fitted.femlm {FENmlm}

Extracts fitted values from a femlm fit

Description

This function extracts the fitted values from a model estimated with femlm. The fitted values that are returned are the expected predictor.

Usage

## S3 method for class 'femlm'
fitted(object, type = c("response", "link"), ...)

## S3 method for class 'values.femlm'
fitted(object, type = c("response", "link"), ...)

Arguments

object

An object of class femlm. Typically the result of a femlm estimation.

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.

...

Not currently used.

Details

This function returns the expected predictor of a femlm fit. The likelihood functions are detailed in femlm help page.

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)).

Author(s)

Laurent Berge

See Also

femlm, resid.femlm, predict.femlm, summary.femlm, vcov.femlm, getFE.

Examples

# simple estimation on iris data, clustering by "Species"
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)