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Feature Selector
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Clean
Filter
Missing Values
Odds Ratios
GLM
sPLS-DA
Multi-Block sPLS-DA
Final GLM
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Overview of the source data
For a numeric looking variable to be considered as categorical
Maximal number (included) of levels:
Maximal number (included) of digits:
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Automatic changes to the data
Read it attentively and modify heuristics to obtain the data that makes sense
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Filter rows (observations)
for reponse variables
Remove Filter
Add Filter
for predictors
Apply same filters
Filter columns (variables)
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Overview of the selected data
Visualize NA's
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Stringent
Maximal tolerated % of NA by row:
Maximal tolerated % of NA by variable (column) :
Imputation
Select imputation method:
k Nearest Neighbours
Central Tendency
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Stringent
Worst variables after stringent
Imputation effect on correlations
Overview of Imputed Data
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Save imputation histograms (it may take a lot of time to generate)
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Choose the significance level:
Forest Plot
OR by variables
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Impose alpha
Standardize dummies
Choose the number of bootstraps:
Choose the oversampling:
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Choose the threshold bootstrap frequency for the plot:
Bootsrapped plot
Bootsrapped FS table
FS on the original data
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Choose cut-offs for graphics
Feature stability threshold:
PCA
Regular PLS-DA
Optimal Parameters
sPLS-DA
Feature Stability
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Ellipses
Biplot
Interactive
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Ellipses
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Ellipses
Biplot
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Choose cut-offs for graphics
PLS correlation circle:
DIABLO:
Feature stability threshold:
Circos:
Pairwise PLS
Optimal Parameters
DIABLO
Feature Stability
Block Interactions
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Ellipses
Biplot
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GLM
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Add Confidence Intervals
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