# effects

##### Effects from Fitted Model

Returns (orthogonal) effects from a fitted model, usually a linear
model. This is a generic function, but currently only has a methods for
objects inheriting from classes `"lm"`

and `"glm"`

.

- Keywords
- models, regression

##### Usage

`effects(object, ...)`## S3 method for class 'lm':
effects(object, set.sign = FALSE, \dots)

##### Arguments

- object
- an Robject; typically, the result of a model fitting function
such as
`lm`

. - set.sign
- logical. If
`TRUE`

, the sign of the effects corresponding to coefficients in the model will be set to agree with the signs of the corresponding coefficients, otherwise the sign is arbitrary. - ...
- arguments passed to or from other methods.

##### Details

For a linear model fitted by `lm`

or `aov`

,
the effects are the uncorrelated single-degree-of-freedom values
obtained by projecting the data onto the successive orthogonal
subspaces generated by the QR decomposition during the fitting
process. The first $r$ (the rank of the model) are associated with
coefficients and the remainder span the space of residuals (but are
not associated with particular residuals).

Empty models do not have effects.

##### Value

- A (named) numeric vector of the same length as
`residuals`

, or a matrix if there were multiple responses in the fitted model, in either case of class`"coef"`

.The first $r$ rows are labelled by the corresponding coefficients, and the remaining rows are unlabelled. Note that in rank-deficient models the corresponding coefficients will be in a different order if pivoting occurred.

##### References

Chambers, J. M. and Hastie, T. J. (1992)
*Statistical Models in S.*
Wadsworth & Brooks/Cole.

##### See Also

##### Examples

`library(stats)`

```
y <- c(1:3, 7, 5)
x <- c(1:3, 6:7)
( ee <- effects(lm(y ~ x)) )
c( round(ee - effects(lm(y+10 ~ I(x-3.8))), 3) )
# just the first is different
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*