Theoretical density for lineups under different scenarios

dV(x, K, m, scenario, type = "numeric")

Arguments

x

vector of the number of picks of the data plot out of K evaluations

K

number of evaluations

m

size of the lineup

scenario

which scenario should be used? 1, 2, or 3?

type

one of "mpfr" or "numeric". Should the result be in arbitrary numeric length or be a numeric? Internally the Rmpfr package is used to get maximal precision.

Examples

dV(0:5, 5, m=2, scenario=3)
#> Warning: `dV()` was deprecated in vinference 1.0.0.
#> ℹ Please use `dVis()` instead.
#> Loading required namespace: Rmpfr
#>         0         1         2         3         4         5 
#> 0.1212987 0.1703680 0.2083333 0.2083333 0.1703680 0.1212987 
## compare to
dVsim(0:5, 5, m=2, scenario=3)
#> Warning: `dVsim()` was deprecated in vinference 1.0.0.
#> ℹ Please use `dVis()` instead.
#>   x scenario3   binom
#> 1 0    0.1237 0.03125
#> 2 1    0.1658 0.15625
#> 3 2    0.2071 0.31250
#> 4 3    0.2075 0.31250
#> 5 4    0.1664 0.15625
#> 6 5    0.1295 0.03125

require(ggplot2)
#> Loading required package: ggplot2
## probabilities can be computed without numeric loss for K=50:
K <- 50
print(qplot(0:K, dV(0:K, K, m=20, scenario=3)));
#> Warning: `qplot()` was deprecated in ggplot2 3.4.0.

print(sum(dV(0:K, K, m=20, scenario=3)));
#> [1] 1