stats (version 3.3)

ls.diag: 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

\dontshow{utils::example("lm", echo = FALSE)}
##-- 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")