add1 {stats}  R Documentation 
Compute all the single terms in the scope
argument that can be
added to or dropped from the model, fit those models and compute a
table of the changes in fit.
add1(object, scope, ...)
## Default S3 method:
add1(object, scope, scale = 0, test = c("none", "Chisq"),
k = 2, trace = FALSE, ...)
## S3 method for class 'lm'
add1(object, scope, scale = 0, test = c("none", "Chisq", "F"),
x = NULL, k = 2, ...)
## S3 method for class 'glm'
add1(object, scope, scale = 0,
test = c("none", "Rao", "LRT", "Chisq", "F"),
x = NULL, k = 2, ...)
drop1(object, scope, ...)
## Default S3 method:
drop1(object, scope, scale = 0, test = c("none", "Chisq"),
k = 2, trace = FALSE, ...)
## S3 method for class 'lm'
drop1(object, scope, scale = 0, all.cols = TRUE,
test = c("none", "Chisq", "F"), k = 2, ...)
## S3 method for class 'glm'
drop1(object, scope, scale = 0,
test = c("none", "Rao", "LRT", "Chisq", "F"),
k = 2, ...)
object 
a fitted model object. 
scope 
a formula giving the terms to be considered for adding or dropping. 
scale 
an estimate of the residual mean square to be
used in computing 
test 
should the results include a test statistic relative to the
original model? The F test is only appropriate for 
k 
the penalty constant in AIC / 
trace 
if 
x 
a model matrix containing columns for the fitted model and all
terms in the upper scope. Useful if 
all.cols 
(Provided for compatibility with S.) Logical to specify
whether all columns of the design matrix should be used. If

... 
further arguments passed to or from other methods. 
For drop1
methods, a missing scope
is taken to be all
terms in the model. The hierarchy is respected when considering terms
to be added or dropped: all main effects contained in a secondorder
interaction must remain, and so on.
In a scope
formula .
means ‘what is already there’.
The methods for lm
and glm
are more
efficient in that they do not recompute the model matrix and call the
fit
methods directly.
The default output table gives AIC, defined as minus twice log
likelihood plus 2p
where p
is the rank of the model (the
number of effective parameters). This is only defined up to an
additive constant (like loglikelihoods). For linear Gaussian models
with fixed scale, the constant is chosen to give Mallows' C_p
,
RSS/scale + 2p  n
. Where C_p
is used,
the column is labelled as Cp
rather than AIC
.
The F tests for the "glm"
methods are based on analysis of
deviance tests, so if the dispersion is estimated it is based on the
residual deviance, unlike the F tests of anova.glm
.
An object of class "anova"
summarizing the differences in fit
between the models.
The model fitting must apply the models to the same dataset. Most
methods will attempt to use a subset of the data with no missing
values for any of the variables if na.action = na.omit
, but
this may give biased results. Only use these functions with data
containing missing values with great care.
The default methods make calls to the function nobs
to
check that the number of observations involved in the fitting process
remained unchanged.
These are not fully equivalent to the functions in S. There is no
keep
argument, and the methods used are not quite so
computationally efficient.
Their authors' definitions of Mallows' C_p
and Akaike's AIC
are used, not those of the authors of the models chapter of S.
The design was inspired by the S functions of the same names described in Chambers (1992).
Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
step
, aov
, lm
,
extractAIC
, anova
require(graphics); require(utils)
## following example(swiss)
lm1 < lm(Fertility ~ ., data = swiss)
add1(lm1, ~ I(Education^2) + .^2)
drop1(lm1, test = "F") # So called 'type II' anova
## following example(glm)
drop1(glm.D93, test = "Chisq")
drop1(glm.D93, test = "F")
add1(glm.D93, scope = ~outcome*treatment, test = "Rao") ## Pearson Chisquare