# ls.diag: Compute Diagnostics for 'lsfit' Regression Results

## Description

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

## Usage

 `1` ```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 σ. `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.

`hat` for the hat matrix diagonals, `ls.print`, `lm.influence`, `summary.lm`, `anova`.
 ```1 2 3 4 5 6 7``` ```##-- 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") ```