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 the normal +
     exponential convolution model and its derivatives. These functions
     are called by 'normexp.fit' and are not normally called directly
     by the user.

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

     normexp.m2loglik(par,x)
     normexp.m2loglik.saddle(par,x)
     normexp.grad(par,x)

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

     par: numeric vector of parameters

       x: numeric vector of (background corrected) intensities

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

     The parameter vector 'par' holds the normal mean, the normal
     log-standard deviation and the exponential mean.

     'normexp.m2loglik' computes minus twice the log-likelihood, and
     'normexp.grad' it is derivative, based on the
     $normal(mu,sigma^2)+exponential(alpha)$ convolution model for the
     intensities. The elements of 'par' are $mu$, $\log(sigma)$ and
     $\log(alpha)$.

     'normexp.m2loglik' is the saddle-point approximation to the
     log-logelihood, which is generally prefered because it is
     numerically more stable.

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

     'normexp.m2loglik' returns a numeric scalar holding minus-twice
     the log-likelihood. 'normexp.grad' returns a numeric vector
     holding the derivatives with respect to the elements of 'par'.

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

     Jeremy Silver and Gordon Smyth

_R_e_f_e_r_e_n_c_e_s:

     Ritchie, M. E., Silver, J., Oshlack, A., Silver, J., Holmes, M.,
     Diyagama, D., Holloway, A., and Smyth, G. K. (2007). A comparison
     of background correction methods for two-colour microarrays.
     _Bioinformatics_ <URL:
     http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btm412>

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

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

