lv                   package:VGAM                   R Documentation

_L_a_t_e_n_t _V_a_r_i_a_b_l_e_s

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

     Generic function for the _latent variables_ of a model.

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

     lv(object, ...)

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

  object: An object for which the extraction of latent variables is
          meaningful. 

     ...: Other arguments fed into the specific methods function of the
          model. Sometimes they are fed into the methods function for
          'Coef'. 

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

     Latent variables occur in reduced-rank regression models, as well
     as in quadratic and additive ordination models.  For the latter
     two, latent variable values are often called _site scores_ by
     ecologists. Latent variables are linear combinations of the
     explanatory variables.

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

     The value returned depends specifically on the methods function
     invoked.

_N_o_t_e:

     Latent variables are not really applicable to 'vglm'/'vgam'
     models.

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

     Thomas W. Yee

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

     Yee, T. W. and Hastie, T. J. (2003) Reduced-rank vector
     generalized linear models. _Statistical Modelling_, *3*, 15-41.

     Yee, T. W. (2004) A new technique for maximum-likelihood canonical
     Gaussian ordination. _Ecological Monographs_, *74*, 685-701.

     Yee, T. W. (2006) Constrained additive ordination. _Ecology_,
     *87*, 203-213.

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

     'lv.qrrvglm', 'lv.rrvglm', 'lv.cao', 'lvplot'.

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

     ## Not run: 
     data(hspider)
     hspider[,1:6] = scale(hspider[,1:6]) # Standardized environmental vars
     set.seed(123)
     p1 = cao(cbind(Pardlugu, Pardmont, Pardnigr, Pardpull, Zoraspin) ~
              WaterCon + BareSand + FallTwig +
              CoveMoss + CoveHerb + ReflLux,
              family = poissonff, data = hspider,
              Rank = 1, df1.nl = c(Zoraspin=2.5, 3),
              Bestof = 3, Crow1positive = TRUE)

     var(lv(p1))  # Scaled to unit variance  # Scaled to unit variance
     c(lv(p1))    # Estimated site scores
     ## End(Not run)

