| 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 |
Value
A list with the following numeric components.
std.dev |
The standard deviation of the errors, an estimate of
|
hat |
diagonal entries |
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)
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 4.4.0 Index]