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old-snippets.tex
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\begin{table}[h!]
\centering
\scriptsize
\begin{tabular}{l|cc|cccc}
\parbox[b][2mm]{33mm}{Model} &
\parbox[b][2mm]{11mm}{\centering Residual} &
\parbox[b][4mm]{7mm }{\centering R\textsuperscript{2} } &
\parbox[b][4mm]{7mm }{\centering CVM 1se } &
\parbox[b][4mm]{7mm }{\centering R\textsuperscript{2} 1se} &
\parbox[b][4mm]{7mm }{\centering CVM min } &
\parbox[b][4mm]{7mm }{\centering R\textsuperscript{2} min}
\\ \hline \hline
Age, Manifold LDDMM & 4.4 & 0.14 & - & - & - & - \\
Age, Intensity VBM & - & - & 4.9 & 0.24 & 4.8 & 0.95 \\
Age, Transport VBM & - & - & 4.5 & 0.39 & 4.3 & 0.72 \\ \hline
MMSE, Manifold LDDMM & 3.8 & 0.15 & - & - & - & - \\
MMSE, Intensity VBM & - & - & 3.80 & 0.21 & 3.61 & 0.97 \\
MMSE, Transport VBM & - & - & 3.61 & 0.25 & 3.27 & 0.54 \\ \hline
CDR, Manifold LDDMM & 0.35 & 0.20 & - & - & - & - \\
CDR, Intensity VBM & - & - & 0.36 & 0.21 & 0.33 & 0.69 \\
CDR, Transport VBM & - & - & 0.32 & 0.40 & 0.30 & 0.72 \\
\end{tabular}
\caption{ \label{fig:prediction} Elastic net regression on transport cost
and mass allocation images versus intensity images and comparison to linear
models based on a global deformation approach. (p--value 0) and for MMSE 1.1 times (p-value 0.03) based on 1000
permutations.}
\vspace{-10mm}
\end{table}
\begin{figure}[h!]
\centering
\scriptsize
\begin{tabular}{cc}
\begin{tabular}{l|cc|cccc}
\parbox[b][2mm]{33mm}{Model} &
\parbox[b][2mm]{11mm}{\centering Residual} &
\parbox[b][4mm]{7mm }{\centering R\textsuperscript{2} } &
\parbox[b][4mm]{7mm }{\centering CVM 1se } &
\parbox[b][4mm]{7mm }{\centering R\textsuperscript{2} 1se} &
\parbox[b][4mm]{7mm }{\centering CVM min } &
\parbox[b][4mm]{7mm }{\centering R\textsuperscript{2} min}
\\ \hline \hline
\scriptsize
Age, Manifold LDDMM & 4.4 & 0.14 & - & - & - & - \\
Age, Intensity VBM & - & - & 4.9 & 0.24 & 4.8 & 0.95 \\
Age, Transport VBM & - & - & 4.5 & 0.39 & 4.3 & 0.72 \\ \hline
MMSE, Manifold LDDMM & 3.8 & 0.15 & - & - & - & - \\
MMSE, Intensity VBM & - & - & 3.80 & 0.21 & 3.61 & 0.97 \\
MMSE, Transport VBM & - & - & 3.61 & 0.25 & 3.27 & 0.54 \\ \hline
CDR, Manifold LDDMM & 0.35 & 0.20 & - & - & - & - \\
CDR, Intensity VBM & - & - & 0.36 & 0.21 & 0.33 & 0.69 \\
CDR, Transport VBM & - & - & 0.32 & 0.40 & 0.30 & 0.72 \\
\end{tabular}
&
\raisebox{-13.5mm}{\includegraphics[width=34mm]{glmnet-cvm-permutations}}
\\
\footnotesize{ (a) } & \footnotesize{ (b) }
\end{tabular}
\caption{ \label{fig:prediction} (b) Elastic net regression on transport cost
and mass allocation images versus intensity images and comparison to linear
models based on a global deformation approach.(a) Permutation test for the
transport based regression fits. The y-Axis show the distribution of the
permuted CVM scaled by the non-permuted CVM, thus for Age the permuted results
are on average 1.5 times above the non-permuted fit (p--value 0), for CDR 1.35
times (p--value 0) and for MMSE 1.1 times (p-value 0.03) based on 1000
permutations.}
\vspace{-7mm}
\end{figure}
\begingroup
\newcolumntype{M}[1]{>{\centering\arraybackslash}m{#1}}
%\scriptsize
\renewcommand{\arraystretch}{0}
\setlength{\tabcolsep}{0pt}
\begin{figure}[h!]
