Add final paper #148
Add final paper #148
Conversation
|
Need to edit the line limit to 80 characters |
| \begin{figure}[H] | ||
| \centering | ||
| \includegraphics[scale=0.55]{figures/processing.png} | ||
| \caption{DVARS, Framewise Displacement and Data mean for subject 2 run 2} |
There was a problem hiding this comment.
Please add to the caption on what are the green lines in dvars, from #107
There was a problem hiding this comment.
@changsiyao do you know what the green line is?
There was a problem hiding this comment.
isn't it just a least squared fitted curve of the data points?
see comments below
|
Green line in dvars and fd are threshold for outliers, 0.5 for both graph. I explained it in the outlier script. |
|
I know the green dotted line is the threshold for outliers, but what about the mean solid line in meanSignal plot? Is it a least squares fitted curve? |
|
The line in mean data is a fitted smooth curve of the bold signals. |
| \subsection{Behavioral analysis} | ||
|
|
||
| We performed statistical analysis using both Python and R (The original paper use R package to fit the Logistic models). We use the library \emph{scikit-learn} in Python and the \emph{glm} function in \emph{stats} in R to fit the models. Models from two library yields the same results. We shows the plot of the behavioral loss aversion $\lambda$ for every subject (median=1.94, mean=2.18, min=0.99, max=0.75). This result is consistent with that of the paper, which indicate that participants are indifferent to gambles whose gain are approximately twice as the loss. | ||
|
|
There was a problem hiding this comment.
Umm we dont use R to do any analysis right? Ie there is no R code involved. Should probabilty rephase this as using the statsmodels and scipy module of Python, delete the comment about us using R
There was a problem hiding this comment.
I think it doesn't matter to mention this. We do use R to do the exploratory analysis.
There was a problem hiding this comment.
lol if we did we need to include the R scipts as supplementary material
|
Can we merge this now and then do the revision? cuz the travis really takes a long time for this pull request. |
|
Forget about travis. It might be broken. Matthew posted that on piazza |
|
OK. Sorry I didn't saw that on piazza. |
|
|
||
| \begin{itemize} | ||
| \item \emph{Accuracy on the training dataet} We uses the fitted models on the original dataets and compared the estimated class and the true class using the Logistic classifier. The accuracy of Logistic models (for 16 participants, 16 models in total) on the training set yielded a median of 89.78\% (min=80.97\%, max=99.21\%). | ||
| \item \emph{Cross-validation} We did the model evaluation using 10-fold cross-validation for every subject, they are still performing accuracies of a median of 89.86\% (min=79.92\%, max=98.45\%). |
There was a problem hiding this comment.
Can we specify what accruacy means? see ross' comments #107
There was a problem hiding this comment.
accuracy is explained in the model part
|
On your next commit message, include |
|
@boyinggong is it ok to merge? |
|
Yes. I've fixed problems you mentioned. |
|
Should we delete the section folder? |
No description provided.