anovaint                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 _i_n_t_e_n_s_i_t_y-_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  intensity-dependent bias  for a single array. The
     predictor variable is the average logged intensity of both
     channels and the response variable is the logged fold-change.

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

     anovaint(obj,index,N=10)

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

     obj: object of class marrayRaw or marrayNorm

   index: index of array to be tested 

       N: number of (intensity) levels for ANOVA

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

     The function 'anovaint' performs a one-factorial ANOVA for objects
     of class marrayRaw or  marrayNorm. The predictor variable is
     the average logged intensity of both channels
     'A=0.5*(log2(Ch1)+log2(Ch2))'. 'Ch1,Ch2' are the fluorescence
     intensities of channel 1 and channel 2, respectively. The response
     variable is the logged fold-change   'M=(log2(Ch2)-log2(Ch1))'.
     The 'A'-scale is divided in 'N' intervals generating 'N' levels of
     factor 'A'. Note that  'N' should divide the total number of spots
      approx. equally. The null hypothesis is the equality of 'mean(M)'
     of the different levels (intervals).  The model formula used is M
     ~ (A - 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
     'A' (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',  'anovaspatial', 'marrayRaw', 'marrayNorm'

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

     # CHECK RAW DATA FOR INTENSITY-DEPENDENT BIAS
     data(sw)
     print(anovaint(sw,index=1,N=10))

     # CHECK  DATA NORMALISED BY OLIN FOR INTENSITY-DEPENDENT BIAS
     data(sw.olin)
     print(anovaint(sw.olin,index=1,N=10))

