end example

See also functions @code{mean} and @code{harmonic_mean}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{kurtois}
@deffn  {Function} kurtosis (@var{list})
@deffnx {Function} kurtosis (@var{matrix})

The kurtosis coefficient, defined as
@ifnottex
@example
@group
                    n
                  ====
            1     \          _ 4
           ----    >    (x - x)  - 3
              4   /       i
           n s    ====
                  i = 1
@end group
@end example
@end ifnottex
@tex
$${{1\over{n s^4}}{\sum_{i=1}^{n}{(x_{i}-\bar{x})^4}}-3}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) kurtosis (s1), numer;
(%o3)                  - 1.273247946514421
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$

(%i5) kurtosis (s2);
(%o5) [- .2715445622195385, 0.119998784429451, 
   - .4275233490482861, - .6405361979019522, - .4952382132352935]
@end example

See also functions @code{mean}, @code{var} and @code{skewness}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{skewness}
@deffn  {Function} skewness (@var{list})
@deffnx {Function} skewness (@var{matrix})

The skewness coefficient, defined as
@ifnottex
@example
@group
                    n
                  ====
            1     \          _ 3
           ----    >    (x - x)
              3   /       i
           n s    ====
                  i = 1
@end group
@end example
@end ifnottex
@tex
$${{1\over{n s^3}}{\sum_{i=1}^{n}{(x_{i}-\bar{x})^3}}}$$
@end tex

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) skewness (s1), numer;
(%o3)                  .009196180476450424
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) skewness (s2);
(%o5) [.1580509020000978, .2926379232061854, .09242174416107717, 
                            .2059984348148687, .2142520248890831]
@end group
@end example

See also functions @code{mean}, @code{var} and @code{kurtosis}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{pearson_skewness}
@deffn  {Function} pearson_skewness (@var{list})
@deffnx {Function} pearson_skewness (@var{matrix})

Pearson's skewness coefficient, defined as 
@ifnottex
@example
@group
                _
             3 (x - med)
             -----------
                  s
@end group
@end example
@end ifnottex
@tex
$${{3\,\left(\bar{x}-med\right)}\over{s}}$$
@end tex
where @var{med} is the median of @var{list}.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) pearson_skewness (s1), numer;
(%o3)                   .2159484029093895
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) pearson_skewness (s2);
(%o5) [- .08019976629211892, .2357036272952649, 
         .1050904062491204, .1245042340592368, .4464181795804519]
@end group
@end example

See also functions @code{mean}, @code{var} and @code{median}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{quarile_skewness}
@deffn  {Function} quartile_skewness (@var{list})
@deffnx {Function} quartile_skewness (@var{matrix})

The quartile skewness coefficient, defined as 
@ifnottex
@example
@group
               c    - 2 c    + c
                3/4      1/2    1/4
               --------------------
                   c    - c
                    3/4    1/4
@end group
@end example
@end ifnottex
@tex
$${{c_{{{3}\over{4}}}-2\,c_{{{1}\over{2}}}+c_{{{1}\over{4}}}}\over{c
 _{{{3}\over{4}}}-c_{{{1}\over{4}}}}}$$
@end tex
where @math{c_p} is the @var{p}-quantile of sample @var{list}.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
@group
(%i3) quartile_skewness (s1), numer;
(%o3)                  .04761904761904762
@end group
(%i4) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i5) quartile_skewness (s2);
(%o5) [- 0.0408542246982353, .1467025572005382, 
       0.0336239103362392, .03780068728522298, .2105263157894735]
@end group
@end example

See also function @code{quantile}.
@end deffn

@c -----------------------------------------------------------------------------
@node Functions and Variables for specific multivariate descriptive statistics, Functions and Variables for statistical graphs, Functions and Variables for descriptive statistics, Package descriptive
@section Functions and Variables for specific multivariate descriptive statistics
@c -----------------------------------------------------------------------------

@c -----------------------------------------------------------------------------
@anchor{cov}
@deffn {Function} cov (@var{matrix})

The covariance matrix of the multivariate sample, defined as
@ifnottex
@example
@group
              n
             ====
          1  \           _        _
      S = -   >    (X  - X) (X  - X)'
          n  /       j        j
             ====
             j = 1
@end group
@end example
@end ifnottex
@c @tex
@c $${S={1\over{n}}{\sum_{j=1}^{n}{\left(X_{j}-\bar{X}\right)\,\left(X_{j}-\bar{X}\right)'}}}$$
@c @end tex
where @math{X_j} is the @math{j}-th row of the sample matrix.

