The leverage of an observation is based on how much the observation's value on the predictor variable differs from the mean of the predictor variable. The greater an observation's leverage, the more potential it has to be an influential observation.
References
Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.
See also
Other influence measures:
ols_hadi(),
ols_pred_rsq(),
ols_press()
Examples
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_leverage(model)
#> [1] 0.14830256 0.12992351 0.06317600 0.11598460 0.17187869 0.09986745
#> [7] 0.14720085 0.12912656 0.45839085 0.13597113 0.11541159 0.10343044
#> [13] 0.04684167 0.05527722 0.20578392 0.21066328 0.19546860 0.08593044
#> [19] 0.14685385 0.14389010 0.11957800 0.11051613 0.08002229 0.10635592
#> [25] 0.19566514 0.09324142 0.15093650 0.16467565 0.23978580 0.21863752
#> [31] 0.53168134 0.07953098