

augPred(nlme)                                R Documentation

_A_u_g_m_e_n_t_e_d _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'. If `object' has a grouping structure
     (i.e. `getGroups(object)' is not `NULL'), predicted
     values are obtained for each group. If `level' has more
     than one element, predictions are obtained for each
     level of the `max(level)' grouping factor. If other
     covariates besides `primary' are used in the prediction
     model, their average (numeric covariates) or most fre-
     quent value (categorical covariates) are used to obtain
     the predicted values. The original observations are
     also included in the returned object.

_U_s_a_g_e_:

     augPred(object, primary, minimum, maximum, length.out, level, ...)

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

  object: a fitted model object from which predictions can
          be extracted, using a `predict' method.

 primary: an optional one-sided formula specifying the pri-
          mary covariate to be used to generate the aug-
          mented predictions. By default, if a  covariate
          can be extracted from the data used to generate
          `object' (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 predic-
          tions. Defaults to 51.

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

     ...: some methods for the generic may require addi-
          tional arguments.

_V_a_l_u_e_:

     a data frame with four columns representing, respec-
     tively, 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 val-
     ues, and the type of value in the third column: `origi-
     nal' for the observed values and `predicted' (single or
     no grouping factor) or `predict.groupVar' (multiple
     levels of grouping), with `groupVar' replaced by the
     actual grouping variable name (`fixed' is used for pop-
     ulation predictions). The returned object inherits from
     class `augPred'.

_N_o_t_e_:

     This function is generic; method functions can be writ-
     ten 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_:

     `plot.augPred', `getGroups', `predict'

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

     library(nlme)
     data(Orthodont)
     fm1 <- lme(Orthodont)
     augPred(fm1, length.out = 2, level = c(0,1))

