step                  package:stats                  R Documentation

_C_h_o_o_s_e _a _m_o_d_e_l _b_y _A_I_C _i_n _a _S_t_e_p_w_i_s_e _A_l_g_o_r_i_t_h_m

_D_e_s_c_r_i_p_t_i_o_n:

     Select a formula-based model by AIC.

_U_s_a_g_e:

     step(object, scope, scale = 0,
          direction = c("both", "backward", "forward"), 
          trace = 1, keep = NULL, steps = 1000, k = 2, ...)

_A_r_g_u_m_e_n_t_s:

  object: an object representing a model of an appropriate class
          (mainly '"lm"' and '"glm"'). This is used as the initial
          model in the stepwise search. 

   scope: defines the range of models examined in the stepwise search.
          This should be either a single formula, or a list containing
          components 'upper' and 'lower', both formulae.  See the
          details for how to specify the formulae and how they are
          used. 

   scale: used in the definition of the AIC statistic for selecting the
          models, currently only for 'lm', 'aov' and 'glm' models. 

direction: the mode of stepwise search, can be one of '"both"',
          '"backward"', or '"forward"', with a default of '"both"'.  If
          the 'scope' argument is missing the default for 'direction'
          is '"backward"'. 

   trace: if positive, information is printed during the running of
          'step'. Larger values may give more detailed information. 

    keep: a filter function whose input is a fitted model object and
          the  associated 'AIC' statistic, and whose output is
          arbitrary.  Typically 'keep' will select a subset of the
          components of  the object and return them. The default is not
          to keep anything. 

   steps: the maximum number of steps to be considered.  The default is
          1000 (essentially as many as required).  It is typically used
          to stop the process early. 

       k: the multiple of the number of degrees of freedom used for the
          penalty. Only 'k = 2' gives the genuine AIC: 'k = log(n)' is
          sometimes referred to as BIC or SBC. 

     ...: any additional arguments to 'extractAIC'. 

_D_e_t_a_i_l_s:

     'step' uses 'add1' and 'drop1' repeatedly; it will work for any
     method for which they work, and that is determined by having a
     valid method for 'extractAIC'. When the additive constant can be
     chosen so that AIC is equal to Mallows' Cp, this is done and the
     tables are labelled appropriately.

     The set of models searched is determined by the 'scope' argument.
     The right-hand-side of its 'lower' component is always included in
     the model, and right-hand-side of the model is included in the
     'upper' component.  If 'scope' is a single formula, it specifes
     the 'upper' component, and the 'lower' model is empty.  If 'scope'
     is missing, the initial model is used as the 'upper' model.

     Models specified by 'scope' can be templates to update 'object' as
     used by 'update.formula'.

     There is a potential problem in using 'glm' fits with a variable
     'scale', as in that case the deviance is not simply related to the
     maximized log-likelihood. The '"glm"' method for function
     'extractAIC' makes the appropriate adjustment for a 'gaussian'
     family, but may need to be amended for other cases. (The
     'binomial' and 'poisson' families have fixed 'scale' by default
     and do not correspond to a particular maximum-likelihood problem
     for variable 'scale'.)

_V_a_l_u_e:

     the stepwise-selected model is returned, with up to two additional
     components.  There is an '"anova"' component corresponding to the
     steps taken in the search, as well as a '"keep"' component if the
     'keep=' argument was supplied in the call. The '"Resid. Dev"'
     column of the analysis of deviance table refers to a constant
     minus twice the maximized log likelihood: it will be a deviance
     only in cases where a saturated model is well-defined (thus
     excluding 'lm', 'aov' and 'survreg' fits, for example).

_W_a_r_n_i_n_g:

     The model fitting must apply the models to the same dataset. This
     may be a problem if there are missing values and R's default of
     'na.action = na.omit' is used.  We suggest you remove the missing
     values first.

_N_o_t_e:

     This function differs considerably from the function in S, which
     uses a number of approximations and does not compute the correct
     AIC.

     This is a minimal implementation. Use 'stepAIC' for a wider range
     of object classes.

_A_u_t_h_o_r(_s):

     B. D. Ripley: 'step' is a slightly simplified version of 'stepAIC'
     in package 'MASS' (Venables & Ripley, 2002 and earlier editions).

     The idea of a 'step' function follows that described in Hastie &
     Pregibon (1992); but the implementation in R is more general.

_R_e_f_e_r_e_n_c_e_s:

     Hastie, T. J. and Pregibon, D. (1992) _Generalized linear models._
     Chapter 6 of _Statistical Models in S_ eds J. M. Chambers and T.
     J. Hastie, Wadsworth & Brooks/Cole.

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ New York: Springer (4th ed).

_S_e_e _A_l_s_o:

     'stepAIC', 'add1', 'drop1'

_E_x_a_m_p_l_e_s:

     example(lm)
     step(lm.D9)  

     data(swiss)
     summary(lm1 <- lm(Fertility ~ ., data = swiss))
     slm1 <- step(lm1)
     summary(slm1)
     slm1$anova

