ls.diag {base}

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 \sigma.
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., E. Kuh and R. E. Welsch (1980). Regression Diagnostics. New York: Wiley.

See Also

hat for the hat matrix diagonals, ls.print, lm.influence, summary.lm, anova.

Examples

##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = as.numeric(gl(2, 10, 20)), y = weight)
dlsD9 <- ls.diag(lsD9)
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")

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