\centering
\begin{tabular}{r}
\begin{tabular}{lcr}
\multicolumn{3}{c}{\parbox[t][3mm]{20mm}{Pearson's r } }\\
\parbox[t][3mm]{7mm}{-0.7} &
\includegraphics[width=0.8\linewidth]{colorbar} &
\parbox[t][3mm]{6mm}{\hfill 0.7}
\end{tabular}\\
\begin{tabular}{l||ccc||ccc||ccc}
\multirow{2}{*}{\rotatebox[origin=c]{90}{Age}}&
% \rotatebox[origin=c]{90}{G}&
\includegraphics[width=0.1\linewidth]{cor-axial-age-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-age-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-age-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-axial-age-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-age-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-age-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-axial-age-t-tG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-age-t-tG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-age-t-tG}
\\
&
% \rotatebox[origin=c]{90}{W}&
\includegraphics[width=0.1\linewidth]{cor-axial-age-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-age-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-age-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-axial-age-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-age-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-age-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-axial-age-t-tW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-age-t-tW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-age-t-tW}
\\ \hline
\multirow{2}{*}{\rotatebox[origin=c]{90}{CDR}}&
% \rotatebox[origin=c]{90}{G}&
\includegraphics[width=0.1\linewidth]{cor-axial-cdr-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-cdr-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-cdr-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-axial-cdr-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-cdr-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-cdr-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-axial-cdr-t-tG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-cdr-t-tG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-cdr-t-tG}
\\
&
% \rotatebox[origin=c]{90}{W}&
\includegraphics[width=0.1\linewidth]{cor-axial-cdr-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-cdr-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-cdr-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-axial-cdr-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-cdr-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-cdr-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-axial-cdr-t-tW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-cdr-t-tW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-cdr-t-tW}
\\ \hline
\multirow{2}{*}{\rotatebox[origin=c]{90}{MMSE}} &
% \rotatebox[origin=c]{90}{G}&
\includegraphics[width=0.1\linewidth]{cor-axial-mmse-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-mmse-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-mmse-t-iG} &
\includegraphics[width=0.1\linewidth]{cor-axial-mmse-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-mmse-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-mmse-t-mG} &
\includegraphics[width=0.1\linewidth]{cor-axial-mmse-t-tG} &
\includegraphics[width=0.1\linewidth]{cor-coronal-mmse-t-tG} &
\includegraphics[width=0.1\linewidth]{cor-sagital-mmse-t-tG}
\\
&
% \rotatebox[origin=c]{90}{W}&
\includegraphics[width=0.1\linewidth]{cor-axial-mmse-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-mmse-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-mmse-t-iW} &
\includegraphics[width=0.1\linewidth]{cor-axial-mmse-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-mmse-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-mmse-t-mW} &
\includegraphics[width=0.1\linewidth]{cor-axial-mmse-t-tW} &
\includegraphics[width=0.1\linewidth]{cor-coronal-mmse-t-tW} &
\includegraphics[width=0.1\linewidth]{cor-sagital-mmse-t-tW}
\\ \hline \hline
& \multicolumn{3}{c||}{Voxel Intensity}
& \multicolumn{3}{c||}{Mass Imbalance}
& \multicolumn{3}{c}{Transport Cost}
\end{tabular}
\end{tabular}
\caption{\label{fig:cor-oasis}
Correlation of Age, MMSE and CDR with smoothed segmentation mask intensity,
optimal transport mass imbalances and optimal transport costs of gray and white
matter. Correlations are only shown at locations permutation tested p--value
less than 0.05. The background image are average white and gray matter
segmentations.
\vspace{-7mm}
}
\end{figure}
\endgroup
\begingroup
\renewcommand{\arraystretch}{0}
\setlength{\tabcolsep}{0pt}
\begin{figure}[h!]
\centering
\begin{tabular}{r}
\begin{tabular}{lcr}
\multicolumn{3}{c}{\parbox[t][3mm]{20mm}{Pearson's r } }\\
\parbox[t][3mm]{7mm}{-0.7} &
\includegraphics[width=0.7\linewidth]{colorbar} &
\parbox[t][3mm]{6mm}{\hfill 0.7}
\end{tabular}\\
\begin{tabular}{l|cc|cc|cc}
\parbox[t]{4mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{Voxel Intensity}}}&
\includegraphics[width=0.13\linewidth]{cor-axial-age-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-axial-age-t-iW} &
\includegraphics[width=0.13\linewidth]{cor-axial-cdr-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-axial-cdr-t-iW} &
\includegraphics[width=0.13\linewidth]{cor-axial-mmse-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-axial-mmse-t-iW} \\
%
&
\includegraphics[width=0.13\linewidth]{cor-coronal-age-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-age-t-iW} &
\includegraphics[width=0.13\linewidth]{cor-coronal-cdr-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-cdr-t-iW} &
\includegraphics[width=0.13\linewidth]{cor-coronal-mmse-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-mmse-t-iW} \\
%
&
\includegraphics[width=0.13\linewidth]{cor-sagital-age-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-age-t-iW} &
\includegraphics[width=0.13\linewidth]{cor-sagital-cdr-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-cdr-t-iW} &
\includegraphics[width=0.13\linewidth]{cor-sagital-mmse-t-iG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-mmse-t-iW} \\ \hline \hline
%%
\parbox[t]{4mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{Mass Imbalance}}}&
\includegraphics[width=0.13\linewidth]{cor-axial-age-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-axial-age-t-mW} &
\includegraphics[width=0.13\linewidth]{cor-axial-cdr-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-axial-cdr-t-mW} &
\includegraphics[width=0.13\linewidth]{cor-axial-mmse-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-axial-mmse-t-mW} \\
%
&
\includegraphics[width=0.13\linewidth]{cor-coronal-age-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-age-t-mW} &
\includegraphics[width=0.13\linewidth]{cor-coronal-cdr-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-cdr-t-mW} &
\includegraphics[width=0.13\linewidth]{cor-coronal-mmse-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-mmse-t-mW} \\
%
&
\includegraphics[width=0.13\linewidth]{cor-sagital-age-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-age-t-mW} &
\includegraphics[width=0.13\linewidth]{cor-sagital-cdr-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-cdr-t-mW} &
\includegraphics[width=0.13\linewidth]{cor-sagital-mmse-t-mG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-mmse-t-mW} \\ \hline \hline
%%
\parbox[t]{2mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{Transport Cost}}}&
\includegraphics[width=0.13\linewidth]{cor-axial-age-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-axial-age-t-tW} &
\includegraphics[width=0.13\linewidth]{cor-axial-cdr-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-axial-cdr-t-tW} &
\includegraphics[width=0.13\linewidth]{cor-axial-mmse-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-axial-mmse-t-tW} \\
%
&
\includegraphics[width=0.13\linewidth]{cor-coronal-age-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-age-t-tW} &
\includegraphics[width=0.13\linewidth]{cor-coronal-cdr-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-cdr-t-tW} &
\includegraphics[width=0.13\linewidth]{cor-coronal-mmse-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-coronal-mmse-t-tW} \\
%
&
\includegraphics[width=0.13\linewidth]{cor-sagital-age-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-age-t-tW} &
\includegraphics[width=0.13\linewidth]{cor-sagital-cdr-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-cdr-t-tW} &
\includegraphics[width=0.13\linewidth]{cor-sagital-mmse-t-tG} &
\includegraphics[width=0.13\linewidth]{cor-sagital-mmse-t-tW} \\ \hline \hline
%
& \parbox[b][4mm]{12mm}{Age(w)}
& \parbox[b][4mm]{12mm}{Age(g)}
& \parbox[b][4mm]{15mm}{CDR(w)}
& \parbox[b][4mm]{15mm}{CDR(g) }
& \parbox[b][4mm]{15mm}{-MMSE(w)}
& \parbox[b][4mm]{15mm}{-MMSE(g)}
\end{tabular}
\end{tabular}
\caption{\label{fig:cor-oasis}
Correlation of Age, MMSE and CDR with smoothed segmentation mask intensity,
optimal transport mass imbalances and optimal transport costs of gray and white
matter. Correlations are only shown at locations permutation tested p--value
less than 0.05. The background image are average white and gray matter
segmentations.
