This document will serve as a "flow of thought" for how Ben and I decided to restructure the slidR
package. This work is from a brainstorming session on 8 September, 2017, due to the fact that I had a few days off from work at UM because of Irma.
slidR
PackageSubject matter experts in machine learning often look to reduce the complexity of their classification or attribution problems through linear dimension reduction (LDR). This package seeks to wrap up a few different LDR methods for classification, regression, and survival analysis. Currently, the lion's share of functional support is for classification, but we are adding other analysis patterns as time progresses.
slidR
We would like to establish a data analysis workflow for this package:
data
, reductMethod
, and projectionRoutine
.data
argument can take in the following:modelR
package)UseMethod
functionality to account for the data being in different classes. Classes we support are:data.frame
resample
matrix
tibble
(in progress)grouped_df
reductMethod
will govern how the function assumes the inputs should be discussed.reductMethod = LD
, then we assume the user is trying to classify observations and that the response is a grouping variable.reductionMethod
. These are different estimators of the data sufficiency (or M) matrix. These methods all return a projection matrix.LD
SY
SYS
SIR
from Li (1992)SAVE
from Cook and Weisberg (1992)PCA
(our version of the eigen
function from base R); use the sample covariance matrix as the M matrix estimateidentity
(use the data itself as its M matrix)projectRoutine
. These can be functions likeAdd the following code to your website.
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