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

Source: R/AllGenerics.R, R/AllClasses.R
lbf.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 topLoci; this accessor keeps the full per-effect breakdown. Across entries with different effect counts the collection method NA-fills the ragged columns.

Usage

lbf(x, ...)

# S4 method for class 'FineMappingResultBase'
lbf(x)

Arguments

x

A QtlFineMappingResult or GwasFineMappingResult.

...

Ignored.

Value

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

See also

topLoci (the scalar logBF column)

Examples

data(qtlFineMappingExample)
lbf(qtlFineMappingExample)
#> # A tibble: 2,828 × 11
#>    study   context   trait  blockId method dataType dataSource standardized
#>    <chr>   <chr>     <chr>  <chr>   <chr>  <chr>    <chr>      <chr>       
#>  1 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  2 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  3 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  4 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  5 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  6 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  7 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  8 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  9 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#> 10 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#> # ℹ 2,818 more rows
#> # ℹ 3 more variables: variant_id <chr>, lbf_L1 <dbl>, lbf_L2 <dbl>