anovapin                package:OLIN                R Documentation

_O_n_e-_f_a_c_t_o_r_i_a_l _A_N_O_V_A _a_s_s_e_s_s_i_n_g _p_i_n-_d_e_p_e_n_d_e_n_t _b_i_a_s.

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

     This function performs an one-factorial analysis of variance
     assessing  pin-dependent bias  for a single array

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

     anovapin(obj,index)

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

     obj: object of class marrayRaw or marrayNorm

   index: index of array to be tested 

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

     The function 'anovapin' performs a one-factorial ANOVA for objects
     of class marrayRaw or  marrayNorm. The predictor variable is
     the pin index; the response variable is the logged fold-change  
     'M=(log2(Ch2)-log2(Ch1))'.  The null hypothesis is equal 
     'mean(M)' of  groups of spots printed by the same pin i.e. a
     spot's M does not dependent on the pin used from printing the
     spot.  The model formula used is M ~ (pin.index - 1) (without an
     intercept term).

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

     The return value is a list of summary statistics of the fitted 
     model as produced by 'summary.lm'. For example, the squared
     multiple correlation coefficient R-square equals the proportion 
     of the variation of 'M' that can be explained by the variation of
     pin index (based on the chosen ANOVA model.)

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

     Matthias E. Futschik  (<URL:
     http://itb.biologie.hu-berlin.de/~futschik>)

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

     'anova', 'summary.lm'

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

     # CHECK RAW DATA FOR INTENSITY-DEPENDENT BIAS
     data(sw)
     print(anovapin(sw,index=1))

     # CHECK  DATA NORMALISED BY OLIN FOR INTENSITY-DEPENDENT BIAS
     data(sw.olin)
     print(anovapin(sw.olin,index=1))

