\HeaderA{summary.aareg}{Summarize an aareg fit}{summary.aareg}
\keyword{survival}{summary.aareg}
\begin{Description}\relax
Creates the overall test statistics for an Aalen additive regression model
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'aareg':
summary(object, maxtime, test=c("aalen", "nrisk"), scale=1,...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] the result of a call to the \code{aareg} function

\item[\code{maxtime}] truncate the input to the model at time "maxtime"

\item[\code{test}] the relative time weights that will be used to compute the test

\item[\code{scale}] scales the coefficients.  
For some data sets, the coefficients of the Aalen model will be very
small (10-4); this simply multiplies the printed values by a constant,
say 1e6, to make the printout easier to read.

\item[\code{...}] for future methods
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
It is not uncommon for the very right-hand tail of the plot to have
large outlying values, particularly for the standard error.  
The \code{maxtime} parameter can then be used to
truncate the range so as to avoid these.
This gives an updated value for the test statistics, without refitting the
model.
\end{Details}
\begin{Value}
a list is returned with the following components

\begin{ldescription}
\item[\code{ table }] a matrix with rows for the intercept and each covariate, and columns
giving a slope estimate, the test statistic, it's standard error,
the z-score and a p-value

\item[\code{ test }] the time weighting used for computing the test statistics

\item[\code{ test.statistic }] the vector of test statistics

\item[\code{ test.var }] the model based variance matrix for the test statistic

\item[\code{ test.var2 }] optionally, a robust variance matrix for the test statistic

\item[\code{ chisq }] the overall test (ignoring the intercept term) for significance of
any variable

\item[\code{ n }] a vector containing the number of observations, the number of unique death
times used in the computation, and the total number of unique death times

\end{ldescription}
\end{Value}
\begin{SeeAlso}\relax
aareg, plot.aareg
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
afit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung,
     dfbeta=TRUE)
summary(afit)
## Not run: 
              slope   test se(test) robust se     z        p 
Intercept  5.05e-03    1.9     1.54      1.55  1.23 0.219000
      age  4.01e-05  108.0   109.00    106.00  1.02 0.307000
      sex -3.16e-03  -19.5     5.90      5.95 -3.28 0.001030
  ph.ecog  3.01e-03   33.2     9.18      9.17  3.62 0.000299

Chisq=22.84 on 3 df, p=4.4e-05; test weights=aalen
## End(Not run)

summary(afit, maxtime=600)
## Not run: 
              slope   test se(test) robust se      z        p 
Intercept  4.16e-03   2.13     1.48      1.47  1.450 0.146000
      age  2.82e-05  85.80   106.00    100.00  0.857 0.392000
      sex -2.54e-03 -20.60     5.61      5.63 -3.660 0.000256
  ph.ecog  2.47e-03  31.60     8.91      8.67  3.640 0.000271

Chisq=27.08 on 3 df, p=5.7e-06; test weights=aalen
## End(Not run)\end{ExampleCode}
\end{Examples}

