| 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", "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", "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 Cp. Ignored if 0 or NULL. |
test |
should the results include a test statistic relative to the
original model? The F test is only appropriate for lm and
aov models or perhaps for glm fits with
estimated dispersion.
The Chisq test can be an exact test
(lm models with known scale) or a likelihood-ratio test or a
test of the reduction in scaled deviance depending on the method. |
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. |
all.cols |
(Provided for compatibility with S.) Logical to specify
whether all columns of the design matrix should be used. If
FALSE then non-estimable columns are dropped, but the result
is not usually statistically meaningful. |
... |
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 second-order
interaction must remain, and so on.
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 log-likelihoods). 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.
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.
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.
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
example(step)#-> swiss add1(lm1, ~ I(Education^2) + .^2) drop1(lm1, test="F") # So called 'type II' anova example(glm) drop1(glm.D93, test="Chisq") drop1(glm.D93, test="F")