fit.normexp              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:

     fit.normexp(foreground,background=NULL,background.matrix=NULL,trace=0,beta.start=NULL)

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

foreground: numeric vector of foreground intensities

background: optional vector of background intensity values

background.matrix: option design matrix for regression on background
          values

   trace: integer value passed to 'optim'. If positive then tracing
          information on the progress of the optimization is given. 
          Higher values give more information.

beta.start: optional numeric vector giving starting values for the
          regression coefficients

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

     Uses Nelder-Mead simplex algorithm to maximize likelihood based on
     $normal(mu,sigma^2)+exponential(alpha)$ convolution model for the
     foreground intensities. The values $mu$ may depend on any
     covariates, for example the observed background values.

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

     A list containing the components 

    beta: numeric vector of estimated regression coefficients

   sigma: numeric scalar giving estimated value of $sigma$

   alpha: numeric scalar giving estimated value of $alpha$

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):

     Gordon Smyth

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

     'signal.normexp', 'optim'

     An overview of normalization and background correction functions
     is given in '4.Normalization'.

_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 <- fit.normexp(f,b)

