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Get Marginal Effects

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

Extract per-variant marginal univariate effects from a fine-mapping entry or result. Returns a data.frame with identity columns (variant_id, chrom, pos, A1, A2), context (N, MAF), and the marginal effect columns (beta, se, z, p). Populated uniformly across the individual-level and RSS paths.

Usage

marginalEffects(x, maxPval = NULL, ...)

# S4 method for class 'FineMappingResultBase'
marginalEffects(
  x,
  maxPval = NULL,
  studyName = NULL,
  context = NULL,
  trait = NULL,
  method = NULL,
  region = NULL
)

Arguments

x

A QtlFineMappingResult or GwasFineMappingResult.

maxPval

Optional numeric (length 1). When non-NULL, filter rows where p > maxPval. Default NULL (no filter).

...

Class-specific selection arguments.

studyName

Character (length 1) or NULL. Restrict the selection to this study; NULL matches all studies.

context

Character (length 1) or NULL. Restrict the selection to this context; NULL matches all contexts.

trait

Character (length 1) or NULL. Restrict the selection to this trait; NULL matches all traits.

method

Character (length 1) or NULL. Restrict the selection to this fine-mapping / weight method; NULL matches all methods.

region

Character (length 1, "chr:start-end") or NULL. Restrict variants to this region; NULL uses the full cis window / all regions.

Value

A data.frame.

Examples

data(qtlFineMappingExample)
marginalEffects(qtlFineMappingExample)
#> # A tibble: 2,828 × 20
#>    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 12 more variables: variant_id <chr>, chrom <chr>, pos <int>, A1 <chr>,
#> #   A2 <chr>, N <dbl>, af <dbl>, beta <dbl>, se <dbl>, z <dbl>,
#> #   z_supplied <lgl>, p <dbl>