RVtests (version 1.0)

SPLS: Sparse PLS for RV Tests

Description

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

Usage

SPLS(x, y, scale = TRUE, ncomp, eta.grid, size.max, a = 2, npermutation = 0, npermutation.max, min.nonsignificant.counts)

Arguments

x

Genotype matrix

y

Phenotype vector

scale

see spls

ncomp

Number of components

eta.grid

see spls

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, if missing, equal to npermutation.

min.nonsignificant.counts

Minimum nonsignificant counts, if missing, equal to 10.

Value

• nonsignificant.counts
  Counts of permuted data that have a higher score than unpermuted data.
• pvalue.empirical
  Empirical pvalue via permutation
• pvalue.nominal
  Theoretical pvalue for the selected variants
• vs
  The selected variants
• total.permutation
  Total permutation

Details

Use spls package to implement SPLS and an information criterion, AIC, BIC, or GIC, to select the best subset of variants.

References

C. Xu, M. Ladouceur, Z. Dastani, J. B. Richards, A. Ciampi, C. M.T. Greenwood (2012), Multiple regression methods show great potential for rare variant association tests, PLoSONE.

See Also

spls, LASSO