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Figure out how to deal with categorical variables
Implement robust distance metrics
use iris data set for testing
Issues 12/16
- Color scheme setting each page?
matrix completion
find some articles to read about it, i'll help you filter them.
laura balzano has written some stuff
rob nowak
becca willet
ben recht
are the most readable ones i know.
For each of the tabs, add separate scaling and visualization drop downs
Issues 12/7
- Something like scree for tsne
- Better caching for tsne
- Find std of R-scores
- Allow for boxplot scaling
- Consider using scalefillbrewer(type = 'div') for heatmaps
- Scree plots look wrong
- Add kmeans++ clustering
- Check if outliers removed from embed
Fix tsne
Implement reactive values
Update scree plot with each new pca
test
- 1
- asdf
- [x]
tell people the appropriate format
eg
- nan's/infs break code
- rows should be data points, columns should be features (or vice versa)
- appropriate size for things to work
Error in outlier analysis. "Row names" not found.
Shared color scheme. Allow user to select color scheme.
Same scaling options as for heatmap in distance metric
tweaks
- on "Data" tab, show: # features, # samples, nnz, # NaN
- on "Marginal" tab, make "combined" the default.
- throughout, rename tabs to be more informative
- "Distance Metric" = "Euclidean Distance"?
- fix size of correlation matrix
- mean vector plotting doesn't really have scatter, probably we want 'jittered scatter'
- add iris.csv file for demo purposes, you can leave the other one, maybe name it something more informative.
deal with NaN's and Inf's eloquently
not sure how.
play with how matlab does it for importing.
we can use default R impute options as one option?
clustering
- cluster on embedded points.
- choose # of dimensions via Zhu & Godsi: https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/automatic-dimensionality-selection-from-scree-plot-use-of-profile-likelihood.pdf (getElbow.R lives somewhere, ask greg or youngser for it)
- we can later enable user to choose the dimension of embedding.
In mean vector gives options for scaling data
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