abs.error.pred {Hmisc} | R Documentation |
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 \hat{Y} -
\mbox{median($\hat{Y}$)}
,
\hat{Y} - Y
, and Y -
\mbox{median($Y$)}
. The function also
computes ratios that correspond to R^2
and 1 - R^2
(but
these ratios do not add to 1.0); the R^2
measure is the ratio of
mean or median absolute \hat{Y} - \mbox{median($\hat{Y}$)}
to the mean or median absolute Y -
\mbox{median($Y$)}
. The 1 - R^2
or SSE/SST
measure is the mean or median absolute \hat{Y} - Y
divided by the mean or median absolute \hat{Y} -
\mbox{median($Y$)}
.
abs.error.pred(fit, lp=NULL, y=NULL)
## S3 method for class 'abs.error.pred'
print(x, ...)
fit |
a fit object typically from |
lp |
a vector of predicted values (Y hat above) if |
y |
a vector of response variable values if |
x |
an object created by |
... |
unused |
a list of class abs.error.pred
(used by
print.abs.error.pred
) containing two matrices:
differences
and ratios
.
Frank Harrell
Department of Biostatistics
Vanderbilt University School of Medicine
f.harrell@vanderbilt.edu
Schemper M (2003): Stat in Med 22:2299-2308.
Tian L, Cai T, Goetghebeur E, Wei LJ (2007): Biometrika 94:297-311.
lm
, ols
, cor
,
validate.ols
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)