gdataM: Simulates data from an observational study according to the...
In jinjinzhou/ITR.Tree: Building an ITR Tree

Description Usage Arguments Value Examples

View source: R/gdataM.R

Simulates data from an observational study according to the following model: 1 + 2*X2 + 4*X4 + beta1*trt*subgrp + beta2*(1-trt)*(1-subgrp) + N(0,1) where subgrp is the group of interacting variable(s). If depth=1, then subgrp=(X1 < 0.5) If depth!=1 then subgrp=(X1>0.3 & X3>0.1)

1	gdataM(n, depth, beta1, beta2)

`n`	size of the dataset to be generate. Required input.
`depth`	gives the number of interacting covariates. If set to 1, then then covariate X1 interacts with treatment. If set to another value, then covariates X1 and X3 both interact with treatment effect (one-way interactions). Required input.
`beta1`	controls the strength of the treatment effect. Required input.
`beta2`	controls the strength of the noise. Required input.

dataframe containing y (outcome), X1-X4 (covariates), trt (treatment), prtx (probability of being in treatment group)

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data<-gdataM(n=500, depth=1, beta1=1, beta2=2)
This generates a dataframe with 500 observations, X1 as the only variable interacting with 
the treatment, and a signal to noise ratio of 1/2.