p0tos1: Finds the LASSO parameter s1 that corresponds to desired...
In ldlasso: LD LASSO Regression for SNP Association Study

Description Usage Arguments Value Author(s) See Also

Estimates the LASSO parameter that corresponds to desired false positive rate. More specifically, it is a bisection algorithm designed to find an s1 that corresponds to p0 nonzero estimates from the ld lasso with a permuted phenotype vector.

1	p0tos1(p0, block.obj, Xa = NA, Y = NA, r2.cut = 0.01, s1high, s1low, max.iter = 100, tol = 0.1)

`p0`	The number of nonzero estimates in permuted model. If the expected false positive rate is set at 0.10, then i = 0.10*p, where p is the number of SNPs.
`block.obj`	An object of class gwaa.data from GenABEL.
`Xa`	If block.obj is NA then a genotype matrix must be provided. Xa is a matrix of genotype values codes as 0, 1 or 2 for homozygous major, heterozygous, or homozygous minor, respectively.
`Y`	If block.obj is NA then a phenotype vector Y must be provided. Y is a vector of diagnoses, where 0 is non-diseased and 1 is diseased.
`r2.cut`	The value for the cutoff value of r-squared. Can be found with r2.cut.fn.
`s1high`	The initial upper limit in the bisection algorithm
`s1low`	The initial lower upper limit in the bisection algorithm
`max.iter`	The maximum number of iterations allowed in the bisection algorithm before NA is returned
`tol`	If p never equals i the bisection algorithim stops when \|s1.old - s1.new\| < tol. Otherwise algortithm stops when p =i.