stats (version 3.6.2)

# anova.lm: ANOVA for Linear Model Fits

## Description

Compute an analysis of variance table for one or more linear model fits.

## Usage

# S3 method for lm
anova(object, …)# S3 method for lmlist
anova(object, …, scale = 0, test = "F")

## Arguments

object, …

objects of class lm, usually, a result of a call to lm.

test

a character string specifying the test statistic to be used. Can be one of "F", "Chisq" or "Cp", with partial matching allowed, or NULL for no test.

scale

numeric. An estimate of the noise variance $$\sigma^2$$. If zero this will be estimated from the largest model considered.

## Value

An object of class "anova" inheriting from class "data.frame".

## Warning

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.

## Details

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 chi-squared 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.

## 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.

The model fitting function lm, anova.

drop1 for so-called ‘type II’ anova where each term is dropped one at a time respecting their hierarchy.

## Examples

Run this code
# NOT RUN {
## 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
# }


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