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\subsection{Stepwise Linear Regression}
\noindent{\bf Description}
Our stepwise linear regression script selects a linear model based on the Akaike information criterion (AIC):
the model that gives rise to the lowest AIC is computed. \\
\noindent{\bf Usage}
{\tt{}-f }path/\/{\tt{}StepLinearRegDS.dml}
{\tt{} -nvargs}
{\tt{} X=}path/file
{\tt{} Y=}path/file
{\tt{} B=}path/file
{\tt{} S=}path/file
{\tt{} O=}path/file
{\tt{} icpt=}int
{\tt{} thr=}double
{\tt{} fmt=}format
\noindent{\bf Arguments}
\item[{\tt X}:]
Location (on HDFS) to read the matrix of feature vectors, each row contains
one feature vector.
\item[{\tt Y}:]
Location (on HDFS) to read the 1-column matrix of response values
\item[{\tt B}:]
Location (on HDFS) to store the estimated regression parameters (the $\beta_j$'s), with the
intercept parameter~$\beta_0$ at position {\tt B[}$m\,{+}\,1$, {\tt 1]} if available
\item[{\tt S}:] (default:\mbox{ }{\tt " "})
Location (on HDFS) to store the selected feature-ids in the order as computed by the algorithm;
by default the selected feature-ids are forwarded to the standard output.
\item[{\tt O}:] (default:\mbox{ }{\tt " "})
Location (on HDFS) to store the CSV-file of summary statistics defined in
Table~\ref{table:linreg:stats}; by default the summary statistics are forwarded to the standard output.
\item[{\tt icpt}:] (default:\mbox{ }{\tt 0})
Intercept presence and shifting/rescaling the features in~$X$:\\
{\tt 0} = no intercept (hence no~$\beta_0$), no shifting or rescaling of the features;\\
{\tt 1} = add intercept, but do not shift/rescale the features in~$X$;\\
{\tt 2} = add intercept, shift/rescale the features in~$X$ to mean~0, variance~1
\item[{\tt thr}:] (default:\mbox{ }{\tt 0.01})
Threshold to stop the algorithm: if the decrease in the value of the AIC falls below {\tt thr}
no further features are being checked and the algorithm stops.
\item[{\tt fmt}:] (default:\mbox{ }{\tt "text"})
Matrix file output format, such as {\tt text}, {\tt mm}, or {\tt csv};
see read/write functions in SystemML Language Reference for details.
\noindent{\bf Details}
Stepwise linear regression iteratively selects predictive variables in an automated procedure.
Currently, our implementation supports forward selection: starting from an empty model (without any variable)
the algorithm examines the addition of each variable based on the AIC as a model comparison criterion. The AIC is defined as
AIC = -2 \log{L} + 2 edf,\label{eq:AIC}
where $L$ denotes the likelihood of the fitted model and $edf$ is the equivalent degrees of freedom, i.e., the number of estimated parameters.
This procedure is repeated until including no additional variable improves the model by a certain threshold
specified in the input parameter {\tt thr}.
For fitting a model in each iteration we use the ``direct solve'' method as in the script {\tt LinearRegDS.dml} discussed in Section~\ref{sec:LinReg}.
\noindent{\bf Returns}
Similar to the outputs from {\tt LinearRegDS.dml} the stepwise linear regression script computes
the estimated regression coefficients and stores them in matrix $B$ on HDFS.
The format of matrix $B$ is identical to the one produced by the scripts for linear regression (see Section~\ref{sec:LinReg}).
Additionally, {\tt StepLinearRegDS.dml} outputs the variable indices (stored in the 1-column matrix $S$)
in the order they have been selected by the algorithm, i.e., $i$th entry in matrix $S$ corresponds to
the variable which improves the AIC the most in $i$th iteration.
If the model with the lowest AIC includes no variables matrix $S$ will be empty (contains one 0).
Moreover, the estimated summary statistics as defined in Table~\ref{table:linreg:stats}
are printed out or stored in a file (if requested).
In the case where an empty model achieves the best AIC these statistics will not be produced.
\noindent{\bf Examples}
\hml -f StepLinearRegDS.dml -nvargs X=/user/biadmin/X.mtx Y=/user/biadmin/Y.mtx
B=/user/biadmin/B.mtx S=/user/biadmin/selected.csv O=/user/biadmin/stats.csv
icpt=2 thr=0.05 fmt=csv