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Across two large Internet-based studies (n = 610), in line with predictions, image selections accentuated favorable social impressions and these impressions were aligned to the social context of the networking sites.
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These were then used to predict a new set of data which were refolded to form the prediction image, showing the location of the classes.
Intersections between the prediction image and total landslide locations allowed us to compute the number of occurred landslides in each LSI values.
Using the swagger service and the free molecule editor JSME [34], we also created a web-based user interface where a prediction image is rendered continuously as a molecule is edited (http://predict-cplogd.os.pharmb.io/ [35]).
Occasionally, a model has an unsatisfactory RMSEP, but still shows acceptable prediction results in the prediction image for a large number of pixels.
Global diagnostics for the PLS-DA models such as R and RMSEP serve as a guide of modelling accuracy for images because of the large number of pixels and should be used in conjunction with the prediction image.
The optimum number of PLS-DA components was selected based on the number of components that resulted in the highest R, lowest RMSEP and prediction image with best classification of the samples (highest percentage correctly classified pixels).
Prediction images were then compared to the actual post-treatment profile photos to determine differences.
Prediction images for the test sets show the accuracy of the PLS-DA models.
For the PLS-DA models, prediction images are shown for the training set to illustrate how well the respective models performed.
The prediction images for F. subglutinans (green) and F. proliferatum (blue) showed a reasonable number of correctly predicted pixels for both the training and test sets (Fig. 5).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com