Example:

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
(%i3) fpprintprec : 7$  /* change precision for pretty output */
@group
(%i4) cov (s2);
      [ 17.22191  13.61811  14.37217  19.39624  15.42162 ]
      [                                                  ]
      [ 13.61811  14.98774  13.30448  15.15834  14.9711  ]
      [                                                  ]
(%o4) [ 14.37217  13.30448  15.47573  17.32544  16.18171 ]
      [                                                  ]
      [ 19.39624  15.15834  17.32544  32.17651  20.44685 ]
      [                                                  ]
      [ 15.42162  14.9711   16.18171  20.44685  24.42308 ]
@end group
@end example

See also function @code{cov1}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{cov1}
@deffn {Function} cov1 (@var{matrix})

The covariance matrix of the multivariate sample, defined as
@ifnottex
@example
@group
              n
             ====
         1   \           _        _
   S  = ---   >    (X  - X) (X  - X)'
    1   n-1  /       j        j
             ====
             j = 1
@end group
@end example
@end ifnottex
@c @tex
@c $${{1\over{n-1}}{\sum_{j=1}^{n}{\left(X_{j}-\bar{X}\right)\,\left(X_{j}-\bar{X}\right)'}}}$$
@c @end tex
where @math{X_j} is the @math{j}-th row of the sample matrix.

Example:

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
(%i3) fpprintprec : 7$ /* change precision for pretty output */
@group
(%i4) cov1 (s2);
      [ 17.39587  13.75567  14.51734  19.59216  15.5774  ]
      [                                                  ]
      [ 13.75567  15.13913  13.43887  15.31145  15.12232 ]
      [                                                  ]
(%o4) [ 14.51734  13.43887  15.63205  17.50044  16.34516 ]
      [                                                  ]
      [ 19.59216  15.31145  17.50044  32.50153  20.65338 ]
      [                                                  ]
      [ 15.5774   15.12232  16.34516  20.65338  24.66977 ]
@end group
@end example

See also function @code{cov}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{global_variance}
@deffn  {Function} global_variances (@var{matrix})
@deffnx {Function} global_variances (@var{matrix}, @var{logical_value})
Function @code{global_variances} returns a list of global variance measures:

@itemize @bullet
@item
@var{total variance}: @code{trace(S_1)},
@item
@var{mean variance}: @code{trace(S_1)/p},
@item
@var{generalized variance}: @code{determinant(S_1)},
@item
@var{generalized standard deviation}: @code{sqrt(determinant(S_1))},
@item
@var{efective variance} @code{determinant(S_1)^(1/p)}, (defined in: Pe@~na, D.
(2002) @var{An@'alisis de datos multivariantes}; McGraw-Hill, Madrid.)
@item
@var{efective standard deviation}: @code{determinant(S_1)^(1/(2*p))}.
@end itemize
where @var{p} is the dimension of the multivariate random variable and 
@math{S_1} the covariance matrix returned by @code{cov1}.

