sample.size.evaluation     package:oneChannelGUI     R Documentation

_T_h_e _f_u_n_c_t_i_o_n _e_x_e_c_u_t_e_s _a_n_d _p_l_o_t_s _r_e_s_u_l_t_s _f_r_o_m _s_s_i_z_e _a_n_d _d_e_l_t_a _f_u_l_c_t_i_o_n _f_r_o_m _t_h_e _s_s_i_z_e _p_a_c_k_a_h_e

_D_e_s_c_r_i_p_t_i_o_n:

     This function represents avisual tool for helping users to
     understand the trade off between sample size and statistical
     power. To know more about see ssize help.

_U_s_a_g_e:

     sample.size.evaluation()

_D_e_t_a_i_l_s:

     Both ssize and delta outputs are calculated using the BH type I
     error correction instead of the Bonferroni used as default in the
     ssize package. Furthermore, instead using the control group
     variance, this implementation uses the common variance described 
     in Wei et al. BMC Genomics. 2004, 5:87

     Main assumptions: A microarray experiment is set up to compare
     gene expressions between one treatment group and one control
     group. Microarray data has been normalized and transformed so that
     the data for each gene is sufficiently close to a  normal
     distribution that a standard 2-sample pooled-variance t-test will
     reliably detect differentially expressed genes.

_A_u_t_h_o_r(_s):

     Raffaele A Calogero

