Get response quantiles

Usage

resp_quantiles(
  resp,
  metric = c("GCA.Eff", "IA.Eff", "GCA.HQ.10", "IA.HQ.10"),
  assays = NULL,
  assay_summary = FALSE,
  assay_quantiles = c(Median = 0.5),
  summary_quantiles = c(`10th percentile` = 0.1)
)

Arguments

resp: calculated mixture response output from calc_concentration_response.
metric: response metric, one of "GCA.Eff", "IA.Eff", "GCA.HQ.10" or "IA.HQ.10".
assays: assays to summarize. If NULL and multiple assays exist, then the first assay is used.
assay_summary: boolean indicating whether to summarize across assays.
assay_quantiles: numeric vector of assay quantiles.
summary_quantiles: numeric vector of quantiles to compute across all assay quantiles.

Value

data frame with computed response quantiles.

Details

The columns of the returned data frame will vary based on the inputs. If assays is specified and assay_summary is FALSE, then the resulting data frame will have an assay column. If assay_summary is TRUE, then the data frame will have an summary_quantile column.

Examples

# Dummy response data
resp <- list(
  "r1" = data.frame(assay = c("a1", "a1", "a2", "a2"),
                    sample = c(1, 2, 1, 2),
                    GCA.Eff = c(1, 2, 3, 4),
                    IA.Eff = c(5, 6, 7, 8),
                    "GCA.HQ.10" = c(9, 10, 11, 12),
                    "IA.HQ.10" = c(13, 14, 15, 16)))

# Summarize single assay
resp_quantiles(resp)
#> Warning: Multiple assays found, using first assay 'a1'
#> # A tibble: 1 × 5
#>   id    assay metric  assay_quantile value
#>   <chr> <chr> <chr>            <dbl> <dbl>
#> 1 r1    a1    GCA.Eff            0.5   1.5
# Specify assay
resp_quantiles(resp, assays = "a1")
#> # A tibble: 1 × 5
#>   id    assay metric  assay_quantile value
#>   <chr> <chr> <chr>            <dbl> <dbl>
#> 1 r1    a1    GCA.Eff            0.5   1.5
# Specify quantiles
resp_quantiles(resp, assays = "a1", assay_quantiles = c(0.25, 0.75))
#> # A tibble: 2 × 5
#>   id    assay metric  assay_quantile value
#>   <chr> <chr> <chr>            <dbl> <dbl>
#> 1 r1    a1    GCA.Eff           0.25  1.25
#> 2 r1    a1    GCA.Eff           0.75  1.75
# Specify metric
resp_quantiles(resp, assays = "a1", metric = "IA.HQ.10")
#> # A tibble: 1 × 5
#>   id    assay metric   assay_quantile value
#>   <chr> <chr> <chr>             <dbl> <dbl>
#> 1 r1    a1    IA.HQ.10            0.5  13.5

# Summarize across assays
resp_quantiles(resp, assay_summary = TRUE)
#> Warning: There are only 2 assays.
#> Consider using a larger number of assays for a more robust analysis.
#> # A tibble: 1 × 5
#>   id    assay_quantile metric  summary_quantile value
#>   <chr>          <dbl> <chr>              <dbl> <dbl>
#> 1 r1               0.5 GCA.Eff              0.1   1.7
# Specify quantiles
suppressWarnings(
  resp_quantiles(resp,
                 assay_summary = TRUE,
                 assay_quantiles = c(0.25, 0.75),
                 summary_quantiles = c(0.1, 0.9))
)
#> # A tibble: 4 × 5
#>   id    assay_quantile metric  summary_quantile value
#>   <chr>          <dbl> <chr>              <dbl> <dbl>
#> 1 r1              0.25 GCA.Eff              0.1  1.45
#> 2 r1              0.25 GCA.Eff              0.9  3.05
#> 3 r1              0.75 GCA.Eff              0.1  1.95
#> 4 r1              0.75 GCA.Eff              0.9  3.55