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 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., Kuh, E. and Welsch, R. E. (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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