pmodel.response: A function to extract the model.response

In plm: Linear Models for Panel Data

A function to extract the model.response

Description

pmodel.response has several methods to conveniently extract the response of several objects.

Usage

pmodel.response(object, ...)

## S3 method for class 'plm'
pmodel.response(object, ...)

## S3 method for class 'data.frame'
pmodel.response(object, ...)

## S3 method for class 'formula'
pmodel.response(object, data, ...)

Arguments

object	an object of class "plm", or a formula of class "Formula",
...	further arguments.
data	a data.frame

Details

The model response is extracted from a pdata.frame (where the response must reside in the first column; this is the case for a model frame), a Formula + data (being a model frame) or a plm object, and the transformation specified by effect and model is applied to it.
Constructing the model frame first ensures proper NA handling and the response being placed in the first column, see also Examples for usage.

Value

A pseries except if model responses' of a "between" or "fd" model as these models "compress" the data (the number of observations used in estimation is smaller than the original data due to the specific transformation). A numeric is returned for the "between" and "fd" model.

Author(s)

Yves Croissant

See Also

plm's model.matrix() for (transformed) model matrix and the corresponding model.frame() method to construct a model frame.

Examples

# First, make a pdata.frame
data("Grunfeld", package = "plm")
pGrunfeld <- pdata.frame(Grunfeld)

# then make a model frame from a Formula and a pdata.frame
form <- inv ~ value + capital
mf <- model.frame(pGrunfeld, form)

# retrieve (transformed) response directly from model frame
resp_mf <- pmodel.response(mf, model = "within", effect = "individual")

# retrieve (transformed) response from a plm object, i.e., an estimated model
fe_model <- plm(form, data = pGrunfeld, model = "within")
pmodel.response(fe_model)

# same as constructed before
all.equal(resp_mf, pmodel.response(fe_model), check.attributes = FALSE) # TRUE

plm documentation built on June 22, 2024, 6:54 p.m.