comparePred               package:nlme               R Documentation

_C_o_m_p_a_r_e _P_r_e_d_i_c_t_i_o_n_s

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

     Predicted values are obtained at the specified values of `primary'
     for each object. If either `object1' or `object2' have a grouping
     structure (i.e. `getGroups(object)' is not `NULL'), predicted
     values are obtained for each group. When both objects determine
     groups, the group levels must be the same. If other covariates
     besides `primary' are used in the prediction model, their
     group-wise averages (numeric covariates) or most frequent values
     (categorical covariates) are used to obtain the predicted values.
     The original observations are also included in the returned
     object.

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

     comparePred(object1, object2, primary, minimum, maximum, length.out,
     level, ...) 

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

object1,object2: fitted model objects, from which predictions can be
          extracted using the `predict' method.

 primary: an optional one-sided formula specifying the primary
          covariate to be used to generate the augmented predictions.
          By default, if a  covariate can be extracted from the data
          used to generate the objects (using `getCovariate'), it will
          be used as `primary'.

 minimum: an optional lower limit for the primary covariate. Defaults
          to `min(primary)'.

 maximum: an optional upper limit for the primary covariate. Defaults
          to `max(primary)'.

length.out: an optional integer with the number of primary covariate
          values at which to evaluate the predictions. Defaults to 51.

   level: an optional integer specifying the desired prediction level.
          Levels increase from outermost to innermost grouping, with
          level 0 representing the population (fixed effects)
          predictions. Only one level can be specified. Defaults to the
          innermost level.

     ...: some methods for the generic may require additional
          arguments.

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

     a data frame with four columns representing, respectively, the
     values of the primary covariate, the groups (if `object' does not
     have a grouping structure, all elements will be `1'), the
     predicted or observed values, and the type of value in the third
     column: the objects' names are used to classify the predicted
     values and `original' is used for the observed values. The
     returned object inherits from classes `comparePred' and `augPred'.

_N_o_t_e:

     This function is generic; method functions can be written to
     handle specific classes of objects. Classes which already have
     methods for this function include: `gls', `lme', and `lmList'.

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

     Jose Pinheiro and Douglas Bates

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

     `augPred', `getGroups'

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

     data(Orthodont)
     fm1 <- lme(distance ~ age * Sex, data = Orthodont, random = ~ age)
     fm2 <- update(fm1, distance ~ age)
     comparePred(fm1, fm2, length.out = 2)