}
\end{figure} \endgroup
\begin{figure}[htb]
\scriptsize
\begin{tabular}{cc}
\raisebox{-15mm}{\includegraphics[width=34mm]{glmnet-cvm-permutations}}
&
\begin{tabular}{l|cccc|cc}
\parbox[2mm]{33mm}{Model} &
\parbox[2mm]{11mm}{\centering Residual} &
\parbox[2mm]{5mm }{\centering $R^2$} &
\parbox[2mm]{5mm }{\centering F} &
\parbox[2mm]{11mm}{\centering p--value} &
\parbox[2mm]{7mm }{\centering CVM } &
\parbox[2mm]{7mm }{\centering $l_1 R^2$}
\\ \hline \hline
\scriptsize
Age, Manifold LDDMM & 4.4 & 0.14 & 9.9 & 1.0e-4 & NA & NA \\
Age, Intensity VBM & 2.4 & 0.86 & 17.4 & $<\epsilon$ & 4.9 & 0.24 \\
Age, Transport VBM & 3.1 & 0.74 & 11.1 & $<\epsilon$ & 4.5 & 0.39 \\ \hline
MMSE, Manifold LDDMM & 3.8 & 0.15 & 20.9 & 1.2e-5 & NA & NA \\
MMSE, Intensity VBM & 3.0 & 0.50 & 5.6 & 2.1e-10 & 3.80 & 0.21 \\
MMSE, Transport VBM & 3.0 & 0.50 & 11.2 & 3.3e-13 & 3.61 & 0.25 \\ \hline
CDR, Manifold LDDMM & 0.35 & 0.20 & 30.0 & 2.4e-7 & NA & NA \\
CDR, Intensity VBM & 0.19 & 0.79 & 19.5 & $<\epsilon$ & 0.36 & 0.21 \\
CDR, Transport VBM & 0.26 & 0.61 & 8.5 & 5.2e-15 & 0.32 & 0.40 \\
\end{tabular} \\
\footnotesize{ (a) } & \footnotesize{ (b) }
\end{tabular}
\caption{ \label{fig:prediction} Prediction with elastic net and comparison to
linear models based on the LDDMM manifold approach.}
\end{figure}
mmse permuted p-value 0.03, other 0
\secti
\begingroup
\renewcommand{\arraystretch}{0}
\setlength{\tabcolsep}{0pt}
\begin{figure}[bth]
\centering
\begin{tabular}{l|cc|cc|cc}
\parbox[t]{4mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{Mass Imbalance}}}&
\includegraphics[width=0.16\linewidth]{cor-axial-age-mW} &
\includegraphics[width=0.16\linewidth]{cor-axial-age-t-mW} &
\includegraphics[width=0.16\linewidth]{cor-axial-cdr-mW} &
\includegraphics[width=0.16\linewidth]{cor-axial-cdr-t-mW} &
\includegraphics[width=0.16\linewidth]{cor-axial-mmse-mW} &
\includegraphics[width=0.16\linewidth]{cor-axial-mmse-t-mW} \\
%
&
\includegraphics[width=0.16\linewidth]{cor-coronal-age-mW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-age-t-mW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-cdr-mW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-cdr-t-mW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-mmse-mW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-mmse-t-mW} \\
%
&
\includegraphics[width=0.16\linewidth]{cor-sagital-age-mW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-age-t-mW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-cdr-mW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-cdr-t-mW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-mmse-mW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-mmse-t-mW} \\ \hline \hline
%
\parbox[t]{2mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{Transport Cost}}}&
\includegraphics[width=0.16\linewidth]{cor-axial-age-tW} &
\includegraphics[width=0.16\linewidth]{cor-axial-age-t-tW} &
\includegraphics[width=0.16\linewidth]{cor-axial-cdr-tW} &
\includegraphics[width=0.16\linewidth]{cor-axial-cdr-t-tW} &
\includegraphics[width=0.16\linewidth]{cor-axial-mmse-tW} &
\includegraphics[width=0.16\linewidth]{cor-axial-mmse-t-tW} \\
%
&
\includegraphics[width=0.16\linewidth]{cor-coronal-age-tW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-age-t-tW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-cdr-tW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-cdr-t-tW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-mmse-tW} &
\includegraphics[width=0.16\linewidth]{cor-coronal-mmse-t-tW} \\
%
&
\includegraphics[width=0.16\linewidth]{cor-sagital-age-tW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-age-t-tW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-cdr-tW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-cdr-t-tW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-mmse-tW} &
\includegraphics[width=0.16\linewidth]{cor-sagital-mmse-t-tW} \\ \hline \hline
%
& \parbox[b][4mm]{6mm}{Age}
& \parbox[b][4mm]{6mm}{Age\textsuperscript{*}}
& \parbox[b][4mm]{6mm}{CDR}
& \parbox[b][4mm]{6mm}{CDR\textsuperscript{*}}
& \parbox[b][4mm]{6mm}{MMSE}
& \parbox[b][4mm]{6mm}{MMSE\textsuperscript{*}}
