ls.diag                package: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)
     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")