Example:

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i3) global_variances (s2);
(%o3) [105.338342060606, 21.06766841212119, 12874.34690469686, 
         113.4651792608501, 6.636590811800795, 2.576158149609762]
@end group
@end example

Function @code{global_variances} has an optional logical argument: 
@code{global_variances (x, true)} tells Maxima that @code{x} is the data matrix, 
making the same as @code{global_variances(x)}.  On the other hand, 
@code{global_variances(x, false)} means that @code{x} is not the data matrix, 
but the covariance matrix, avoiding its recalculation,

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
(%i3) s : cov1 (s2)$
@group
(%i4) global_variances (s, false);
(%o4) [105.338342060606, 21.06766841212119, 12874.34690469686, 
         113.4651792608501, 6.636590811800795, 2.576158149609762]
@end group
@end example

See also @code{cov} and @code{cov1}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{cor}
@deffn  {Function} cor (@var{matrix})
@deffnx {Function} cor (@var{matrix}, @var{logical_value})

The correlation matrix of the multivariate sample.

Example:

@example
(%i1) load (descriptive)$
(%i2) fpprintprec : 7 $
(%i3) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i4) cor (s2);
      [   1.0     .8476339  .8803515  .8239624  .7519506 ]
      [                                                  ]
      [ .8476339    1.0     .8735834  .6902622  0.782502 ]
      [                                                  ]
(%o4) [ .8803515  .8735834    1.0     .7764065  .8323358 ]
      [                                                  ]
      [ .8239624  .6902622  .7764065    1.0     .7293848 ]
      [                                                  ]
      [ .7519506  0.782502  .8323358  .7293848    1.0    ]
@end group
@end example

Function @code{cor} has an optional logical argument: @code{cor(x,true)} tells 
Maxima that @code{x} is the data matrix, making the same as @code{cor(x)}.  On 
the other hand, @code{cor(x,false)} means that @code{x} is not the data matrix, 
but the covariance matrix, avoiding its recalculation,

@example
(%i1) load (descriptive)$
(%i2) fpprintprec : 7 $
(%i3) s2 : read_matrix (file_search ("wind.data"))$
(%i4) s : cov1 (s2)$
@group
(%i5) cor (s, false); /* this is faster */
      [   1.0     .8476339  .8803515  .8239624  .7519506 ]
      [                                                  ]
      [ .8476339    1.0     .8735834  .6902622  0.782502 ]
      [                                                  ]
(%o5) [ .8803515  .8735834    1.0     .7764065  .8323358 ]
      [                                                  ]
      [ .8239624  .6902622  .7764065    1.0     .7293848 ]
      [                                                  ]
      [ .7519506  0.782502  .8323358  .7293848    1.0    ]
@end group
@end example

See also @code{cov} and @code{cov1}.
@end deffn

@c -----------------------------------------------------------------------------
@anchor{list_correlations}
@deffn  {Function} list_correlations (@var{matrix})
@deffnx {Function} list_correlations (@var{matrix}, @var{logical_value})

Function @code{list_correlations} returns a list of correlation measures:

@itemize @bullet

@item
@var{precision matrix}: the inverse of the covariance matrix @math{S_1},
@ifnottex
@example
@group
       -1     ij
      S   = (s  )             
       1         i,j = 1,2,...,p
@end group
@end example
@end ifnottex
@tex
$${S_{1}^{-1}}={\left(s^{ij}\right)_{i,j=1,2,\ldots, p}}$$
@end tex

@item
@var{multiple correlation vector}:  @math{(R_1^2, R_2^2, ..., R_p^2)}, with 
@ifnottex
@example
@group
       2          1
      R  = 1 - -------
       i        ii
               s   s
                    ii
@end group
@end example
@end ifnottex
@tex
$${R_{i}^{2}}={1-{{1}\over{s^{ii}s_{ii}}}}$$
@end tex
being an indicator of the goodness of fit of the linear multivariate regression 
model on @math{X_i} when the rest of variables are used as regressors.

