daisy                package:cluster                R Documentation

_D_i_s_s_i_m_i_l_a_r_i_t_y _M_a_t_r_i_x _C_a_l_c_u_l_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Returns a matrix containing all the pairwise dissimilarities
     (distances) between observations in the dataset. The original
     variables may be of mixed types.

_U_s_a_g_e:

     daisy(x, metric = "euclidean", stand = F, type = list())

_A_r_g_u_m_e_n_t_s:

       x: data matrix or dataframe. Dissimilarities will be computed
          between the rows of `x'. Columns of class `numeric' will be
          recognized as interval scaled variables, columns of class
          `factor' will be recognized as nominal variables, and columns
          of class `ordered' will be recognized as ordinal variables.
          Other variable types should be specified with the `type'
          argument. Missing values (NAs) are allowed.

  metric: character string specifying the metric to be used. The
          currently available options are "euclidean" and "manhattan".
          Euclidean distances are root sum-of-squares of differences,
          and manhattan distances are the sum of absolute differences.
          If not all columns of `x' are numeric, then this argument
          will be ignored.

   stand: logical flag: if TRUE, then the measurements in `x' are
          standardized before calculating the dissimilarities.
          Measurements are standardized for each variable (column), by
          subtracting the variable's mean value and dividing by  the
          variable's mean absolute deviation. If not all columns of `x'
          are numeric, then this argument will be ignored.

    type: list containing some (or all) of the types of the variables
          (columns) in `x'. The list may contain the following
          components: `ordratio' (ratio scaled variables to be treated
          as ordinal variables), `logratio' (ratio scaled variables
          that must be logarithmically transformed), `asymm'
          (asymmetric binary variables). Each component's value is a
          vector, containing the names or the numbers of the
          corresponding columns of `x'. Variables not mentioned in the
          `type' list are interpreted as usual (see argument `x').

_D_e_t_a_i_l_s:

     `daisy' is fully described in chapter 1 of Kaufman and Rousseeuw
     (1990). Compared to `dist' whose input must be numeric variables,
     the main feature of `daisy' is its ability to handle other
     variable types as well (e.g. nominal, ordinal, asymmetric binary)
     even when different types occur in the same dataset.

     In the `daisy' algorithm, missing values in a row of x are not
     included in the dissimilarities involving that row. If all
     variables are interval scaled, the metric is "euclidean", and ng
     is the number of columns in which neither row i and j have NAs,
     then the dissimilarity d(i,j) returned is sqrt(ncol(x)/ng) times
     the Euclidean distance between the two vectors of length ng
     shortened to exclude NAs. The rule is similar for the "manhattan"
     metric, except that the coefficient is ncol(x)/ng. If ng is zero,
     the dissimilarity is NA.

     When some variables have a type other than interval scaled, the
     dissimilarity between two rows is the weighted sum of the
     contribution of each variable. The weight becomes zero when that
     variable is missing in either or both rows, or when the variable
     is asymmetric binary and both values are zero. In all other
     situations, the weight of the variable is 1. The contribution of
     nominal or binary variable a to the total dissimilarity is zero if
     both values are different, else it is equal to 1. The contribution
     of other variables is the absolute difference of both values,
     divided by the total range of that variable. Ordinal variables are
     first converted to ranks. If nok is the number of nonzero weights,
     the dissimilarity is multiplied by the factor 1/nok and thus
     ranges between 0 and 1. If nok is zero, the dissimilarity is NA.

_V_a_l_u_e:

     an object of class `"dissimilarity"' containing the
     dissimilarities among the rows of x. This is typically the input
     for the functions `pam', `fanny', `agnes' or `diana'. See
     dissimilarity.object for details.

_B_A_C_K_G_R_O_U_N_D:

     Dissimilarities are used as inputs to cluster analysis and 
     multidimensional scaling. The choice of metric may have a large
     impact.

_R_e_f_e_r_e_n_c_e_s:

     Kaufman, L. and Rousseeuw, P.J. (1990).  Finding Groups in Data:
     An Introduction to Cluster Analysis.  Wiley, New York.

     Struyf, A., Hubert, M. and Rousseeuw, P.J. (1997). Integrating
     Robust  Clustering Techniques in S-PLUS, Computational Statistics
     and Data Analysis, 26, 17-37.

_S_e_e _A_l_s_o:

     `dissimilarity.object', `dist', `pam', `fanny', `clara', `agnes',
     `diana'.

_E_x_a_m_p_l_e_s:

     data(agriculture)
     ## Example 1 in ref
     ## Compute the dissimilarities using Euclidean metric and without
     ## standardization 
     daisy(agriculture, metric = "euclidean", stand = FALSE)

     data(flower)
     ## Example 2 in ref
     daisy(flower, type = list(asymm = 3))
     daisy(flower, type = list(asymm = c(1, 3), ordratio = 7))

