| 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 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).
abs.error.pred(fit, lp=NULL, y=NULL) ## S3 method for class 'abs.error.pred': print(x, ...)
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. If you do not specify
the lp argument, fit must contain fitted.values or
linear.predictors. You must specify fit or both of lp and y.
|
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 |
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.
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)