ls.diag {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.

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")


[Package stats version 4.2.0 Index]