RaModel {RaSEn}R Documentation

Generate data (x,y)(x, y) from various models in two papers.

Description

RaModel generates data from 4 models described in Tian, Y. and Feng, Y., 2021(b) and 8 models described in Tian, Y. and Feng, Y., 2021(a).

Usage

RaModel(model.type, model.no, n, p, p0 = 1/2, sparse = TRUE)

Arguments

model.type

indicator of the paper covering the model, which can be 'classification' (Tian, Y. and Feng, Y., 2021(b)) or 'screening' (Tian, Y. and Feng, Y., 2021(a)).

model.no

model number. It can be 1-4 when model.type = 'classification' and 1-8 when model.type = 'screening', respectively.

n

sample size

p

data dimension

p0

marginal probability of class 0. Default = 0.5. Only used when model.type = 'classification' and model.no = 1, 2, 3.

sparse

a logistic object indicating model sparsity. Default = TRUE. Only used when model.type = 'classification' and model.no = 1, 4.

Value

x

n * p matrix. n observations and p features.

y

n responses.

Note

When model.type = 'classification' and sparse = TRUE, models 1, 2, 4 require p5p \ge 5 and model 3 requires p50p \ge 50. When model.type = 'classification' and sparse = FALSE, models 1 and 4 require p50p \ge 50 and p30p \ge 30, respectively. When model.type = 'screening', models 1, 4, 5 and 7 require p4p \ge 4. Models 2 and 8 require p5p \ge 5. Model 3 requires p22p \ge 22. Model 5 requires p2p \ge 2.

References

Tian, Y. and Feng, Y., 2021(a). RaSE: A variable screening framework via random subspace ensembles. Journal of the American Statistical Association, (just-accepted), pp.1-30.

Tian, Y. and Feng, Y., 2021(b). RaSE: Random subspace ensemble classification. Journal of Machine Learning Research, 22(45), pp.1-93.

See Also

Rase, RaScreen.

Examples

train.data <- RaModel("classification", 1, n = 100, p = 50)
xtrain <- train.data$x
ytrain <- train.data$y

## Not run: 
train.data <- RaModel("screening", 2, n = 100, p = 50)
xtrain <- train.data$x
ytrain <- train.data$y

## End(Not run)

[Package RaSEn version 3.0.0 Index]