m2loglik.normexp            package:limma            R Documentation

_N_o_r_m_a_l + _E_x_p_o_n_e_n_t_i_a_l _L_o_g-_L_i_k_e_l_i_h_o_o_d

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

     Marginal log-likelihood of foreground values for normal +
     exponential model and its derivatives. This function is called by
     'backgroundCorrect' and is not normally called directly by the
     user.

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

     m2loglik.normexp(theta,foreground,background=NULL,background.matrix=NULL)
     sumloglik(theta,foreground,background)
     grsumloglik(theta,foreground,background)

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

   theta: numeric vector of parameters

foreground: numeric vector of foreground intensities

background: optional vector of background intensity values

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

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

     Computes minus twice the log-likelihood based on the
     $normal(mu,sigma^2)+exponential(alpha)$ convolution model for the
     foreground intensities. The last two elements of 'theta' are
     $\log(sigma)$ and $\log(alpha)$. The other elements are the vector
     $beta$.

     The vector $beta$ is related to $mu$ through $mu=Xbeta$ if
     $X=$'background.matrix' is non-null. Otherwise
     $mu=beta_1+beta_2$'background' if 'background' is non-null. If
     both 'background' and 'background.matrix' are null, then
     $mu=beta_1$.

     'm2loglik.normexp' is minus-twice the log-likelihood. 'sumloglik'
     is minus the log-likelihood. 'grsumloglik' is the gradient vector
     and 'grloglik' the gradient components for individual spots.

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

     Numeric scalar giving minus twice the log-likelihood

_B_u_g_s:

     There are undocumented arguments in the function 'grloglik'. These
     will disappear soon.

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

     Jeremy Silver and Gordon Smyth

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

     'fit.normexp', 'signal.normexp'

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