@item
@var{partial correlation matrix}: with element @math{(i, j)} being
@ifnottex
@example
@group
                         ij
                        s
      r        = - ------------
       ij.rest     / ii  jj\ 1/2
                   |s   s  |
                   \       /
@end group
@end example
@end ifnottex
@tex
$${r_{ij.rest}}={-{{s^{ij}}\over \sqrt{s^{ii}s^{jj}}}}$$
@end tex

@end itemize

Example:

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
(%i3) z : list_correlations (s2)$
(%i4) fpprintprec : 5$ /* for pretty output */
@group
(%i5) z[1];  /* precision matrix */
      [  .38486   - .13856   - .15626   - .10239    .031179  ]
      [                                                      ]
      [ - .13856   .34107    - .15233    .038447   - .052842 ]
      [                                                      ]
(%o5) [ - .15626  - .15233    .47296    - .024816  - .10054  ]
      [                                                      ]
      [ - .10239   .038447   - .024816   .10937    - .034033 ]
      [                                                      ]
      [ .031179   - .052842  - .10054   - .034033   .14834   ]
@end group
@group
(%i6) z[2];  /* multiple correlation vector */
(%o6)      [.85063, .80634, .86474, .71867, .72675]
@end group
@group
(%i7) z[3];  /* partial correlation matrix */
      [  - 1.0     .38244   .36627   .49908   - .13049 ]
      [                                                ]
      [  .38244    - 1.0    .37927  - .19907   .23492  ]
      [                                                ]
(%o7) [  .36627    .37927   - 1.0    .10911    .37956  ]
      [                                                ]
      [  .49908   - .19907  .10911   - 1.0     .26719  ]
      [                                                ]
      [ - .13049   .23492   .37956   .26719    - 1.0   ]
@end group
@end example

Function @code{list_correlations} also has an optional logical argument: 
@code{list_correlations(x,true)} tells Maxima that @code{x} is the data matrix, 
making the same as @code{list_correlations(x)}.  On the other hand, 
@code{list_correlations(x,false)} means that @code{x} is not the data matrix, 
but the covariance matrix, avoiding its recalculation.

See also @code{cov} and @code{cov1}.
@end deffn

@c -----------------------------------------------------------------------------
@node Functions and Variables for statistical graphs,  , Functions and Variables for specific multivariate descriptive statistics, Package descriptive
@section Functions and Variables for statistical graphs
@c -----------------------------------------------------------------------------

@c --- 16.10.2011 --------------------------------------------------------------
@anchor{barsplot}
@anchor{barsplot_description}
@deffn  {Function} barsplot (@var{data1}, @var{data2}, @dots{}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} barsplot_description (@dots{})

Plots bars diagrams for discrete statistical variables, both for one or
multiple samples.

@var{data} can be a list of outcomes representing one sample, or a matrix of
@var{m} rows and @var{n} columns, representing @var{n} samples of size @var{m}
each.

Available options are:

@itemize @bullet
@item
@var{box_width} (default, @code{3/4}): relative width of rectangles.  This 
value must be in the range @code{[0,1]}.

@item
@var{grouping} (default, @code{clustered}): indicates how multiple samples are
shown.  Valid values are: @code{clustered} and @code{stacked}.

@item
@var{groups_gap} (default, @code{1}): a positive integer number representing 
the gap between two consecutive groups of bars.

@item
@var{bars_colors} (default, @code{[]}): a list of colors for multiple samples.
When there are more samples than specified colors, the extra necesary colors 
are chosen at random.  See @mxref{color_graphic_option,color} to learn more
about them.

@item
@var{frequency} (default, @code{absolute}): indicates the scale of the
ordinates.  Possible values are:  @code{absolute}, @code{relative}, 
and @code{percent}.

@item
@var{ordering} (default, @code{orderlessp}): possible values are
@code{orderlessp} or @code{ordergreatp}, indicating how statistical outcomes
should be ordered on the @code{x}-axis.

@item
@var{sample_keys} (default, @code{[]}): a list with the strings to be used in
the legend.  When the list length is other than @code{0} or the number of
samples, an error message is returned.

@item
@var{start_at} (default, @code{0}): indicates where the plot begins to be
plotted on the @code{x}-axis.

@item
All global @code{draw} options, except @code{xtics}, which is 
internally assigned by @code{barsplot}.
If you want to set your own values for this option or want to build
complex scenes, make use of @code{barsplot_description}.  See example below.

