logLik                 package:stats                 R Documentation

_E_x_t_r_a_c_t _L_o_g-_L_i_k_e_l_i_h_o_o_d

_D_e_s_c_r_i_p_t_i_o_n:

     This function is generic; method functions can be written to
     handle specific classes of objects.  Classes which already have
     methods for this function include: 'glm', 'lm', 'nls', 'Arima' and
     'gls', 'lme' and others in package 'nlme'.

_U_s_a_g_e:

     logLik(object, ...)

     ## S3 method for class 'lm':
     logLik(object, REML = FALSE, ...)

_A_r_g_u_m_e_n_t_s:

  object: any object from which a log-likelihood value, or a
          contribution to a log-likelihood value, can be extracted.

     ...: some methods for this generic function require additional
          arguments.

    REML: an optional logical value.  If 'TRUE' the restricted
          log-likelihood is returned, else, if 'FALSE', the
          log-likelihood is returned.  Defaults to 'FALSE'.

_D_e_t_a_i_l_s:

     For a '"glm"' fit the 'family' does not have to specify how to
     calculate the log-likelihood, so this is based on the family's
     'aic()' function to compute the AIC.  For the 'gaussian', 'Gamma'
     and 'inverse.gaussian' families it assumed that the dispersion of
     the GLM is estimated has been counted as a parameter in the AIC
     value, and for all other families it is assumed that the
     dispersion is known.

     Note that this procedure is not completely accurate for the gamma
     and inverse gaussian families, as the estimate of dispersion used
     is not the MLE.

     For '"lm"' fits it is assumed that the scale has been estimated
     (by maximum likelihood or REML), and all the constants in the
     log-likelihood are included.

_V_a_l_u_e:

     Returns an object, say 'r', of class 'logLik' which is a number
     with attributes, 'attr(r, "df")' (*d*egrees of *f*reedom) giving
     the number of (estimated) parameters in the model. There is a
     simple 'print' method for 'logLik' objects.

     The details depend on the method function used; see the
     appropriate documentation.

_A_u_t_h_o_r(_s):

     Jose Pinheiro and Douglas Bates

_R_e_f_e_r_e_n_c_e_s:

     For 'logLik.lm':

     Harville, D.A. (1974). Bayesian inference for variance components
     using only error contrasts. _Biometrika_, *61*, 383-385.

_S_e_e _A_l_s_o:

     'logLik.gls', 'logLik.lme', in package 'nlme', etc.

_E_x_a_m_p_l_e_s:

     x <- 1:5
     lmx <- lm(x ~ 1)
     logLik(lmx) # using print.logLik() method
     utils::str(logLik(lmx))

     ## lm method
     (fm1 <- lm(rating ~ ., data = attitude))
     logLik(fm1)
     logLik(fm1, REML = TRUE)

     res <- try(utils::data(Orthodont, package="nlme"))
     if(!inherits(res, "try-error")) {
       fm1 <- lm(distance ~ Sex * age, Orthodont)
       print(logLik(fm1))
       print(logLik(fm1, REML = TRUE))
     }

