Global stutter model where the expected stutter rate is constant across alleles and loci

Global stutter model where the expected stutter rate is constant across alleles and loci

Usage

global_stutter_model(
  back_stutter_rate,
  forward_stutter_rate,
  size_regression,
  sex_locus_name = "AMEL"
)

Arguments

back_stutter_rate

Numeric. (Optional)

forward_stutter_rate

Numeric. (Optional)

size_regression

Function, see read_size_regression.

sex_locus_name

Character vector, defaults to "AMEL".

Value

Object of class stutter_model to be used by e.g. gamma_model.

Details

When a pg_model is constructed (see gamma_model), a stutter model can optionally be applied. In the global stutter model, the expected stutter rate is constant across all loci and for all parent alleles.

See also

allele_specific_stutter_model for a stutter model where the expected stutter rate depends on the allele and locus.

Examples

# the stutter model needs a size regression to determine fragment length
# of stutter products
size_regression <- read_size_regression(system.file("extdata",
"GlobalFiler_SizeRegression.csv",package = "simDNAmixtures"))

# define a stutter model with an expected back stutter rate of 10%
stutter_model <- global_stutter_model(back_stutter_rate = 0.1,
                                     size_regression = size_regression)

stutter_model
#> $stutters
#> $stutters$BackStutter
#> $stutters$BackStutter$rate
#> [1] 0.1
#> 
#> $stutters$BackStutter$delta
#> [1] -1
#> 
#> 
#> 
#> $sex_locus_name
#> [1] "AMEL"
#> 
#> $size_regression
#> function (locus, allele) 
#> {
#>     if (has_exceptions) {
#>         locus_exceptions <- exceptions[[locus]]
#>         if (!is.null(locus_exceptions)) {
#>             if (allele %in% names(locus_exceptions)) {
#>                 size <- locus_exceptions[[allele]]
#>                 return(size)
#>             }
#>         }
#>     }
#>     regression_locus <- regression_df_by_locus[[locus]]
#>     if (is.null(regression_locus)) {
#>         stop("No size regression available for locus ", locus)
#>     }
#>     intercept <- regression_locus$Intercept
#>     slope <- regression_locus$Slope
#>     allele_numeric <- as.numeric(allele)
#>     if (has_repeat_length_by_marker) {
#>         repeat_length <- repeat_length_by_marker[[locus]]
#>         if (!is.null(repeat_length)) {
#>             allele_numeric <- repeats_to_decimals(allele_numeric, 
#>                 repeat_length)
#>         }
#>     }
#>     intercept + slope * allele_numeric
#> }
#> <bytecode: 0x55ce0eb8eb40>
#> <environment: 0x55ce0f62cbd8>
#> 
#> $add_expected_stutter
#> function (...) 
#> global_stutter_model_add_expected_stutter(stutter_model, ...)
#> <bytecode: 0x55ce0f8bbd60>
#> <environment: 0x55ce0f8bb7e8>
#> 
#> attr(,"class")
#> [1] "stutter_model"