splitSNPs

This function takes a matrix or data frame of SNPs coded as
0, 1, 2 or 1, 2, 3 and returns a data frame with twice as many
columns. SNPs are splitted into dominant and recessive modes,
i.e., for a \(\mathrm{SNP} \in \lbrace 0,1,2 \rbrace\), two variables
\(\mathrm{SNP}_D = (\mathrm{SNP} \neq 0)\) and
\(\mathrm{SNP}_R = (\mathrm{SNP} = 2)\) are generated.

A statistical learning method that tries to find the best set
of predictors and interactions between predictors for modeling binary or
quantitative response data in a decision tree. Several search algorithms
and ensembling techniques are implemented allowing for finetuning the
method to the specific problem. Interactions with quantitative
covariables can be properly taken into account by fitting local
regression models. Moreover, a variable importance measure for assessing
marginal and interaction effects is provided. Implements the
procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

Michael Lau

logicDT

Identifying Interactions Between Binary Predictors

splitSNPs function

<dl><dt>data</dt>
<dd>A matrix or data frame only consisting of SNPs to
be splitted</dd></dl>

Arguments

Split biallelic SNPs into binary variables — splitSNPs

<dl>

<dt>data</dt>
<dd>A matrix or data frame only consisting of SNPs to
be splitted</dd>

</dl>

splitSNPs: Split biallelic SNPs into binary variables

Description

Usage

Value

Arguments