Description Usage Arguments Details Value References Examples
To recover the synergistic interactions between the target A
and the
rest of the genotype X
, OWL
formulates a weighted binary
classification problem. The outcome is the mapping of A
to {0,1}. The
covariates are X
. The propensity scores and the phenotypes are
combined in the sample weights Y/P(A|X). For binary
phenotypes, OWL is a case-only approach. The approach also accommodates
nonnegative continuous phenotypes.
1 | OWL(A, X, Y, propensity, ...)
|
A |
target variant. If not binary, the variable A must be encoded as either (0, 1) or (0, 1, 2). |
X |
rest of the genotype |
Y |
phenotype (binary or continuous) |
propensity |
propensity scores (a vector or a two-column matrix) |
... |
additional arguments to |
For continuous phenotypes, if the outcome Y
is
not nonnegative, it is translated to make it nonnegative.
a vector containing the area under the stability selection path for
each variable in X
Zhao, Y., Zeng, D., Rush, A. J., & Kosorok, M. R. (2012). Estimating Individualized Treatment Rules Using Outcome Weighted Learning. Journal of the American Statistical Association, 107(499), 1106–1118.
1 2 3 4 5 6 7 | 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, min = 0, max = 1) < 1/ (1 + exp(-X[, c(1, 7)] %*% rnorm(2)))
OWL(A, X, Y, propensity, short = FALSE, n_lambda = 50, n_subsample = 1)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.