stepAIC package:MASS 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:
Performs stepwise model selection by AIC.
_U_s_a_g_e:
stepAIC(object, scope, scale = 0,
direction = c("both", "backward", "forward"),
trace = 1, keep = NULL, steps = 1000, use.start = FALSE,
k = 2, ...)
_A_r_g_u_m_e_n_t_s:
object: an object representing a model of an appropriate class. 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’ and ‘aov’ models (see
‘extractAIC’ for details).
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
‘stepAIC’. Larger values may give more information on the
fitting process.
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.
use.start: if true the updated fits are done starting at the linear
predictor for the currently selected model. This may speed up
the iterative calculations for ‘glm’ (and other fits), but it
can also slow them down. *Not used* in R.
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’. (None are currently
used.)
_D_e_t_a_i_l_s:
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 specifies
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 ‘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’.)
Where a conventional deviance exists (e.g. for ‘lm’, ‘aov’ and
‘glm’ fits) this is quoted in the analysis of variance table: it
is the _unscaled_ deviance.
_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).
_N_o_t_e:
The model fitting must apply the models to the same dataset. This
may be a problem if there are missing values and an ‘na.action’
other than ‘na.fail’ is used (as is the default in R). We suggest
you remove the missing values first.
_R_e_f_e_r_e_n_c_e_s:
Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
Statistics with S._ Fourth edition. Springer.
_S_e_e _A_l_s_o:
‘addterm’, ‘dropterm’, ‘step’
_E_x_a_m_p_l_e_s:
quine.hi <- aov(log(Days + 2.5) ~ .^4, quine)
quine.nxt <- update(quine.hi, . ~ . - Eth:Sex:Age:Lrn)
quine.stp <- stepAIC(quine.nxt,
scope = list(upper = ~Eth*Sex*Age*Lrn, lower = ~1),
trace = FALSE)
quine.stp$anova
cpus1 <- cpus
attach(cpus)
for(v in names(cpus)[2:7])
cpus1[[v]] <- cut(cpus[[v]], unique(quantile(cpus[[v]])),
include.lowest = TRUE)
detach()
cpus0 <- cpus1[, 2:8] # excludes names, authors' predictions
cpus.samp <- sample(1:209, 100)
cpus.lm <- lm(log10(perf) ~ ., data = cpus1[cpus.samp,2:8])
cpus.lm2 <- stepAIC(cpus.lm, trace = FALSE)
cpus.lm2$anova
example(birthwt)
birthwt.glm <- glm(low ~ ., family = binomial, data = bwt)
birthwt.step <- stepAIC(birthwt.glm, trace = FALSE)
birthwt.step$anova
birthwt.step2 <- stepAIC(birthwt.glm, ~ .^2 + I(scale(age)^2)
+ I(scale(lwt)^2), trace = FALSE)
birthwt.step2$anova
quine.nb <- glm.nb(Days ~ .^4, data = quine)
quine.nb2 <- stepAIC(quine.nb)
quine.nb2$anova