@item
The following local @code{draw} options: @code{key}, @code{color}, 
@code{fill_color}, @code{fill_density} and @code{line_width}.  See also
@mrefdot{bars}
@end itemize

Function @code{barsplot_description} creates a graphic object
suitable for creating complex scenes, together with other
graphic objects.  There is also a function @code{wxbarsplot} for 
creating embedded histograms in interfaces wxMaxima and iMaxima.

Examples:

Univariate sample in matrix form.  Absolute frequencies.

@example
(%i1) load (descriptive)$
(%i2) m : read_matrix (file_search ("biomed.data"))$
@group
(%i3) barsplot(
        col(m,2),
        title        = "Ages",
        xlabel       = "years",
        box_width    = 1/2,
        fill_density = 3/4)$
@end group
@end example

Two samples of different sizes, with relative frequencies and user declared
colors.

@example
(%i1) load (descriptive)$
(%i2) l1:makelist(random(10),k,1,50)$
(%i3) l2:makelist(random(10),k,1,100)$
@group
(%i4) barsplot(
        l1,l2,
        box_width    = 1,
        fill_density = 1,
        bars_colors  = [black, grey],
        frequency = relative,
        sample_keys = ["A", "B"])$
@end group
@end example

Four non numeric samples of equal size.

@example
(%i1) load (descriptive)$
@group
(%i2) barsplot(
        makelist([Yes, No, Maybe][random(3)+1],k,1,50),
        makelist([Yes, No, Maybe][random(3)+1],k,1,50),
        makelist([Yes, No, Maybe][random(3)+1],k,1,50),
        makelist([Yes, No, Maybe][random(3)+1],k,1,50),
        title  = "Asking for something to four groups",
        ylabel = "# of individuals",
        groups_gap   = 3,
        fill_density = 0.5,
        ordering     = ordergreatp)$
@end group
@end example

Stacked bars.

@example
(%i1) load (descriptive)$
@group
(%i2) barsplot(
        makelist([Yes, No, Maybe][random(3)+1],k,1,50),
        makelist([Yes, No, Maybe][random(3)+1],k,1,50),
        makelist([Yes, No, Maybe][random(3)+1],k,1,50),
        makelist([Yes, No, Maybe][random(3)+1],k,1,50),
        title  = "Asking for something to four groups",
        ylabel = "# of individuals",
        grouping     = stacked,
        fill_density = 0.5,
        ordering     = ordergreatp)$
@end group
@end example

@code{barsplot} in a multiplot context.

@example
(%i1) load (descriptive)$
(%i2) l1:makelist(random(10),k,1,50)$
(%i3) l2:makelist(random(10),k,1,100)$
(%i4) bp1 : 
        barsplot_description(
         l1,
         box_width = 1,
         fill_density = 0.5,
         bars_colors = [blue],
         frequency = relative)$
@group
(%i5) bp2 : 
        barsplot_description(
         l2,
         box_width = 1,
         fill_density = 0.5,
         bars_colors = [red],
         frequency = relative)$
@end group
(%i6) draw(gr2d(bp1), gr2d(bp2))$
@end example

For bars diagrams related options, see @mref{bars} of package @code{draw}.
See also functions @mref{histogram} and @mrefdot{piechart}
@end deffn

@c --- 16.10.2011 --------------------------------------------------------------
@anchor{boxplot}
@anchor{boxplot_description}
@deffn  {Function} boxplot (@var{data})
@deffnx {Function} boxplot (@var{data}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} boxplot_description (@dots{})

This function plots box-and-whishker diagrams.  Argument @var{data} can be a
list, which is not of great interest, since these diagrams are mainly used for
comparing different samples, or a matrix, so it is possible to compare
two or more components of a multivariate statistical variable.
But it is also allowed @var{data} to be a list of samples with 
possible different sample sizes, in fact this is the only function 
in package @code{descriptive} that admits this type of data structure.

