make_classification: Data Simulation for single stage

Description

It gives simulated data. The outcomes are generated based on a pattern mixture model using a latent variable with 4 categories. For each category, X has a multivariate normal distribution and each category is assigned a vector of optimal treatments y. Specifically, we generate centroids of the classes from a multivariate normal distribution mean 0 and std 5. We add the centroids to the first pinfo dimension of the vectors of feature variables X simulated from multivariate normal distribution with pinfo+pnoise dimensions.

Then we assign optimal treatments y to each latent category. The observed treatment assignments A are completely random to be 1 and -1 with probability 0.5, and the outcomes are generated as: $R_1=0, R_2= Ay+N(0,1)$.

Usage

make_classification(n_cluster, pinfo, pnoise, n_sample, centroids = 0)

Arguments

n_cluster

number of cluster.

pinfo

number of infomative variables, dimentions of the centroids related to the latent class of the sample.

pnoise

number of noise variable.

n_sample

number of sample to generate

centroids

For a training set, donot assign centroids, this value is generated randomly by the function. For a testing set, one want to assign the same set of centroids as the training set. it is a matrix of dimention n_cluster by p.

Value

XThe feature variable matrix, it is a n_sample by pinfo+pnoise matrix generated from multivariate normal distribution. Where the noises are with mean 0 and std 1. The informative variables are shifted to centered at the randomly generate centroids.
AThe treatment assignment vector
yThe true optimal treatment
ROutcomes vector
centroidsAre from pinfo dimentional multivariate normal distribution.

References

This function borrows idea from a python comparable function make_classification in scikit_learn http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification

Examples

Run this code

n_cluster=10
pinfo=10
pnoise=20
example1=make_classification(n_cluster,pinfo,pnoise,100)
test=make_classification(n_cluster,pinfo,pnoise,100,example1$centroids)
model1=Olearning_Single(example1$X,example1$A,example1$R)
Atp=predict(model1,test$X)
V1=mean(test$R[Atp==test$A])

model2=wsvm(example1$X,example1$A,example1$R,'rbf',0.05)
Atp=predict(model2,test$X)
V2=mean(test$R[Atp==test$A])