View source: R/allelic_series.R
ASBT | R Documentation |
Burden test with allelic series weights.
ASBT(
anno,
geno,
pheno,
apply_int = TRUE,
covar = NULL,
indicator = FALSE,
is_pheno_binary = FALSE,
method = "none",
min_mac = 0,
return_beta = FALSE,
score_test = FALSE,
weights = c(1, 2, 3)
)
anno |
(snps x 1) annotation vector with integer values in 1 through the number of annotation categories L. |
geno |
(n x snps) genotype matrix. |
pheno |
(n x 1) phenotype vector. |
apply_int |
Apply rank-based inverse normal transform to the phenotype? Default: TRUE. Ignored if phenotype is binary. |
covar |
(n x p) covariate matrix. Defaults to an (n x 1) intercept. |
indicator |
Convert raw counts to indicators? |
is_pheno_binary |
Is the phenotype binary? Default: FALSE. |
method |
Method for aggregating across categories: ("none", "max", "sum"). Default: "none". |
min_mac |
Minimum minor allele count for inclusion. Default: 0. |
return_beta |
Return the estimated effect size? Default: FALSE. |
score_test |
Run a score test? If FALSE, performs a Wald test. |
weights |
(L x 1) vector of annotation category weights. Note that the
number of annotation categories L is inferred from the length of |
If return_beta = TRUE
, a list of including the effect size
data.frame "betas" and the p-value "pval". If return_beta = FALSE
,
a numeric p-value.
# Generate data.
data <- DGP(n = 1e3, snps = 1e2)
# Run the Allelic Series Burden Test.
# Note: the output is a scalar p-value.
results <- ASBT(
anno = data$anno,
geno = data$geno,
pheno = data$pheno,
covar = data$covar
)
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