Available options are:

@itemize @bullet

@item
@var{box_width} (default, @code{3/4}): relative width of boxes.
This value must be in the range @code{[0,1]}.

@item
@var{box_orientation} (default, @code{vertical}): possible values:
@code{vertical} and @code{horizontal}.

@item
All @code{draw} options, except @code{points_joined}, @code{point_size},
@code{point_type}, @code{xtics}, @code{ytics}, @code{xrange}, and @code{yrange},
which are internally assigned by @code{boxplot}.
If you want to set your own values for this options or want to build
complex scenes, make use of @code{boxplot_description}.

@item
The following local @code{draw} options: @code{key}, @code{color}, 
and @code{line_width}.

@end itemize

Function @code{boxplot_description} creates a graphic object
suitable for creating complex scenes, together with other
graphic objects.  There is also a function @code{wxboxplot} for 
creating embedded histograms in interfaces wxMaxima and iMaxima.

Examples:

Box-and-whishker diagram from a multivariate sample.

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix(file_search("wind.data"))$
@group
(%i3) boxplot(s2,
        box_width  = 0.2,
        title      = "Windspeed in knots",
        xlabel     = "Stations",
        color      = red,
        line_width = 2)$
@end group
@end example

Box-and-whishker diagram from three samples of different sizes.

@example
(%i1) load (descriptive)$
@group
(%i2) A :
       [[6, 4, 6, 2, 4, 8, 6, 4, 6, 4, 3, 2],
        [8, 10, 7, 9, 12, 8, 10],
        [16, 13, 17, 12, 11, 18, 13, 18, 14, 12]]$
@end group
(%i3) boxplot (A, box_orientation = horizontal)$
@end example
@end deffn

@c --- 16.10.2011 --------------------------------------------------------------
@anchor{histogram}
@anchor{histogram_description}
@deffn  {Function} histogram (@var{list})
@deffnx {Function} histogram (@var{list}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} histogram (@var{one_column_matrix})
@deffnx {Function} histogram (@var{one_column_matrix}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} histogram (@var{one_row_matrix})
@deffnx {Function} histogram (@var{one_row_matrix}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} histogram_description (@dots{})

This function plots an histogram from a continuous sample.
Sample data must be stored in a list of numbers or a one dimensional matrix.

Available options are:

@itemize @bullet

@item
@var{nclasses} (default, @code{10}): number of classes of the histogram, or
a list indicating the limits of the classes and the number of them, or
only the limits.

@item
@var{frequency} (default, @code{absolute}): indicates the scale of the
ordinates.  Possible values are:  @code{absolute}, @code{relative}, 
and @code{percent}.

@item
@var{htics} (default, @code{auto}): format of the histogram tics.  Possible
values are: @code{auto}, @code{endpoints}, @code{intervals}, or a list
of labels.

@item
All global @code{draw} options, except @code{xrange}, @code{yrange}, 
and @code{xtics}, which are internally assigned by @code{histogram}.
If you want to set your own values for these options, make use of 
@code{histogram_description}.  See examples bellow.

@item
The following local @code{draw} options: @code{key}, @code{color}, 
@code{fill_color}, @code{fill_density} and @code{line_width}.  See also
@code{bars}.
@end itemize

Function @code{histogram_description} creates a graphic object
suitable for creating complex scenes, together with other
graphic objects.  There is also a function @code{wxhistogram} for 
creating embedded histograms in interfaces wxMaxima and iMaxima.

Examples:

A simple with eight classes:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
(%i3) histogram (
           s1,
           nclasses     = 8,
           title        = "pi digits",
           xlabel       = "digits",
           ylabel       = "Absolute frequency",
           fill_color   = grey,
           fill_density = 0.6)$
@end example

Setting the limits of the histogram to -2 and 12, with 3 classes.
Also, we introduce predefined tics:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
(%i3) histogram (
           s1,
           nclasses     = [-2,12,3],
           htics        = ["A", "B", "C"],
           terminal     = png,
           fill_color   = "#23afa0",
           fill_density = 0.6)$
@end example

