ls.diag {stats} | R Documentation |
Computes basic statistics, including standard errors, t- and p-values for the regression coefficients.
ls.diag(ls.out)
ls.out |
Typically the result of lsfit() |
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 |
Belsley, D. A., Kuh, E. and Welsch, R. E. (1980) Regression Diagnostics. New York: Wiley.
hat
for the hat matrix diagonals,
ls.print
,
lm.influence
, summary.lm
,
anova
.
##-- 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")