anova.lm {stats}  R Documentation 
Compute an analysis of variance table for one or more linear model fits.
## S3 method for class 'lm'
anova(object, ...)
## S3 method for class 'lmlist'
anova(object, ..., scale = 0, test = "F")
object , ... 
objects of class 
test 
a character string specifying the test statistic to be
used. Can be one of 
scale 
numeric. An estimate of the noise variance

Specifying a single object gives a sequential analysis of variance table for that fit. That is, the reductions in the residual sum of squares as each term of the formula is added in turn are given in as the rows of a table, plus the residual sum of squares.
The table will contain F statistics (and P values) comparing the mean square for the row to the residual mean square.
If more than one object is specified, the table has a row for the residual degrees of freedom and sum of squares for each model. For all but the first model, the change in degrees of freedom and sum of squares is also given. (This only make statistical sense if the models are nested.) It is conventional to list the models from smallest to largest, but this is up to the user.
Optionally the table can include test statistics. Normally the
F statistic is most appropriate, which compares the mean square for a
row to the residual sum of squares for the largest model considered.
If scale
is specified chisquared tests can be used. Mallows'
C_p
statistic is the residual sum of squares plus twice the
estimate of \sigma^2
times the residual degrees of freedom.
An object of class "anova"
inheriting from class "data.frame"
.
The comparison between two or more models will only be valid if they
are fitted 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,
and anova.lmlist
will detect this with an error.
Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
The model fitting function lm
, anova
.
drop1
for
socalled ‘type II’ ANOVA where each term is dropped one at a
time respecting their hierarchy.
## sequential table
fit < lm(sr ~ ., data = LifeCycleSavings)
anova(fit)
## same effect via separate models
fit0 < lm(sr ~ 1, data = LifeCycleSavings)
fit1 < update(fit0, . ~ . + pop15)
fit2 < update(fit1, . ~ . + pop75)
fit3 < update(fit2, . ~ . + dpi)
fit4 < update(fit3, . ~ . + ddpi)
anova(fit0, fit1, fit2, fit3, fit4, test = "F")
anova(fit4, fit2, fit0, test = "F") # unconventional order