We make use of @code{histogram_description} for setting 
the @code{xrange} and adding an explicit curve into the scene:

@example
(%i1) load (descriptive)$
(%i2) ( load("distrib"),
        m: 14, s: 2,
        s2: random_normal(m, s, 1000) ) $
(%i3) draw2d(
        grid   = true,
        xrange = [5, 25],
        histogram_description(
          s2,
          nclasses     = 9,
          frequency    = relative,
          fill_density = 0.5),
        explicit(pdf_normal(x,m,s), x, m - 3*s, m + 3* s))$
@end example
@end deffn

@c --- 16.10.2011 --------------------------------------------------------------
@anchor{piechart}
@deffn  {Function} piechart (@var{list})
@deffnx {Function} piechart (@var{list}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} piechart (@var{one_column_matrix})
@deffnx {Function} piechart (@var{one_column_matrix}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} piechart (@var{one_row_matrix})
@deffnx {Function} piechart (@var{one_row_matrix}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} piechart_description (@dots{})

Similar to @mrefcomma{barsplot} but plots sectors instead of rectangles.

Available options are:

@itemize @bullet

@item
@var{sector_colors} (default, @code{[]}): a list of colors for sectors.
When there are more sectors than specified colors, the extra necesary colors 
are chosen at random.  See @mxref{color_graphic_option,color} to learn more
about them.

@item
@var{pie_center} (default, @code{[0,0]}): diagram's center.

@item
@var{pie_radius} (default, @code{1}): diagram's radius.

@item
All global @code{draw} options, except @code{key}, which is 
internally assigned by @code{piechart}.
If you want to set your own values for this option or want to build
complex scenes, make use of @code{piechart_description}.

@item
The following local @code{draw} options: @code{key}, @code{color}, 
@code{fill_density} and @code{line_width}.  See also
@mrefdot{ellipse}
@end itemize

Function @code{piechart_description} creates a graphic object
suitable for creating complex scenes, together with other
graphic objects.  There is also a function @code{wxpiechart} for 
creating embedded histograms in interfaces wxMaxima and iMaxima.

Example:

@example
(%i1) load (descriptive)$
(%i2) s1 : read_list (file_search ("pidigits.data"))$
(%i3) piechart(
        s1,
        xrange  = [-1.1, 1.3],
        yrange  = [-1.1, 1.1],
        title   = "Digit frequencies in pi")$
@end example

See also function @mrefdot{barsplot}
@end deffn

@c --- 16.10.2011 --------------------------------------------------------------
@anchor{scatterplot}
@deffn  {Function} scatterplot (@var{list})
@deffnx {Function} scatterplot (@var{list}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} scatterplot (@var{matrix})
@deffnx {Function} scatterplot (@var{matrix}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} scatterplot_description (@dots{})

Plots scatter diagrams both for univariate (@var{list}) and multivariate 
(@var{matrix}) samples.

Available options are the same admitted by @mrefdot{histogram}

Function @code{scatterplot_description} creates a graphic object
suitable for creating complex scenes, together with other
graphic objects.  There is also a function @code{wxscatterplot} for 
creating embedded histograms in interfaces wxMaxima and iMaxima.

Examples:

Univariate scatter diagram from a simulated Gaussian sample.

@example
(%i1) load (descriptive)$
(%i2) load (distrib)$
@group
(%i3) scatterplot(
        random_normal(0,1,200),
        xaxis      = true,
        point_size = 2,
        dimensions = [600,150])$
@end group
@end example

Two dimensional scatter plot.

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i3) scatterplot(
       submatrix(s2, 1,2,3),
       title      = "Data from stations #4 and #5",
       point_type = diamant,
       point_size = 2,
       color      = blue)$
@end group
@end example

Three dimensional scatter plot.

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
(%i3) scatterplot(submatrix (s2, 1,2), nclasses=4)$
@end example

Five dimensional scatter plot, with five classes histograms.

