# abs.error.pred

##### Indexes of Absolute Prediction Error for Linear Models

Computes the mean and median of various absolute errors related to ordinary multiple regression models. The mean and median absolute errors correspond to the mean square due to regression, error, and total. The absolute errors computed are derived from Yhat - median(Yhat), Yhat - Y, and Y - median(Y). The function also computes ratios that correspond to Rsquare and 1 - Rsquare (but these ratios do not add to 1.0); the Rsquare measure is the ratio of mean or median absolute Yhat - median(Yhat) to the mean or median absolute Y - median(Y). The 1 - Rsquare or SSE/SST measure is the mean or median absolute Yhat - Y divided by the mean or median absolute Y - median(Y).

- Keywords
- robust, models, regression

##### Usage

`abs.error.pred(fit, lp=NULL, y=NULL)`## S3 method for class 'abs.error.pred':
print(x, \dots)

##### Arguments

- fit
- a fit object typically from
`lm`

or`ols`

that contains a`y`

vector (i.e., you should have specified`y=TRUE`

to the fitting function) unless the`y`

argument is given to`abs.error.pred`

. - lp
- a vector of predicted values (Y hat above) if
`fit`

is not given - y
- a vector of response variable values if
`fit`

(with`y=TRUE`

in effect) is not given - x
- an object created by
`abs.error.pred`

- ...
- unused

##### Value

- a list of class
`abs.error.pred`

(used by`print.abs.error.pred`

) containing two matrices:`differences`

and`ratios`

.

##### concept

predictive accuracy

##### References

Schemper M (2003): Stat in Med 22:2299-2308.

##### See Also

`lm`

, `ols`

, `cor`

, `validate.ols`

##### Examples

```
set.seed(1) # so can regenerate results
x1 <- rnorm(100)
x2 <- rnorm(100)
y <- exp(x1+x2+rnorm(100))
f <- lm(log(y) ~ x1 + poly(x2,3), y=TRUE)
abs.error.pred(lp=exp(fitted(f)), y=y)
rm(x1,x2,y,f)
```

*Documentation reproduced from package Hmisc, version 3.0-10, License: GPL version 2 or newer*