SNP_reduction: SNP pre-processing.
In LaiJiang/CIVMR: Constrained Instrumental Variables in Mendelian Randomization with Pleiotropy
Description
Usage
Arguments
Value
Examples
View source: R/CIVMR_function.R
Description
This function remove highly correlated SNPs. It also
calculate MAF for each snp and delete rare snps with low MAF (e.g. 0.01).
Usage
1
SNP_reduction(snp_matrix, crit_high_cor = 0.8, maf_crit = 0.01)
Arguments
snp_matrix:
SNP matrix with dimension n * p.
crit_high_cor:
criteria to choose highly correlated SNPs.
maf_crit:
criteria to choose rare SNPs.
Value
sel_snp: the new dosage matrix of selected SNPs.
id_snp: the ids (columns) of selected SNPs in the original SNP matrix.
Examples
1
2
data(simulation)
snp.rdc <- SNP_reduction(simulation$G)
LaiJiang/CIVMR documentation built on July 16, 2020, 12:45 a.m.
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Description Usage Arguments Value Examples
View source: R/CIVMR_function.R
Description
This function remove highly correlated SNPs. It also calculate MAF for each snp and delete rare snps with low MAF (e.g. 0.01).
Usage
1 | SNP_reduction(snp_matrix, crit_high_cor = 0.8, maf_crit = 0.01)
|
Arguments
snp_matrix: |
SNP matrix with dimension n * p. |
crit_high_cor: |
criteria to choose highly correlated SNPs. |
maf_crit: |
criteria to choose rare SNPs. |
Value
sel_snp: the new dosage matrix of selected SNPs.
id_snp: the ids (columns) of selected SNPs in the original SNP matrix.
Examples
1 2 | data(simulation)
snp.rdc <- SNP_reduction(simulation$G)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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