IsoRawp               package:IsoGene               R Documentation

_I_s_o_R_a_w_p

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

     The function calculates the raw one-sided and two-sided p-values
     for each test statistic using permutations.

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

     IsoRawp(x, y, niter)

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

       x: numeric vector containing the dose levels

       y: a data frame of the gene expression with Probe IDs as row
          names

   niter: number of permutations to use

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

     The number of permutations to use can be chosen based on the
     number of possible permutations of samples. If the possible number
     is too big, usually >5000 permutations can be sufficient.

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

     A list of components 

raw.p.one: returns the one-sided p-value matrix for the five test
          statisticsin in 6 columns: the first column is the probe ID,
          the second to the last columns contain the raw p-values for
          each test statistic

raw.p.two: returns the two-sided p-value matrix for the five test
          statistics in 6 columns: the first column is the probe ID,
          the second to the last columns contain the raw p-values for
          each test statistic

 rawp.up: returns the one-sided p-value matrix testing increasing
          alternative for the five test statistics in 6 columns: the
          first column is the probe ID, the second to the last columns
          contain the raw p-values for each test statistic

 rawp.dn: returns the one-sided p-value matrix testing decreasing
          alternative for the five test statistics in 6 columns: the
          first column is the probe ID, the second to the last columns
          contain the raw p-values for each test statistic

_N_o_t_e:

     For each gene, the one-sided p-values are calculated from 
     min(p^Up, p^Down) and the  two sided p-values are calculated from 
     min{2 * min(p^Up, p^Down), 1},  where p^Up and p^Down are the
     p-values calculated for each ordered alternative.

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

     Lin et al.

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

     Lin et al. (2007). Microarray Experiments: a Comparis. on of
     Testing Procedures, Multiplicity, and Resampling-Based Inference, 
     Stat. App. in Gen. & Mol. Bio., 6(1), article 26.

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

     'IsoTestBH'

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

     ## Not run: 
       set.seed(1234)
       x <- c(rep(1,3),rep(2,3),rep(3,3))
       y1 <- matrix(rnorm(90, 1,1),10,9)  # 10 genes with no trends
       y2 <- matrix(c(rnorm(30, 1,1), rnorm(30,2,1),
                      rnorm(30,3,1)), 10, 9)  # 10 genes with increasing trends
       y <- data.frame(rbind(y1, y2))  # y needs to be a data frame 
       rp <- IsoRawp(x, y, niter = 1000)
       rp
     ## End(Not run)

