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.

The functions listed in See Also give a more direct way of computing a variety of regression diagnostics. A list containing the following components: \itemhata vector containing the diagonal of the “hat” matrix. \itemcoefficientsthe change in the estimated coefficients which results when the i-th case is dropped from the regression is contained in the i-th row of this matrix. \itemsigmaa vector whose i-th element contains the estimate of the residual standard deviation obtained when the i-th case is dropped from the regression. 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.

Note that cases with weights == 0 are dropped (contrary to the situation in S). Belsley, D. A., Kuh, E. and Welsch, R. E. (1980) Regression Diagnostics. New York: Wiley. summary.lm for summary and related methods;
influence.measures,
hat for the hat matrix diagonals,
dfbetas, dffits, covratio, cooks.distance, lm. ## Analysis of the life-cycle savings data given in Belsley, Kuh ## and Welsch. data(LifeCycleSavings) summary(lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings), corr = TRUE) rstudent(lm.SR) dfbetas(lm.SR) dffits(lm.SR) covratio(lm.SR) regression

Usage

lm.influence(lm.obj)

Arguments