lm.influence {stats} | R Documentation

## Regression Diagnostics

### Description

This function provides the basic quantities which are used in forming a wide variety of diagnostics for checking the quality of regression fits.

### Usage

    influence(model, ...)
    ## S3 method for class 'lm':
    influence(model, do.coef = TRUE, ...)
    ## S3 method for class 'glm':
    influence(model, do.coef = TRUE, ...)

    lm.influence(model, do.coef = TRUE)

### Arguments

`model` | an object as returned by `lm`.
`do.coef` | logical indicating if the changed `coefficients` (see below) are desired. These need O(n^2 p) computing time.
`...` | further arguments passed to or from other methods.

### Details

The `influence.measures()` and other functions listed in See Also provide a more user oriented way of computing a variety of regression diagnostics. These all build on `lm.influence`.

An attempt is made to ensure that computed hat values that are probably one are treated as one, and the corresponding rows in `sigma` and `coefficients` are `NaN`. (Dropping such a case would normally result in a variable being dropped, so it is not possible to give simple drop-one diagnostics.)

`naresid` is applied to the results and so will fill in with `NA`s it the fit had `na.action = na.exclude`.

### Value

A list containing the following components of the same length or number of rows n, which is the number of non-zero weights. Cases omitted in the fit are omitted unless a `na.action` method was used (such as `na.exclude`) which restores them.

`hat` | a vector containing the diagonal of the “hat” matrix.
`coefficients` | (unless `do.coef` is false) a matrix whose i-th row contains the change in the estimated coefficients which results when the i-th case is dropped from the regression. Note that aliased coefficients are not included in the matrix.
`sigma` | a vector whose i-th element contains the estimate of the residual standard deviation obtained when the i-th case is dropped from the regression.
`wt.res` | a vector of weighted (or for class `glm` rather deviance) residuals.

### Note

The `coefficients` returned by the R version of `lm.influence` differ from those computed by S. Rather than returning the coefficients which result from dropping each case, we return the changes in the coefficients. This is more directly useful in many diagnostic measures.
Since these need O(n^2 p) computing time, they can be omitted by `do.coef = FALSE`.

Note that cases with `weights == 0` are dropped (contrary to the situation in S).

If a model has been fitted with `na.action=na.exclude` (see `na.exclude`), cases excluded in the fit are considered here.

### References

See the list in the documentation for `influence.measures`.

Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

### See Also

`summary.lm` for `summary` and related methods;
`influence.measures`,
`hat` for the hat matrix diagonals,
`dfbetas`, `dffits`, `covratio`, `cooks.distance`, `lm`.

### Examples

    ## Analysis of the life-cycle savings data
    ## given in Belsley, Kuh and Welsch.
    summary(lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi,
                        data = LifeCycleSavings),
            corr = TRUE)
    str(lmI <- lm.influence(lm.SR))

    ## For more "user level" examples, use example(influence.measures)

[Package stats version 2.3.1 Index]