# ls.diag

##### 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.

The standard deviation of the errors, an estimate of \(\sigma\).

diagonal entries \(h_{ii}\) of the hat matrix \(H\)

standardized residuals

studentized residuals

Cook's distances

DFITS statistics

correlation matrix

standard errors of the regression coefficients

Scaled covariance matrix of the coefficients

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

`library(stats)`

```
# NOT RUN {
##-- Using the same data as the lm(.) example:
lsD9 <- lsfit(x = as.numeric(gl(2, 10, 20)), y = weight)
dlsD9 <- ls.diag(lsD9)
# }
# NOT RUN {
utils::str(dlsD9, give.attr = FALSE)
# }
# NOT RUN {
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")
# }
```

*Documentation reproduced from package stats, version 3.6.0, License: Part of R 3.6.0*

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