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*.

# 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

## Author

Laurent Berge

## 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)