gendataPaper: Simulation Scenario from Bhatnagar et al. (2018+) sail paper
In sahirbhatnagar/sail: Sparse Additive Interaction Learning

Description Usage Arguments Details Value See Also

generates the different simulation scenarios. This function is not intended to be called directly by users. See gendata

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gendataPaper(n, p, corr = 0, E = truncnorm::rtruncnorm(n, a = -1, b =
  1), betaE = 2, SNR = 2, hierarchy = c("strong", "weak", "none"),
  nonlinear = TRUE, interactions = TRUE, causal, not_causal)

`n`	number of observations
`p`	number of main effect variables (X)
`corr`	correlation between predictors
`E`	simulated environment vector of length `n`. Can be continuous or integer valued. Factors must be converted to numeric. Default: `truncnorm::rtruncnorm(n, a = -1, b = 1)`
`betaE`	exposure effect size
`SNR`	signal to noise ratio
`hierarchy`	type of hierarchy. Can be one of `c("strong", "weak", "none")`. Default: "strong"
`nonlinear`	simulate non-linear terms (logical). Default: TRUE
`interactions`	simulate interaction (logical). Default: TRUE
`causal`	character vector of causal variable names
`not_causal`	character vector of noise variables

Requires installation of truncnorm package. Not meant to be called directly by user. Use gendata.

A list with the following elements:

x: matrix of dimension nxp of simulated main effects
y: simulated response vector of length n
e: simulated exposure vector of length n
Y.star: linear predictor vector of length n
f1: the function f1 evaluated at x_1 (f1(X1))
f2: the function f1 evaluated at x_1 (f1(X1))
f3: the function f1 evaluated at x_1 (f1(X1))
f4: the function f1 evaluated at x_1 (f1(X1))
betaE: the value for β_E
f1.f: the function f1
f2.f: the function f2
f3.f: the function f3
f4.f: the function f4
X1: an n length vector of the first predictor
X2: an n length vector of the second predictor
X3: an n length vector of the third predictor
X4: an n length vector of the fourth predictor
scenario: a character representing the simulation scenario identifier as described in Bhatnagar et al. (2018+)
causal: character vector of causal variable names
not_causal: character vector of noise variables