Computes various residuals and measures of influence for a
fit from `ols`

.

```
# S3 method for ols
residuals(object,
type=c("ordinary", "score", "dfbeta", "dfbetas",
"dffit", "dffits", "hat", "hscore"), …)
```

object

object created by `ols`

. Depending on `type`

, you may have had to
specify `x=TRUE`

to `ols`

.

type

type of residual desired. `"ordinary"`

refers to the usual residual.
`"score"`

is the matrix of score residuals (contributions to first
derivative of log likelihood).
`dfbeta`

and `dfbetas`

mean respectively the raw and normalized matrix
of changes in regression coefficients after
deleting in turn each observation. The coefficients are normalized by their
standard errors. `hat`

contains the leverages --- diagonals of the ``hat'' matrix.
`dffit`

and `dffits`

contain respectively the difference and normalized
difference in predicted values when each observation is omitted.
The S `lm.influence`

function is used. When `type="hscore"`

, the
ordinary residuals are divided by one minus the corresponding hat
matrix diagonal element to make residuals have equal variance.

…

ignored

a matrix or vector, with places for observations that were originally
deleted by `ols`

held by `NA`

s

# NOT RUN { set.seed(1) x1 <- rnorm(100) x2 <- rnorm(100) x1[1] <- 100 y <- x1 + x2 + rnorm(100) f <- ols(y ~ x1 + x2, x=TRUE, y=TRUE) resid(f, "dfbetas") which.influence(f) # }