Description Usage Format Source Examples
Simulated (non-hierarchical) dataset containing 10,000 i.i.d. observations, with each row i
consisting of measured baseline
covariates (W1
, W2
, W3
and W4
), continuous exposure (A
) and continous outcome (Y
).
The baseline covariates W1
, W2
, W3
and W4
were sampled as i.i.d., while the value of exposure A
for each observation i
was drawn conditionally on the value of i
's four baseline covariates. Besides, the continuous
outcome Y
for each observation depends on i
's baseline covariates and exposure values in (W1[i]
,W2[i]
,
W3[i]
, W4[i]
, A[i]
). The following section provides more details regarding individual variables in simulated
data.
1 |
A data frame with 10,000 independent observations (rows) and 6 variables:
binary baseline covariate with P(W1=1) = 0.5
binary baseline covariate with P(W2=1) = 0.3
continuous normal baseline covariate with μ = 0 and σ = 0.25
continuous uniform baseline covariate with min=0
and max=1
continuous normal exposure where its mean depends on individual's baseline covariate values in (W1, W2, W3, W4)
continuous normal outcome where its mean depends on individual's baseline covariate and exposure values in (W1
,
W2
, W3
, W4
, A
)
https://github.com/chizhangucb/tmleCommunity/blob/master/tests/dataGeneration/get.iid.dat.Acont.R
1 2 3 4 5 6 7 8 9 10 | data(indSample.iid.cA.cY_list)
indSample.iid.cA.cY <- indSample.iid.cA.cY_list$indSample.iid.cA.cY
# True mean of outcome under intervention g0
psi0.Y <- indSample.iid.cA.cY_list$psi0.Y
# True mean of outcoem under stochastic intervention gstar
psi0.Ygstar <- indSample.iid.cA.cY_list$psi0.Ygstar
# truncated bound used in sampling A* under gstar (in data generating mechanism)
indSample.iid.cA.cY_list$truncBD
# shift value used in sampling A* under gstar
indSample.iid.cA.cY_list$shift.val
|
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