normexp.fit              package:limma              R Documentation

_F_i_t _N_o_r_m_a_l+_E_x_p _C_o_n_v_o_l_u_t_i_o_n _M_o_d_e_l _t_o _O_b_s_e_r_v_e_d _I_n_t_e_n_s_i_t_i_e_s

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

     Fit normal+exponential convolution model to observed intensities.
     The normal part represents the background and the exponential
     represents the signal intensities. This function is called by
     'backgroundCorrect' and is not normally called directly by the
     user.

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

     normexp.fit(x, trace=FALSE)

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

       x: numeric vector of (background corrected) intensities

   trace: logical, if 'TRUE', tracing information on the progress of
          the optimization is given.

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

     This function uses maximum likelihood estimation to fit a model to
     the foreground and background intensities. The model is a
     $normal(mu,sigma^2)+exponential(alpha)$ convolution model for the
     background corrected intensities.

     This is essentially the same model which is used by
     'bg.correct.rma' in the affy package. The difference is that the
     parameters are estimated by maximum likelihood and that the
     estimated background is subtracted before applying the model-based
     background.

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

     A list containing the components 

     par: numeric vector giving estimated values of $mu$, $\log(sigma)$
          and $\logalpha$

m2loglik: numeric scalar giving minus twice the log-likelihood

convergence: integer code indicating successful convergence or
          otherwise of the optimization. See 'optim'.

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

     Jeremy Silver and Gordon Smyth

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

     'normexp.signal'

     An overview of background correction functions is given in
     '04.Background'.

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

     f <- c(2,3,1,10,3,20,5,6)
     b <- c(2,2,2,2,2,2,2,2)
     out <- normexp.fit(f-b)
     normexp.signal(out$par, x=f-b)