\end{tabular}
\caption{\label{fig:cor-oasis-gray}
Correlation of age, MMSE and CDR with optimal transport mass imbalances and
optimal transport costs of gray matter. The columns with a \textsuperscript{*}
only show the voxels were the correlation has a permutation tested p-value less
than 0.05 }
\end{figure}
\endgroup
\begingroup
\renewcommand{\arraystretch}{0}
\setlength{\tabcolsep}{0pt}
\begin{figure}[bth]
\centering
\begin{tabular}{l|cc|cc|cc}
\parbox[t]{4mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{Mass Imbalance}}}&
\includegraphics[width=0.16\linewidth]{cor-axial-age-mG} &
\includegraphics[width=0.16\linewidth]{cor-axial-age-t-mG} &
\includegraphics[width=0.16\linewidth]{cor-axial-cdr-mG} &
\includegraphics[width=0.16\linewidth]{cor-axial-cdr-t-mG} &
\includegraphics[width=0.16\linewidth]{cor-axial-mmse-mG} &
\includegraphics[width=0.16\linewidth]{cor-axial-mmse-t-mG} \\
%
&
\includegraphics[width=0.16\linewidth]{cor-coronal-age-mG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-age-t-mG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-cdr-mG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-cdr-t-mG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-mmse-mG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-mmse-t-mG} \\
%
&
\includegraphics[width=0.16\linewidth]{cor-sagital-age-mG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-age-t-mG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-cdr-mG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-cdr-t-mG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-mmse-mG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-mmse-t-mG} \\ \hline \hline
%
\parbox[t]{2mm}{\multirow{3}{*}{\rotatebox[origin=c]{90}{Transport Cost}}}&
\includegraphics[width=0.16\linewidth]{cor-axial-age-tG} &
\includegraphics[width=0.16\linewidth]{cor-axial-age-t-tG} &
\includegraphics[width=0.16\linewidth]{cor-axial-cdr-tG} &
\includegraphics[width=0.16\linewidth]{cor-axial-cdr-t-tG} &
\includegraphics[width=0.16\linewidth]{cor-axial-mmse-tG} &
\includegraphics[width=0.16\linewidth]{cor-axial-mmse-t-tG} \\
%
&
\includegraphics[width=0.16\linewidth]{cor-coronal-age-tG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-age-t-tG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-cdr-tG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-cdr-t-tG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-mmse-tG} &
\includegraphics[width=0.16\linewidth]{cor-coronal-mmse-t-tG} \\
%
&
\includegraphics[width=0.16\linewidth]{cor-sagital-age-tG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-age-t-tG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-cdr-tG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-cdr-t-tG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-mmse-tG} &
\includegraphics[width=0.16\linewidth]{cor-sagital-mmse-t-tG} \\ \hline \hline
%%
& \parbox[b][4mm]{6mm}{Age}
& \parbox[b][4mm]{6mm}{Age\textsuperscript{*}}
& \parbox[b][4mm]{6mm}{CDR}
& \parbox[b][4mm]{6mm}{CDR\textsuperscript{*}}
& \parbox[b][4mm]{6mm}{MMSE}
& \parbox[b][4mm]{6mm}{MMSE\textsuperscript{*}}
\end{tabular}
\caption{\label{fig:cor-oasis-white}
Correlation of age, MMSE and CDR with optimal transport mass imbalances and
optimal transport costs of white matter. The columns with a \textsuperscript{*}
only show the voxels were the correlation has a permutation tested p-value less
than 0.05 }
\end{figure}
\endgroup
%%%VBM glmnet /linear models
> ind <- get.selected.index(m.cdr$model)
> print( sqrt( m.cdr$model$cvm[ind$lambda.min] ) )
[1] 0.3369944
> print( m.cdr$model$glmnet.fit$dev.ratio[ind$lambda.min] )
[1] 0.5276706
> build.linear.model( cdr[cdr.index], all[cdr.index,ind$non.zero])$anova
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
X1 1 1.8308 1.83076 48.8029 2.017e-10 ***
X2 1 2.6704 2.67043 71.1860 1.159e-13 ***
X3 1 1.1870 1.18697 31.6413 1.342e-07 ***
X4 1 1.7614 1.76136 46.9528 3.919e-10 ***
X5 1 1.8326 1.83258 48.8512 1.982e-10 ***
X6 1 1.3744 1.37444 36.6386 1.861e-08 ***
X7 1 1.1817 1.18169 31.5004 1.421e-07 ***
X8 1 0.6623 0.66228 17.6545 5.285e-05 ***
X9 1 0.1397 0.13965 3.7227 0.0561616 .
