## Compute Diagnostics for `lsfit' Regression Results

## Usage

    ls.diag(ls.out)

## Arguments

`ls.out` | Typically the result of `lsfit()`

## Description

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

## 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., E. Kuh and R. E. Welsch (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)
    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 Contents]