OINT  R Documentation 
Association test that synthesizes the DINT
and
IINT
tests. The first approach is most powerful for traits that
could have arisen from a rankpreserving transformation of a latent normal
trait. The second approach is most powerful for traits that are linear in
covariates, yet have skewed or kurtotic residual distributions. During the
omnibus test, the direct and indirect tests are separately applied, then the
pvalues are combined via the Cauchy combination method.
OINT(
y,
G,
X = NULL,
k = 0.375,
ties.method = "average",
weights = c(1, 1),
simple = FALSE
)
y 
Numeric phenotype vector. 
G 
Genotype matrix with observations as rows, SNPs as columns. 
X 
Model matrix of covariates and structure adjustments. Should include an intercept. Omit to perform marginal tests of association. 
k 
Offset applied during ranknormalization. See

ties.method 
Method of breaking ties, passed to 
weights 
Respective weights to allocate the DINT and IINT tests. 
simple 
Return the OINT pvalues only? 
A numeric matrix of pvalues, three for each column of G
.
Basic association test BAT
.
Direct INT test DINT
.
Indirect INT test IINT
.
set.seed(100)
# Design matrix
X < cbind(1, rnorm(1e3))
# Genotypes
G < replicate(1e3, rbinom(n = 1e3, size = 2, prob = 0.25))
storage.mode(G) < "numeric"
# Phenotype
y < exp(as.numeric(X %*% c(1, 1)) + rnorm(1e3))
# Omnibus
p < OINT(y = y, G = G, X = X, simple = TRUE)
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