X10 1 0.1610 0.16103 4.2926 0.0405350 *
X11 1 0.8895 0.88950 23.7115 3.634e-06 ***
X12 1 0.5419 0.54187 14.4447 0.0002334 ***
X13 1 0.3259 0.32586 8.6866 0.0038885 **
X14 1 0.0633 0.06331 1.6876 0.1965444
X15 1 0.4172 0.41721 11.1216 0.0011519 **
X16 1 0.1530 0.15301 4.0789 0.0457677 *
X17 1 0.3900 0.38999 10.3961 0.0016476 **
X18 1 0.1350 0.13503 3.5995 0.0603283 .
X19 1 0.2067 0.20674 5.5110 0.0206212 *
X21 1 0.0823 0.08235 2.1951 0.1412046
X22 ;
double eyeClosingRadiusFactor = 1/7.0;
1 0.0191 0.01907 0.5085 0.4772630
X23 1 0.0743 0.07425 1.9794 0.1621720
Residuals 114 4.2765 0.03751
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary( build.linear.model( cdr[cdr.index], all[cdr.index,ind$non.zero])$lm )
Call:
lm(formula = y ~ ., data = data.frame(y = y, x))
Residuals:
Min 1Q Median 3Q Max
-0.53460 -0.11300 0.00687 0.11912 0.39452
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.35161 0.10524 12.843 < 2e-16 ***
X1 -0.22982 0.08038 -2.859 0.005051 **
X2 -0.13452 0.06163 -2.183 0.031113 *
X3 -0.07904 0.09291 -0.851 0.396691
X4 -0.09599 0.06301 -1.523 0.130434
X5 -0.03280 0.06564 -0.500 0.618281
X6 1.02841 0.22170 4.639 9.42e-06 ***
X7 -0.01586 0.05894 -0.269 0.788312
X8 -0.04065 0.06851 -0.593 0.554060
X9 -0.08348 0.06526 -1.279 0.203413
X10 -0.01422 0.06816 -0.209 0.835085
X11 -0.27093 0.07094 -3.819 0.000219 ***
X12 -0.09436 0.06162 -1.531 0.128452
X13 -0.09235 0.04994 -1.849 0.067024 .
X14 -0.06120 0.06248 -0.980 0.329381
X15 -0.13793 0.05184 -2.661 0.008925 **
X16 -0.07291 0.05324 -1.369 0.173552
X17 0.07349 0.07021 1.047 0.297430
X18 0.12848 0.09265 1.387 0.168224
X19 -0.12813 0.04871 -2.630 0.009706 **
X20 NA NA NA NA
X21 -0.03906 0.06738 -0.580 0.563266
X22 -0.02671 0.06978 -0.383 0.702563
X23 -0.08188 0.05820 -1.407 0.162172
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1937 on 114 degrees of freedom
Multiple R-squared: 0.7901, Adjusted R-squared: 0.7496
F-statistic: 19.51 on 22 and 114 DF, p-value: < 2.2e-16
> build.linear.model( mmse[mmse.index], all[mmse.index,ind$non.zero])$anova
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
X1 1 81.53 81.533 8.9223 0.0034495 **
X2 1 93.27 93.274 10.2072 0.0018096 **
X3 1 151.29 151.288 16.5558 8.737e-05 ***
X4 1 53.78 53.780 5.8853 0.0168355 *
X5 1 141.24 141.244 15.4567 0.0001453 ***
X6 1 92.45 92.454 10.1175 0.0018922 **
X7 1 133.88 133.876 14.6504 0.0002119 ***
X8 1 18.54 18.540 2.0289 0.1570614
X9 1 0.01 0.012 0.0013 0.9710873
X10 1 7.21 7.208 0.7888 0.3763388
X11 1 123.07 123.067 13.4676 0.0003707 ***
X12 1 51.85 51.845 5.6736 0.0188783 *
X13 1 8.04 8.039 0.8798 0.3502466
X14 1 14.19 14.191 1.5529 0.2152593
X15 1 54.49 54.486 5.9626 0.0161490 *
X16 1 20.78 20.777 2.2736 0.1343573
X17 1 17.36 17.355 1.8992 0.1708631
X18 1 0.31 0.313 0.0342 0.8535828
X19 1 27.40 27.400 2.9985 0.0860480 .
X21 1 26.70 26.704 2.9223 0.0900843 .
X22 1 0.48 0.480 0.0525 0.8191587
X23 1 24.62 24.618 2.6940 0.1034817
Residuals 114 1041.74 9.138
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary( build.linear.model( mmse[mmse.index], all[mmse.index,ind$non.zero])$lm )
Call:
lm(formula = y ~ ., data = data.frame(y = y, x))
Residuals:
Min 1Q Median 3Q Max
-8.4886 -1.3224 0.2691 1.4388 7.0671
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.51382 1.64248 10.663 < 2e-16 ***
X1 0.99499 1.25449 0.793 0.42934
X2 0.06708 0.96195 0.070 0.94453
X3 3.34606 1.45005 2.308 0.02283 *
X4 -0.47072 0.98341 -0.479 0.63310
X5 0.41234 1.02441 0.403 0.68806
X6 -9.16063 3.46026 -2.647 0.00926 **
X7 0.97547 0.91996 1.060 0.29123
X8 -0.27645 1.06920 -0.259 0.79644
X9 0.10220 1.01853 0.100 0.92025
X10 -0.26964 1.06386 -0.253 0.80037
X11 3.55619 1.10726 3.212 0.00172 **
X12 0.99934 0.96175 1.039 0.30096
X13 0.55716 0.77946 0.715 0.47619
X14 -1.56943 0.97512 -1.609 0.11028
X15 1.54554 0.80912 1.910 0.05863 .
