permutest               package:CORREP               R Documentation

_P_e_r_m_u_t_a_t_i_o_n _T_e_s_t _P-_v_a_l_u_e _f_o_r _M_u_l_t_i_v_a_r_a_i_t_e _C_o_r_r_e_l_a_t_i_o_n

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

     This function calculates p-values of the multivariate correlation
     estimator by enumerating all permutations. We recommend using
     Likehood Ratio Test implemented in function cor.LRtest1 if your
     data has moderate to large sample size (>5) The procedure is same
     as those permutation tests for Pearson correlation coefficient or
     other parameters. Since the approximation of null distribution
     requires enumerating all permutations. The computational burden
     increases in $n^2$.

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

     permutest(x, y=NULL, m, G)

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

       x: data matrix, column represents samples (conditions), and row
          represents variables (genes), see example below for format
          information

       y: optional, used when x and y are vectors

       m: number of replicates

       G: number of genes

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

     See manuscript.

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

      PV: P-values of permutation tests

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

     Dongxiao Zhu and Youjuan Li

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

     Zhu, D and Li Y. 2007. Multivariate Correlation Estimator for
     Inferring Functional Relationships from Replicated 'OMICS' data.
     Submitted.

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

     'cor.LRtest1', 'cor.LRtest2', 'cor.test'

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

     library("CORREP")
     library("e1071")
     d0 <- NULL
     ## sample size is set to 5, it takes about a min to finish 
     for(l in 1:5)
     d0 <- rbind(d0, rnorm(100))
     ## data must have row variance of 1 
     d0.std <- apply(d0, 2, function(x) x/sd(x))
     M <- cor.balance(t(d0.std), m = 4, G= 25)
     M.pv <- permutest(t(d0.std), m = 4, G= 25)

