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Per-variant per-effect log Bayes factors (wide)

Source: R/AllGenerics.R, R/AllClasses.R
getLbf.Rd

The variant x effect matrix of single-effect log Bayes factors (lbf_variable, or fSuSiE's lBF) from the stored fit: one row per variant, one lbf_L<k> column per effect. The per-variant scalar summary (max across effects) is the logBF column of getTopLoci; this accessor keeps the full per-effect breakdown. Across entries with different effect counts the collection method NA-fills the ragged columns.

Usage

getLbf(x, ...)

# S4 method for class 'FineMappingResultBase'
getLbf(x, ...)

Arguments

x

A FineMappingRow or FineMappingResultBase.

...

Ignored.

Value

A tibble: variant_id + lbf_L1..lbf_LL (the collection method also carries the entry identity columns).

See also

getTopLoci (the scalar logBF column)

Examples

data(qtlFineMappingExample)
getLbf(qtlFineMappingExample)
#> # A tibble: 2,828 × 8
#>    study   context   trait  blockId method variant_id         lbf_L1 lbf_L2
#>    <chr>   <chr>     <chr>  <chr>   <chr>  <chr>               <dbl>  <dbl>
#>  1 study_1 context_1 gene_1 NA      susie  chr22:32119788:T:C -0.765 -0.134
#>  2 study_1 context_1 gene_1 NA      susie  chr22:32119867:T:G -0.765 -0.134
#>  3 study_1 context_1 gene_1 NA      susie  chr22:32119961:T:G -1.35  -0.825
#>  4 study_1 context_1 gene_1 NA      susie  chr22:32120053:T:C -1.78  -1.04 
#>  5 study_1 context_1 gene_1 NA      susie  chr22:32120593:A:G -1.78  -1.04 
#>  6 study_1 context_1 gene_1 NA      susie  chr22:32120636:T:C -1.59  -0.881
#>  7 study_1 context_1 gene_1 NA      susie  chr22:32120932:G:A -1.49  -0.765
#>  8 study_1 context_1 gene_1 NA      susie  chr22:32120975:A:G -1.41  -0.712
#>  9 study_1 context_1 gene_1 NA      susie  chr22:32121290:C:T -1.68  -0.957
#> 10 study_1 context_1 gene_1 NA      susie  chr22:32121498:A:C -1.68  -0.957
#> # ℹ 2,818 more rows