R: Compute Diagnostics for 'lsfit' Regression Results

ls.diag {stats}

R Documentation

Compute Diagnostics for `lsfit` Regression Results

Description

Computes basic statistics, including standard errors, t- and p-values for the regression coefficients.

Usage

ls.diag(ls.out)

Arguments

ls.out

Typically the result of lsfit()

Value

A list with the following numeric components.

`std.dev`	The standard deviation of the errors, an estimate of σ.
`hat`	diagonal entries h_{ii} of the hat matrix H
`std.res`	standardized residuals
`stud.res`	studentized residuals
`cooks`	Cook's distances
`dfits`	DFITS statistics
`correlation`	correlation matrix
`std.err`	standard errors of the regression coefficients
`cov.scaled`	Scaled covariance matrix of the coefficients
`cov.unscaled`	Unscaled covariance matrix of the coefficients

References

Belsley, D. A., Kuh, E. and Welsch, R. E. (1980) Regression Diagnostics. New York: Wiley.

Examples


##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = as.numeric(gl(2, 10, 20)), y = weight)
dlsD9 <- ls.diag(lsD9)
utils::str(dlsD9, give.attr = FALSE)
abs(1 - sum(dlsD9$hat) / 2) < 10*.Machine$double.eps # sum(h.ii) = p
plot(dlsD9$hat, dlsD9$stud.res, xlim = c(0, 0.11))
abline(h = 0, lty = 2, col = "lightgray")

[Package stats version 3.4.1 Index]

Compute Diagnostics for lsfit Regression Results