ls.diag

0th

Percentile

Compute Diagnostics for lsfit Regression Results

Computes basic statistics, including standard errors, t- and p-values for the regression coefficients.

Keywords
regression
Usage
ls.diag(ls.out)
Arguments
ls.out
Typically the result of lsfit()
Value

• A list with the following numeric components.
• std.devThe standard deviation of the errors, an estimate of $\sigma$.
• hatdiagonal entries $h_{ii}$ of the hat matrix $H$
• std.resstandardized residuals
• stud.resstudentized residuals
• cooksCook's distances
• dfitsDFITS statistics
• correlationcorrelation matrix
• std.errstandard errors of the regression coefficients
• cov.scaledScaled covariance matrix of the coefficients
• cov.unscaledUnscaled covariance matrix of the coefficients

References

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.
library(stats) \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")