classifly v0.4


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Explore classification models in high dimensions

Given $p$-dimensional training data containing $d$ groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-faceted. This package implements methods for understanding the division of space between the groups.

Functions in classifly

Name Description
classify Extract classifications from a variety of methods.
classifly Classifly provides a convenient method to fit a classification function and then explore the results in the original high dimensional space.
generate_classification_data Generate classification data.
simvar Simulate observations from a vector
variables Extract predictor and response variables for a model object.
knnf A wrapper function for knn to allow use with classifly.
advantage Calculate the advantage the most likely class has over the next most likely.
olives Olives
posterior Extract posterior group probabilities
generate_data Generate new data from a data frame.
explore Default method for exploring objects
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License MIT + file LICENSE
LazyData true
Roxygen list(wrap = FALSE)
Packaged 2014-04-23 16:59:08 UTC; hadley
NeedsCompilation no
Repository CRAN
Date/Publication 2014-04-23 19:51:22

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