Pure Error analysis of variance

Description

For a linear model object, finds the sum of squares for lack of fit and the sum of squares for pure error. These are added to the standard anova table to give a test for lack of fit. If there is no pure error, then the regular anova table is returned.

Usage

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### This is a generic function.  

pureErrorAnova(mod)

## S3 method for class 'lm'
pureErrorAnova(mod)

### Methods for other than models for type lm have not been defined.

Arguments

mod

an object of type lm

Details

For regression models with one predictor, say y ~ x, this method fits y ~ x + factor(x) and prints the anova table. With more than one predictor, it computes a random linear combination L of the terms in the mean function and then gives the anova table for update(mod, ~.+factor(L)).

Value

Returns an analsis of variance table.

Author(s)

Sanford Weisberg, sandy@stat.umn.edu

References

Weisberg, S. (2005). Applied Linear Regression, third edition, New York: Wiley, Chapter 5.

See Also

lm

Examples

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x <- c(1,1,1,2,3,3,4,4,4,4)
y <- c(2.55,2.75,2.57,2.40,4.19,4.70,3.81,4.87,2.93,4.52)
m1 <- lm(y~x)
anova(m1)  # ignore pure error
pureErrorAnova(m1)  # include pure error

head(forbes)
m2 <- lm(Lpres~Temp, data=forbes)
pureErrorAnova(m2)  # function does nothing because there is no pure error

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