@example
(%i1) load (descriptive)$
(%i2) s2 : read_matrix (file_search ("wind.data"))$
@group
(%i3) scatterplot(
        s2,
        nclasses     = 5,
        frequency    = relative,
        fill_color   = blue,
        fill_density = 0.3,
        xtics        = 5)$
@end group
@end example

For plotting isolated or line-joined points in two and three dimensions,
see @mrefdot{points}  See also @mrefdot{histogram}
@end deffn

@c --- 16.10.2011 --------------------------------------------------------------
@anchor{starplot}
@anchor{starplot_description}
@deffn  {Function} starplot (@var{data1}, @var{data2}, @dots{}, @var{option_1}, @var{option_2}, @dots{})
@deffnx {Function} starplot_description (@dots{})

Plots star diagrams for discrete statistical variables, both for one or
multiple samples.

@var{data} can be a list of outcomes representing one sample, or a matrix of
@var{m} rows and @var{n} columns, representing @var{n} samples of size @var{m}
each.

Available options are:

@itemize @bullet
@item
@var{stars_colors} (default, @code{[]}): a list of colors for multiple samples.
When there are more samples than specified colors, the extra necesary colors 
are chosen at random.  See @mxref{color_graphic_option,color} to learn more
about them.

@item
@var{frequency} (default, @code{absolute}): indicates the scale of the
radii.  Possible values are: @code{absolute} and @code{relative}.

@item
@var{ordering} (default, @code{orderlessp}): possible values are
@code{orderlessp} or @code{ordergreatp}, indicating how statistical outcomes
should be ordered.

@item
@var{sample_keys} (default, @code{[]}): a list with the strings to be used in
the legend.  When the list length is other than 0 or the number of samples, an
error message is returned.

@item
@var{star_center} (default, @code{[0,0]}): diagram's center.

@item
@var{star_radius} (default, @code{1}): diagram's radius.

@item
All global @code{draw} options, except @code{points_joined}, @code{point_type}, 
and @code{key}, which are internally assigned by @code{starplot}.
If you want to set your own values for this options or want to build
complex scenes, make use of @code{starplot_description}.

@item
The following local @code{draw} option: @code{line_width}.
@end itemize

Function @code{starplot_description} creates a graphic object
suitable for creating complex scenes, together with other
graphic objects.  There is also a function @code{wxstarplot} for 
creating embedded histograms in interfaces wxMaxima and iMaxima.

Example:

Plot based on absolute frequencies.  Location and radius defined by the user.

@example
(%i1) load (descriptive)$
(%i2) l1: makelist(random(10),k,1,50)$
(%i3) l2: makelist(random(10),k,1,200)$
@group
(%i4) starplot(
        l1, l2,
        stars_colors = [blue,red],
        sample_keys = ["1st sample", "2nd sample"],
        star_center = [1,2],
        star_radius = 4,
        proportional_axes = xy,
        line_width = 2 ) $ 
@end group
@end example
@end deffn

@c --- 16.10.2011 --------------------------------------------------------------
@anchor{stemplot}
@deffn  {Function} stemplot (@var{data})
@deffnx {Function} stemplot (@var{data}, @var{option})

Plots stem and leaf diagrams.  Unique available option is:

@itemize @bullet

@item
@var{leaf_unit} (default, @code{1}): indicates the unit of the leaves;
must be a power of @code{10}.
@end itemize

Example:

@example
(%i1) load (descriptive)$
(%i2) load(distrib)$
(%i3) stemplot(
        random_normal(15, 6, 100),
        leaf_unit = 0.1);
-5|4
 0|37
 1|7
 3|6
 4|4
 5|4
 6|57
 7|0149
 8|3
 9|1334588
10|07888
11|01144467789
12|12566889
13|24778
14|047
15|223458
16|4
17|11557
18|000247
19|4467799
20|00
21|1
22|2335
23|01457
24|12356
25|455
27|79
key: 6|3 =  6.3
(%o3)                  done
@end example
@end deffn

@c --- End of file descriptive.de.texi -----------------------------------------

