LASSO: LASSO for Rare Variant Tests

Description Usage Arguments Details Value Author(s) References See Also

Description

Use LASSO for selecting significant variants and testing the variants associated with disease traits.

Usage

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LASSO(x, y, family = c("gaussian", "binomial", "poisson", "multinomial", "cox"), 
	alpha = 1, nlambda = 100, lambda.min.ratio, standardize = TRUE, 
	size.max, a = 2, npermutation = 0, npermutation.max, min.nonsignificant.counts)

Arguments

x

Genotype matrix, each row as an individual and each column as a snp

y

Phenotype vector

family

Family: gaussian, binomial, poisson, multinomial, and cox

alpha

alpha = 1 for LASSO, see glmnet

nlambda

see glmnet

lambda.min.ratio

see glmnet

standardize

see glmnet

size.max

Maximum number of variants included

a

Penalty parameter for information criterion, a=2 for AIC.

npermutation

Number of permutation, if less than 1, the permutation will not be run.

npermutation.max

Maximum permutation

min.nonsignificant.counts

Minimum nonsignificant counts

Details

Use glmnet package to implement LASSO and an information criterion (AIC, BIC, or GIC) to select a set of variants.

Value

nonsignificant.counts

Counts of permuted data that have a higher score than unpermuted data.

pvalue.empirical

Empirical pvalue via permutation

pvalue.nominal

Not availabe

vs

The selected variants

total.permutation

Total permutation

family

Family

Author(s)

C. Xu

References

Xu C, Ladouceur M, Dastani Z, Richards JB, Ciampi A, Greenwood CMT. (2012) Multiple Regression Methods Show Great Potential for Rare Variant Association Tests. PLoS ONE 7(8): e41694. doi:10.1371/journal.pone.0041694

See Also

SPLS, glmnet


RVtests documentation built on May 1, 2019, 9:51 p.m.