\subsection{Results timing huang260}

\begin{figure}[H]
\centering
\includegraphics[height = 6.5in, width= 5in, angle = -90]{huang260.timing.plot.ps}
\caption{ results are obtained by generating totally new datasets for all $11*3*2 = 66$ fitting (huang260)
}
\label{fig:huang260.timing}
\end{figure}

\noindent
From figure \ref{fig:huang260.timing}, we can see that there are some
fluctuations of the timing, and sometimes the time for null fit is
even smaller than that for glmfit, we guess that it is due to random
variation of cluster itself. Increasing the size of the data may
resolve this conflict.

\begin{figure}[H]
\centering
\includegraphics[height = 6.5in, width= 5in, angle = -90]{huang260.timing.plot.large.ps}
\caption{ results are obtained by generating totally new datasets for all $11*3*2 = 66$ fitting (huang260)
}
\label{fig:huang260.timing.large}
\end{figure}

\noindent
From figure \ref{fig:huang260.timing.large}, we can see that there are some
pattern along the change of number of subset.  The time consumed by glmfit is decreasing first then increasing.


\begin{figure}[H]
\centering
\includegraphics[height = 6.5in, width= 5in, angle = -90]{huang260.timing.plot.3.ps}
\caption{ results are obtained by generating totally new datasets for different data size (huang260)
}
\label{fig:huang260.timing.3}
\end{figure}


\begin{figure}[H]
\centering
\includegraphics[height = 6.5in, width= 5in, angle = -90]{huang260.timing.plot.4.ps}
\caption{ results are obtained by generating totally new datasets for different data size (huang260)
}
\label{fig:huang260.timing.4}
\end{figure}


