add1
Add or Drop All Possible Single Terms to a Model
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.
 Keywords
 models
Usage
add1(object, scope, ...)
"add1"(object, scope, scale = 0, test = c("none", "Chisq"), k = 2, trace = FALSE, ...)
"add1"(object, scope, scale = 0, test = c("none", "Chisq", "F"), x = NULL, k = 2, ...)
"add1"(object, scope, scale = 0, test = c("none", "Rao", "LRT", "Chisq", "F"), x = NULL, k = 2, ...)
drop1(object, scope, ...)
"drop1"(object, scope, scale = 0, test = c("none", "Chisq"), k = 2, trace = FALSE, ...)
"drop1"(object, scope, scale = 0, all.cols = TRUE, test = c("none", "Chisq", "F"), k = 2, ...)
"drop1"(object, scope, scale = 0, test = c("none", "Rao", "LRT", "Chisq", "F"), k = 2, ...)
Arguments
 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 $Cp$. Ignored if
0
orNULL
.  test
 should the results include a test statistic relative to the
original model? The F test is only appropriate for
lm
andaov
models or perhaps forglm
fits with estimated dispersion. The $Chisq$ test can be an exact test (lm
models with known scale) or a likelihoodratio test or a test of the reduction in scaled deviance depending on the method. Forglm
fits, you can also choose"LRT"
and"Rao"
for likelihood ratio tests and Rao's efficient score test. The former is synonymous with"Chisq"
(although both have an asymptotic chisquare distribution).  k
 the penalty constant in AIC / $Cp$.
 trace
 if
TRUE
, print out progress reports.  x
 a model matrix containing columns for the fitted model and all
terms in the upper scope. Useful if
add1
is to be called repeatedly. Warning: no checks are done on its validity.  all.cols
 (Provided for compatibility with S.) Logical to specify
whether all columns of the design matrix should be used. If
FALSE
then nonestimable columns are dropped, but the result is not usually statistically meaningful.  ...
 further arguments passed to or from other methods.
Details
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' $Cp$,
$RSS/scale + 2p  n$. Where $Cp$ 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
.
Value

An object of class
"anova"
summarizing the differences in fit
between the models.
Note
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' $Cp$ and Akaike's AIC are used, not those of the authors of the models chapter of S.
Warning
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.
References
Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
See Also
step
, aov
, lm
,
extractAIC
, anova
Examples
library(stats)
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