Description Usage Arguments Value References Examples
In the modified outcome approach, we estimate the risk difference
E[Y|A=1,X]-E[Y|A=0,X].
The risk difference measures the synergy between A
and the set of
covariates in X
. For genome-wide association studies, it can be
interpreted as a pure epistatic term. However, for a single sample, we only
observe one of the two possibilities A=1 or A=0, making the direct
estimate of the risk difference impossible. Through propensity scores,
modified outcome was proposed as a solution to this problem. The risk
difference is recovered by constructing a modified outcome that combines
A, Y and the propensity score P(A|X):
Y x [A/P(A=1|X) -(1-A)/P(A=0|X)].
The use of stabilityGLM
or stabilityBIG
for
the modified outcome regression allows us to recover the
interacting components within X
.
1 | modified_outcome(A, X, Y, propensity, parallel = FALSE, ...)
|
A |
target variant |
X |
rest of the genotype |
Y |
phenotype |
propensity |
propensity scores vector/matrix. If given as a matrix, the first column is P(A = 0|X) while the second is P(A = 1|X) |
parallel |
whether to perform support estimation in a
parallelized fashion with the |
... |
additional arguments to be passed to |
a vector containing the area under the stability selection path for
each variable in X
Rosenbaum, Paul R., and Donald B. Rubin. 'The central role of the propensity score in observational studies for causal effects.' Biometrika 70.1 (1983): 41-55.
1 2 3 4 5 6 7 8 | n <- 30
p <- 10
X <- matrix((runif(n * p) < 0.5) + (runif(n * p) < 0.5), ncol = p, nrow = n)
A <- (runif(n, min = 0, max = 1) < 0.3)
propensity <- runif(n, min = 0.4, max = 0.8)
Y <- runif(n) < 1/ (1 + exp(- 2 * X[, 5, drop = FALSE]))
auc_scores <- modified_outcome(A, X, Y, propensity,
ncores = 1, parallel = TRUE, n_subsample = 1)
|
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