lm.extract: Extract linear regression components

Description

lm.extract fit a linear model and extract coefficients, unscaled covariance matrix, residual variance, fitted values, residuals, degrees of freedom, and leverage and cook's distance for each data point.

Usage

lm.extract(formula, data, na.action = na.exclude)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted on the format response ~ terms.

data

a data set containing the variables in the model.

na.action

a function which indicate what should happend when the data contain NAs. The default is na.exclude (see ?na.fail).

Value

ajt: a named vector of coefficients
res: the residuals
fit: the fitted values
dof: the degrees of freedom
sigma.djt: the residual standard error
Ajt.us: a named unscaled variance-covariance matrix
leverage: the estimated leverage for each data point. I.e. a vector containing the diagonal of the 'hat' matrix (see lm.influence?)
cook: the estimated Cook's distance for each data point (see cooks.distance?)

Details

lm.extract works through calls to lm, residuals, predict, df.residuals, deviance, vcov, lm.influence and cooks.distance. Consult these functions for further details. The function was written for internal use with lmf, but can be executed as a standalone.

Examples

Run this code

#Simulated data
xx <- rnorm(n = 100, mean = 10, sd = 2)
yy <- xx + 10 + rnorm(n = 100, 0, 2)
#Extract linear model components
extract <- lm.extract(formula = yy ~ xx, data = data.frame(xx = xx, yy = yy))
str(extract)
#Plot the xx-yy relation
plot(xx, yy)
abline(a = extract$ajt[1], b = extract$ajt[2])