# ls.diag

0th

Percentile

##### 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.

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.
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") # }