X16 0.90974 0.83090 1.095 0.27587
X17 -0.42487 1.09581 -0.388 0.69894
X18 0.87231 1.44604 0.603 0.54755
X19 1.51644 0.76026 1.995 0.04847 *
X20 NA NA NA NA
X21 1.24946 1.05170 1.188 0.23729
X22 -0.64033 1.08912 -0.588 0.55774
X23 1.49085 0.90831 1.641 0.10348
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.023 on 114 degrees of freedom
Multiple R-squared: 0.5231, Adjusted R-squared: 0.431
F-statistic: 5.683 on 22 and 114 DF, p-value: 2.113e-10
> print( sqrt( m.age$model$cvm[ind$lambda.min] ) )
[1] 4.821759
> print( m.age$model$glmnet.fit$dev.ratio[ind$lambda.min] )
[1] 0.5374445
> build.linear.model( age[age.index], all[age.index,ind$non.zero])$anova
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
X1 1 459.56 459.56 81.7679 1.067e-14 ***
X2 1 287.65 287.65 51.1798 1.318e-10 ***
X3 1 455.19 455.19 80.9902 1.328e-14 ***
X4 1 276.32 276.32 49.1649 2.623e-10 ***
X5 1 255.65 255.65 45.4867 9.447e-10 ***
X6 1 265.54 265.54 47.2473 5.095e-10 ***
X7 1 134.47 134.47 23.9258 3.747e-06 ***
X8 1 199.76 199.76 35.5426 3.610e-08 ***
X9 1 109.63 109.63 19.5059 2.502e-05 ***
X10 1 81.00 81.00 14.4116 0.0002496 ***
X11 1 56.09 56.09 9.9796 0.0020830 **
X12 1 14.58 14.58 2.5940 0.1103571
X13 1 120.26 120.26 21.3967 1.099e-05 ***
X14 1 56.68 56.68 10.0853 0.0019771 **
X15 1 43.16 43.16 7.6797 0.0066381 **
X16 1 10.35 10.35 1.8424 0.1776642
X17 1 17.53 17.53 3.1186 0.0803967 .
X18 1 96.69 96.69 17.2039 6.961e-05 ***
X19 1 10.35 10.35 1.8414 0.1777766
X20 1 9.85 9.85 1.7518 0.1886089
X21 1 6.70 6.70 1.1926 0.2773866
X22 1 39.29 39.29 6.9902 0.0094902 **
X23 1 37.31 37.31 6.6381 0.0114136 *
X24 1 97.51 97.51 17.3499 6.519e-05 ***
X25 1 12.73 12.73 2.2646 0.1354486
X26 1 31.34 31.34 5.5760 0.0201079 *
X27 1 7.67 7.67 1.3640 0.2455709
X28 1 13.61 13.61 2.4210 0.1228136
X29 1 35.48 35.48 6.3120 0.0135592 *
X30 1 30.58 30.58 5.4414 0.0216284 *
X31 1 0.00 0.00 0.0000 0.9960026
X32 1 14.68 14.68 2.6126 0.1091048
X33 1 0.04 0.04 0.0064 0.9361609
X34 1 36.77 36.77 6.5429 0.0120005 *
Residuals 102 573.27 5.62
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary( build.linear.model( age[age.index], all[age.index,ind$non.zero])$lm )
Call:
lm(formula = y ~ ., data = data.frame(y = y, x))
Residuals:
Min 1Q Median 3Q Max
-5.997 -1.348 0.281 1.494 4.167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 84.90862 1.11561 76.110 < 2e-16 ***
X1 -2.21284 0.83988 -2.635 0.009733 **
X2 -1.19194 0.68158 -1.749 0.083334 .
X3 -1.50039 0.79992 -1.876 0.063559 .
X4 -1.29185 0.73302 -1.762 0.081000 .
X5 2.22152 0.66255 3.353 0.001123 **
X6 -1.34104 0.66844 -2.006 0.047478 *
X7 0.04952 1.52818 0.032 0.974211
X8 -2.55550 0.62414 -4.094 8.49e-05 ***
X9 -1.25294 0.60144 -2.083 0.039731 *
X10 -0.73707 1.64825 -0.447 0.655689
X11 -0.95247 1.34994 -0.706 0.482066
X12 0.51538 0.88868 0.580 0.563232
X13 -1.32079 0.62809 -2.103 0.037938 *
X14 -0.93136 0.68649 -1.357 0.177872
X15 -0.66467 0.88601 -0.750 0.454872
X16 0.35224 0.83104 0.424 0.672564
X17 -0.63662 0.93207 -0.683 0.496145
X18 -1.23444 0.62904 -1.962 0.052437 .
X19 -0.73601 0.79241 -0.929 0.355174
X20 0.33562 0.73732 0.455 0.649944
X21 -0.37593 0.66002 -0.570 0.570220
X22 -0.86862 0.67620 -1.285 0.201853
X23 -1.78443 0.60955 -2.927 0.004215 **
X24 -2.36223 0.59533 -3.968 0.000135 ***
X25 -1.17843 0.61988 -1.901 0.060118 .
