Evoxploit: Object that contains original data and derived evolution...

In unmnn/evoxploit: Augment Evolution Features to a Longitudinal Study Dataset

Description

Evoxploit computes evolution features on longitudinal data.

Format

R6Class object.

Usage

evo <- Evoxploit$new(data, label, wave_suffix = "_s", minPts = NULL,
eps = NULL, run = TRUE, verbose = FALSE)

summary(evo)
evo$data
evo$label
evo$evo_features
evo$all_features
evo$clu

Arguments

For Evoxploit$new():

data:

('data.frame')
The data set with all input features (predictors). The object (created with Predictor$new()) holding the machine learning model and the data.

target:

('factor' | 'numeric')
The target variable (response).

wave_suffix:

('character(1)')
The wave suffix given as string.

minPts:

('integer(1)')
(optional) minPts parameter for DBSCAN clustering.

eps:

('double(1)')
(optional) eps parameter for DBSCAN clustering.

run:

('logical(1)')
Should the (evo) features extraction method be run?

verbose:

('logical(1)')
Whether or not to show some diagnostic messages. Defaults to FALSE.

Methods

Public methods

• Evoxploit$new()

• Evoxploit$run()

• Evoxploit$summary()

• Evoxploit$clone()

---

Method new()

Usage

Evoxploit$new(
  data,
  label,
  wave_suffix = "_s",
  minPts = NULL,
  eps = NULL,
  train_lgc = rep(TRUE, nrow(data)),
  run = TRUE,
  verbose = FALSE
)

---

Method run()

Usage

Evoxploit$run(verbose = FALSE)

---

Method summary()

Usage

Evoxploit$summary()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Evoxploit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

unmnn/evoxploit documentation built on Oct. 28, 2020, 12:24 p.m.