This function extracts the fitted values from a model estimated with `femlm`

,
`feols`

or `feglm`

. The fitted values that are returned are the *expected predictor*.

## Usage

```
# S3 method for fixest
fitted(object, type = c("response", "link"), na.rm = TRUE, ...)
# S3 method for fixest
fitted.values(object, type = c("response", "link"), na.rm = TRUE, ...)
```

## Arguments

- object
A

`fixest`

object. Obtained using the functions`femlm`

,`feols`

or`feglm`

.- type
Character either equal to

`"response"`

(default) or`"link"`

. If`type="response"`

, then the output is at the level of the response variable, i.e. it is the expected predictor \(E(Y|X)\). If`"link"`

, then the output is at the level of the explanatory variables, i.e. the linear predictor \(X\cdot \beta\).- na.rm
Logical, default is

`TRUE`

. If`FALSE`

the number of observation returned will be the number of observations in the original data set, otherwise it will be the number of observations used in the estimation.- ...
Not currently used.

## Value

It returns a numeric vector of length the number of observations used to estimate the model.

If `type = "response"`

, the value returned is the expected predictor, i.e. the
expected value of the dependent variable for the fitted model: \(E(Y|X)\).
If `type = "link"`

, the value returned is the linear predictor of the fitted model,
that is \(X\cdot \beta\) (remind that \(E(Y|X) = f(X\cdot \beta)\)).

## Details

This function returns the *expected predictor* of a `fixest`

fit. The likelihood functions
are detailed in `femlm`

help page.

## See also

See also the main estimation functions `femlm`

, `feols`

or `feglm`

.
`resid.fixest`

, `predict.fixest`

, `summary.fixest`

, `vcov.fixest`

, `fixef.fixest`

.

## Examples

```
# simple estimation on iris data, using "Species" fixed-effects
res_poisson = femlm(Sepal.Length ~ Sepal.Width + Petal.Length +
Petal.Width | Species, iris)
# we extract the fitted values
y_fitted_poisson = fitted(res_poisson)
# Same estimation but in OLS (Gaussian family)
res_gaussian = femlm(Sepal.Length ~ Sepal.Width + Petal.Length +
Petal.Width | Species, iris, family = "gaussian")
y_fitted_gaussian = fitted(res_gaussian)
# comparison of the fit for the two families
plot(iris$Sepal.Length, y_fitted_poisson)
points(iris$Sepal.Length, y_fitted_gaussian, col = 2, pch = 2)
```