

cor {base}                                   R Documentation

_C_o_r_r_e_l_a_t_i_o_n _a_n_d _C_o_v_a_r_i_a_n_c_e _M_a_t_r_i_c_e_s

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

     Compute the correlation or covariance matrix of the
     columns of `x' and the columns of `y'.

_U_s_a_g_e_:

     cor(x, y=x, use="all.obs")
     cov(x, y=x, use="all.obs")

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

       x: a matrix or data frame.

       y: a matrix or data frame.

     use: a character string giving the method for handling
          missing observations. This must be one of the
          stringss `"all.obs"', `"complete.obs"' or `"pair-
          wise.complete.obs"' (abbreviations are accept-
          able).

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

     If `use' is `"all.obs"', then the presence of missing
     observations will cause the computation to fail.  If
     `use' has the value `"complete.obs"' then missing val-
     ues are handled by casewise deletion.  Finally, if
     `use' has the value `"pairwise.complete.obs"' then the
     correlation between each pair of variables is computed
     using all complete pairs of observations on those vari-
     ables.  This can result in covariance or correlation
     matrices which are not positive semidefinite.

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

     `cov.wt' for weighted covariance computation.

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

     ## Two simple vectors
     cor(1:10,2:11)# == 1

     ## Correlation Matrix of Multivariate sample:
     data(longley)
     (Cl <- cor(longley))
     ## Graphical Correlation Matrix:
     symnum(Cl) # highly correlated

     ##--- Missing value treatment:
     data(swiss)
     C1 <- cov(swiss)
     range(eigen(C1, only=T)$val) # 6.19  1921
     swiss[1,2] <- swiss[7,3] <- swiss[25,5] <- NA # create 3 "missing"

      C2 <- cov(swiss) # Error: missing obs...

     C2 <- cov(swiss, use = "complete")
     range(eigen(C2, only=T)$val) # 6.46  1930
     C3 <- cov(swiss, use = "pairwise")
     range(eigen(C3, only=T)$val) # 6.19  1938

