colmean, colmin, colmax, colrange, colvar, colsd, colprod, colsumpackage:bigmemoryR Documentation

_B_a_s_i_c _s_t_a_t_i_s_t_i_c_s _f_o_r "_b_i_g._m_a_t_r_i_x" _o_b_j_e_c_t_s.

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

     These functions operate on columns of the 'big.matrix'.

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

     colmean(x, cols, na.rm)
     colmin(x, cols, na.rm)
     colmax(x, cols, na.rm)
     colrange(x, cols, na.rm)
     colvar(x, cols, na.rm)
     colsd(x, cols, na.rm)
     colsum(x, cols, na.rm)
     colprod(x, cols, na.rm)

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

       x: a 'big.matrix'.

    cols: a scalar or vector of column(s) to be summarized.

   na.rm: if 'TRUE', remove 'NA' values before summarizing.

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

     These functions essentially apply summary functions to each column
     (or each specified column) of the 'big.matrix' in turn.

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

     For 'colrange', a matrix with two columns and 'length(cols)' rows;
     column 1 contains the minimum, and column 2 contains the maximum
     for that column.  The other functions return vectors of length
     'length(cols)'.

_A_u_t_h_o_r(_s):

     John W. Emerson and Michael J. Kane

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

     'big.matrix'

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

     x <- as.big.matrix(matrix(sample(1:10, 20, replace=TRUE), 5, 4))
     colnames(x) <- c("a", "b", "c", "d")
     x[,]
     mean(x)
     colmean(x)
     colmin(x)
     colmin(x, 1)
     colmax(x)
     colmax(x, "b")
     colsd(x)
     colrange(x)
     range(x)
     colsum(x)
     colprod(x)

