distancematrix            package:hopach            R Documentation

_f_u_n_c_t_i_o_n_s _t_o _c_o_m_p_u_t_e _p_a_i_r _w_i_s_e _d_i_s_t_a_n_c_e_s _b_e_t_w_e_e_n _v_e_c_t_o_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     The function 'distancematrix' is applied to a matrix of data to
     compute the pair wise distances between all rows of the matrix.
     The function 'distancevector' is applied to a matrix and a vector
     to compute the pair wise distances between each row of the matrix
     and  the vector. Both functions allow different choices of
     distance metric. The functions 'dissmatrix' and 'dissvector' allow
     one to  convert between a distance matrix and a vector of the
     upper triangle. The  function 'vectmatrix' is used internally.

_U_s_a_g_e:

     distancematrix(X, d, na.rm=TRUE)

     distancevector(X, y, d, na.rm=TRUE)

     dissmatrix(v)

     dissvector(M)

     vectmatrix(index, p)

_A_r_g_u_m_e_n_t_s:

       X: a numeric matrix. Missing values will be ignored if
          na.rm=TRUE.

       y: a numeric vector, possibly a row of X. Missing values will be
          ignoredif na.rm=TRUE.

   na.rm: an indicator of whether or not to remove missing values. If
          na.rm=TRUE (default), then distances are computed over all
          pairwise non-missing values. Else missing values are
          propagated through the distance computation.

       d: character string specifying the metric to be used for
          calculating  dissimilarities between vectors. The currently
          available options are  "cosangle" (cosine angle or uncentered
          correlation distance), "abscosangle"  (absolute cosine angle
          or absolute uncentered correlation distance),  "euclid"
          (Euclidean distance), "abseuclid" (absolute Euclidean
          distance), "cor" (correlation distance), and "abscor"
          (absolute correlation distance). Advanced users can write
          their own distance functions and add these.

       M: a symmetric matrix of pair wise distances.

       v: a vector of pair wise distances corresponding to the upper
          triangle of a distance matrix, stored by rows.

   index: index in a distance vector, like that returned by
          'dissvector'.

       p: number of elements, e.g. the number of rows in a distance
          matrix.

_D_e_t_a_i_l_s:

     For cosine-angle and correlation (and their absolute values) these
     functions return the square root of the usual distance, for
     example as computed by the dist() function. Typically, this
     transformation makes the dissimilarity correspond more closely
     with the norm. Also, the  Euclidean distance is standardized by
     the sample size, so that the values produced are equal to the
     values from the dist() function divided by sqrt(n).

_V_a_l_u_e:

     For 'distancematrix', a matrix of all pair wise distances between
     rows of 'X'. The value in row 'j' and column 'i' is the distance
     between rows 'i' and 'j'. The matrix is symmetric, and can be
     converted to a vector containing the upper triangle using the
     function 'dissvector'. 

     For 'distancevector', a vector of all pair wise distances between
     rows of 'X' and the vector 'y'. Entry 'j' is the distance between
     row 'j' of 'X' and the vector 'y'.

     For 'dissmatrix', the corresponding distance vector. For 
     'dissvector', the corresponding distance matrix. If 'M' has 'p'
     rows (and columns), then 'v' is length 'p*(p-1)/2'.

     For 'vectmatrix', the indices of the row and column of a distance
     matrix corresponding to entry 'index' in the corresponding 
     distance vector.

_W_a_r_n_i_n_g:

     The correlation and absolute correlation distance functions call
     the 'cor' function, and will therefore fail if there are missing
     values in the data and na.rm!=TRUE.

_A_u_t_h_o_r(_s):

     Katherine S. Pollard <kpollard@soe.ucsc.edu> and Mark J. van der
     Laan <laan@stat.berkeley.edu>

_R_e_f_e_r_e_n_c_e_s:

     van der Laan, M.J. and Pollard, K.S. A new algorithm for hybrid
     hierarchical clustering with visualization and the bootstrap.
     Journal of Statistical Planning and Inference, 2003, 117, pp.
     275-303.

     <URL:
     http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/hopach.pdf>

_S_e_e _A_l_s_o:

     'hopach', 'correlationordering', 'disscosangle'

_E_x_a_m_p_l_e_s:

     mydata<-matrix(rnorm(50),nrow=10)
     deuclid<-distancematrix(mydata,d="euclid")
     vdeuclid<-dissvector(deuclid)
     ddaisy<-daisy(mydata)
     vdeuclid
     ddaisy/sqrt(length(mydata[1,]))

     d1<-distancematrix(mydata,d="abscosangle")
     d2<-distancevector(mydata,mydata[1,],d="abscosangle")
     d1[1,]
     d2 #equal to d1[1,]

     d3<-dissvector(d1)
     pair<-vectmatrix(5,10)
     d1[pair[1],pair[2]]
     d3[5]

