# dummy.coef

##### Extract Coefficients in Original Coding

This extracts coefficients in terms of the original levels of the coefficients rather than the coded variables.

- Keywords
- models

##### Usage

`dummy.coef(object, …)`# S3 method for lm
dummy.coef(object, use.na = FALSE, …)

# S3 method for aovlist
dummy.coef(object, use.na = FALSE, …)

##### Arguments

- object
a linear model fit.

- use.na
logical flag for coefficients in a singular model. If

`use.na`

is true, undetermined coefficients will be missing; if false they will get one possible value.- …
arguments passed to or from other methods.

##### Details

A fitted linear model has coefficients for the contrasts of the factor
terms, usually one less in number than the number of levels. This
function re-expresses the coefficients in the original coding; as the
coefficients will have been fitted in the reduced basis, any implied
constraints (e.g., zero sum for `contr.helmert`

or `contr.sum`

)
will be respected. There will be little point in using
`dummy.coef`

for `contr.treatment`

contrasts, as the missing
coefficients are by definition zero.

The method used has some limitations, and will give incomplete results
for terms such as `poly(x, 2)`

. However, it is adequate for
its main purpose, `aov`

models.

##### Value

A list giving for each term the values of the coefficients. For a
multistratum `aov`

model, such a list for each stratum.

##### Warning

This function is intended for human inspection of the output: it should not be used for calculations. Use coded variables for all calculations.

The results differ from S for singular values, where S can be incorrect.

##### See Also

##### Examples

`library(stats)`

```
# NOT RUN {
options(contrasts = c("contr.helmert", "contr.poly"))
## From Venables and Ripley (2002) p.165.
npk.aov <- aov(yield ~ block + N*P*K, npk)
dummy.coef(npk.aov)
npk.aovE <- aov(yield ~ N*P*K + Error(block), npk)
dummy.coef(npk.aovE)
# }
```

*Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0*