X26 -9.93285 3.89183 -2.552 0.012186 *
X27 -0.03034 0.81001 -0.037 0.970191
X28 -1.00501 0.63002 -1.595 0.113760
X29 -1.46378 0.63787 -2.295 0.023792 *
X30 -2.56991 1.00318 -2.562 0.011877 *
X31 0.94415 0.85612 1.103 0.272697
X32 -1.35505 1.08415 -1.250 0.214205
X33 -0.18314 1.01072 -0.181 0.856573
X34 1.57927 0.61741 2.558 0.012001 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.371 on 102 degrees of freedom
Multiple R-squared: 0.8529, Adjusted R-squared: 0.8039
F-statistic: 17.39 on 34 and 102 DF, p-value: < 2.2e-16
%%%%Optimal transport glmnet / linear models
> build.linear.model( age[age.index], all[age.index,ind$non.zero])$anova
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
mG.35390 1 452.79 452.79 48.6765 2.537e-10 ***
mG.47319 1 397.31 397.31 42.7120 2.155e-09 ***
mW.10288 1 608.08 608.08 65.3711 9.769e-13 ***
mW.32409 1 380.72 380.72 40.9285 4.158e-09 ***
mW.34788 1 11.55 11.55 1.2421 0.2675328
mW.35355 1 107.21 107.21 11.5259 0.0009610 ***
mW.41736 1 27.60 27.60 2.9667 0.0878573 .
mW.44233 1 60.30 60.30 6.4829 0.0123018 *
mW.44276 1 44.15 44.15 4.7463 0.0315351 *
mW.46565 1 13.08 13.08 1.4064 0.2382650
mW.46653 1 10.66 10.66 1.1456 0.2868669
mW.47145 1 15.81 15.81 1.6996 0.1951067
mW.58048 1 2.13 2.13 0.2292 0.6330675
mW.58092 1 6.07 6.07 0.6530 0.4208182
mW.58137 1 14.06 14.06 1.5119 0.2215200
mW.81162 1 58.13 58.13 6.2494 0.0139274 *
mW.81206 1 1.86 1.86 0.2000 0.6556251
mW.83581 1 4.67 4.67 0.5018 0.4802432
tG.5201 1 120.23 120.23 12.9254 0.0004902 ***
tG.15161 1 88.43 88.43 9.5064 0.0025995 **
tG.35152 1 120.20 120.20 12.9218 0.0004910 ***
tG.56970 1 140.95 140.95 15.1531 0.0001719 ***
tG.57013 1 73.48 73.48 7.8989 0.0058745 **
tG.60180 1 12.94 12.94 1.3910 0.2408212
tw.49347 1 59.03 59.03 6.3455 0.0132328 *
tw.51634 1 19.76 19.76 2.1241 0.1478975
tw.55270 1 8.28 8.28 0.8897 0.3476540
tw.83450 1 33.18 33.18 3.5672 0.0616126 .
Residuals 108 1004.61 9.30
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary( build.linear.model( age[age.index], all[age.index,ind$non.zero])$lm )
Call:
lm(formula = y ~ ., data = data.frame(y = y, x))
Residuals:
Min 1Q Median 3Q Max
-7.2959 -1.6669 0.0181 1.5565 7.6762
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.645e+01 9.753e-01 78.391 <2e-16 ***
mG.35390 -3.474e-01 1.430e-01 -2.430 0.0168 *
mG.47319 -1.606e-02 1.536e-01 -0.105 0.9169
mW.10288 -9.264e+05 5.529e+05 -1.676 0.0967 .
mW.32409 -1.143e-01 1.326e-01 -0.862 0.3905
mW.34788 1.261e-01 1.675e-01 0.753 0.4534
mW.35355 -2.656e-01 2.164e-01 -1.227 0.2224
mW.41736 -5.270e-02 7.319e-02 -0.720 0.4731
mW.44233 2.144e-01 3.346e-01 0.641 0.5230
mW.44276 -1.938e-01 2.383e-01 -0.813 0.4178
mW.46565 -2.752e-01 4.190e-01 -0.657 0.5127
mW.46653 8.739e-02 2.716e-01 0.322 0.7483
mW.47145 -2.127e-02 1.377e-01 -0.154 0.8775
mW.58048 -4.132e-02 2.625e-01 -0.157 0.8752
mW.58092 -4.960e-02 3.270e-01 -0.152 0.8797
mW.58137 -6.686e-02 2.454e-01 -0.272 0.7858
mW.81162 -1.355e-01 2.790e-01 -0.486 0.6283
mW.81206 1.095e-01 3.178e-01 0.345 0.7310
mW.83581 -1.362e-01 1.510e-01 -0.902 0.3690
tG.5201 1.367e+00 7.404e-01 1.846 0.0676 .
tG.15161 2.641e-01 1.097e-01 2.409 0.0177 *
tG.35152 8.047e-02 3.093e-02 2.602 0.0106 *
tG.56970 -4.579e+02 3.260e+02 -1.404 0.1631
tG.57013 -1.873e+03 9.301e+02 -2.014 0.0465 *
tG.60180 5.572e-04 4.735e-04 1.177 0.2419
tw.49347 -1.140e-04 1.256e-03 -0.091 0.9279
tw.51634 -1.563e-03 1.454e-03 -1.075 0.2846
tw.55270 1.488e+03 2.232e+03 0.667 0.5062
tw.83450 -8.596e-04 4.551e-04 -1.889 0.0616 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.05 on 108 degrees of freedom
Multiple R-squared: 0.7422, Adjusted R-squared: 0.6754
F-statistic: 11.11 on 28 and 108 DF, p-value: < 2.2e-16
>
> build.linear.model( cdr[cdr.index], all[cdr.index,ind$non.zero])$anova
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
mW.15261 1 4.7664 4.7664 68.3471 2.696e-13 ***
mW.19740 1 1.0071 1.0071 14.4415 0.0002328 ***
mW.26275 1 0.8482 0.8482 12.1622 0.0006917 ***
mW.26515 1 0.4747 0.4747 6.8069 0.0102870 *
mW.26516 1 0.0003 0.0003 0.0040 0.9499686
mW.28759 1 0.0401 0.0401 0.5746 0.4499910
mW.28803 1 0.0504 0.0504 0.7226 0.3970552
mW.28986 1 0.0124 0.0124 0.1783 0.6736484
mW.30919 1 0.0320 0.0320 0.4590 0.4994502
mW.31047 1 0.0087 0.0087 0.1242 0.7251502
mW.37502 1 0.1681 0.1681 2.4109 0.1232374
mW.46809 1 0.1013 0.1013 1.4524 0.2306223
tG.508 1 0.9945 0.9945 14.2599 0.0002537 ***
tG.552 1 0.0207 0.0207 0.2974 0.5865795
tG.2841 1 0.0599 0.0599 0.8585 0.3561025
tG.2884 1 0.1344 0.1344 1.9275 0.1677143
tG.21905 1 1.7449 1.7449 25.0202 2.056e-06 ***
tG.69510 1 1.1820 1.1820 16.9495 7.255e-05 ***
tw.35578 1 0.3562 0.3562 5.1077 0.0257018 *
tw.46838 1 0.1289 0.1289 1.8488 0.1765849
tw.66592 1 0.2250 0.2250 3.2260 0.0751026 .
