Data Class

Class for storing data and various fixed quantity

Public fields

X

matrix of regressor

y

vector of response

C_inv

Inverse of the Cholesky decomposition of S

S

SDP structuring matrix

wy

vector of observation weights

Active bindings

d

number of regressor

n

sample size

sparse_encoding

logical indicating if the matrix of regressor is sparsely encoded

varnames

character, the names of the covariates/regressors

normx

norm of each column of X

Methods

Public methods

• DataModel$new()

• DataModel$CholStruct()

• DataModel$splitTrainTest()

• DataModel$splitSubSamples()

• DataModel$clone()

---

Method new()

constructor for DataModel

Usage

DataModel$new(
  covariates,
  outcome,
  cov_struct,
  obs_weights = rep(1, length(outcome)),
  check_args = TRUE
)

Arguments

covariates

matrix of covariates/regressors

outcome

vector of outcome/response

cov_struct

sdp matrix structuring the covariates/regressors

obs_weights

vector of observations weights

check_args

logical, should args be check at initialization?

cov_weights

vector of covariates/regressors weights

---

Method CholStruct()

Compute Cholesky factorization of the Structuring matrix

Usage

DataModel$CholStruct()

---

Method splitTrainTest()

a function splitting the data into train and test folds

Usage

DataModel$splitTrainTest(
  nfolds = 10,
  folds = split(sample(1:self$n), rep(1:nfolds, length = self$n))
)

Arguments

nfolds

the number of folds

folds

a list of vectors describing the folds (optional)

Returns

a list with train and test data and id.

---

Method splitSubSamples()

a function splitting data into subsamples

Usage

DataModel$splitSubSamples(
  n_subsamples = 50,
  subsample_size = floor(self$n/2),
  subsamples = replicate(n_subsamples, sample(1:self$n, subsample_size), simplify =
    FALSE),
  weakness = 1
)

Arguments

n_subsamples

the number of subsamples

subsample_size

the subsample size

subsamples

list with vector of subsamples (optional)

weakness

coefficient for randonly reweighting the regressor, default to 1

Returns

a list of DataModel, resampling of the original

---

Method clone()

The objects of this class are cloneable with this method.

Usage

DataModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.