Residuals 115 8.0198 0.0697
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary( build.linear.model( cdr[cdr.index], all[cdr.index,ind$non.zero])$lm )
Call:
lm(formula = y ~ ., data = data.frame(y = y, x))
Residuals:
Min 1Q Median 3Q Max
-0.49165 -0.15764 0.00573 0.13733 1.13225
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.039e-01 8.995e-02 5.602 1.47e-07 ***
mW.15261 -3.043e+03 2.178e+03 -1.397 0.165146
mW.19740 -1.404e-02 2.376e-02 -0.591 0.555772
mW.26275 -1.063e-02 1.195e-02 -0.890 0.375436
mW.26515 -3.440e-02 5.217e-02 -0.660 0.510888
mW.26516 2.313e-02 3.792e-02 0.610 0.543112
mW.28759 -4.882e-02 3.116e-02 -1.567 0.119907
mW.28803 1.065e-02 3.471e-02 0.307 0.759562
mW.28986 -3.702e-03 1.133e-02 -0.327 0.744475
mW.30919 -3.991e-03 1.053e-02 -0.379 0.705334
mW.31047 5.306e-02 3.278e-02 1.619 0.108224
mW.37502 -1.261e-02 9.434e-03 -1.337 0.183833
mW.46809 -3.850e-03 5.822e-03 -0.661 0.509711
tG.508 -1.043e+01 1.576e+01 -0.662 0.509257
tG.552 1.440e+00 1.594e+00 0.903 0.368227
tG.2841 2.244e-02 1.600e-02 1.402 0.163673
tG.2884 -1.345e-02 1.155e-02 -1.165 0.246621
tG.21905 2.542e-03 7.543e-04 3.369 0.001026 **
tG.69510 3.096e-04 8.019e-05 3.860 0.000187 ***
tw.35578 -7.095e-05 7.569e-05 -0.937 0.350531
tw.46838 -6.802e-05 6.278e-05 -1.083 0.280920
tw.66592 -1.013e-01 5.640e-02 -1.796 0.075103 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2641 on 115 degrees of freedom
Multiple R-squared: 0.6064, Adjusted R-squared: 0.5345
F-statistic: 8.437 on 21 and 115 DF, p-value: 5.228e-15
>
build.linear.model( mmse[mmse.index], all[mmse.index,ind$non.zero])$anova
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
mW.14977 1 374.49 374.49 42.4923 1.578e-09 ***
mW.28802 1 92.72 92.72 10.5208 0.001514 **
mW.28803 1 4.47 4.47 0.5073 0.477654
mW.28986 1 33.64 33.64 3.8173 0.052959 .
mW.35808 1 11.54 11.54 1.3093 0.254716
mW.42139 1 17.54 17.54 1.9897 0.160853
mW.46853 1 20.00 20.00 2.2689 0.134513
tG.509 1 149.53 149.53 16.9667 6.868e-05 ***
tG.54723 1 171.01 171.01 19.4037 2.249e-05 ***
tG.78265 1 147.69 147.69 16.7577 7.567e-05 ***
tw.49090 1 59.98 59.98 6.8054 0.010196 *
Residuals 125 1101.63 8.81
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> summary( build.linear.model( mmse[mmse.index], all[mmse.index,ind$non.zero])$lm )
Call:
lm(formula = y ~ ., data = data.frame(y = y, x))
Residuals:
Min 1Q Median 3Q Max
-10.6100 -1.4960 0.4654 2.1175 5.2417
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.455e+01 6.280e-01 39.087 < 2e-16 ***
mW.14977 2.908e+00 2.157e+00 1.348 0.180084
mW.28802 1.457e-01 2.909e-01 0.501 0.617253
mW.28803 -1.121e-01 2.942e-01 -0.381 0.703862
mW.28986 1.236e-01 1.462e-01 0.845 0.399634
mW.35808 1.335e-02 1.730e-01 0.077 0.938631
mW.42139 3.368e-03 1.333e-01 0.025 0.979882
mW.46853 5.715e-02 6.197e-02 0.922 0.358186
tG.509 -1.902e+01 8.063e+00 -2.359 0.019872 *
tG.54723 -1.368e+01 3.580e+00 -3.823 0.000207 ***
tG.78265 -9.828e+02 2.409e+02 -4.079 8e-05 ***
tw.49090 8.518e-04 3.265e-04 2.609 0.010196 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.969 on 125 degrees of freedom
Multiple R-squared: 0.4956, Adjusted R-squared: 0.4513
F-statistic: 11.17 on 11 and 125 DF, p-value: 3.371e-14