Description Usage Arguments Details Value Examples

Calculate model density for a given set of parameters

1 2 3 4 5 | ```
Voss.density(t, pars, boundary, DstarM = TRUE, prec = 3)
LBA.density(t, pars, boundary, DstarM = TRUE, ...)
Wiener.density(t, pars, boundary, DstarM)
``` |

`t` |
Time grid for density to be calculated on. |

`pars` |
Parameter vector where (if |

`boundary` |
For which response option will the density be calculated? Either 'upper' or 'lower'. |

`DstarM` |
Logical, see |

`prec` |
Precision with which the density is calculated. Corresponds roughly to the number of decimals accurately calculated. |

`...` |
Other arguments, see |

These functions are examples of what `fun.density`

should look like.
`Voss.density`

is an adaptation of `ddiffusion`

,
`LBA.density`

is an adaptation of `dLBA`

, and
`wiener.density`

is an adaptation of `dwiener`

.
To improve speed one can remove error handling.
Normally error handling is useful, however
because differential evolution can result in an incredible number of
function evaluations (more than 10.000) it is recommended to omit error handling in custom
density functions. `estDstarM`

will apply some internal error checks
(see `testFun`

) on the density functions before starting differential
evolution. A version of `ddifusion`

without error handling can be found in
the source code (commented out to pass R check). Note that for in `Voss.density`

if DstarM == FALSE nondecision parameters are implemented manually and might differ
from from how they are implemented in other packages. The parameter `t0`

specifies the mean of a uniform distribution and `st0`

specifies the relative
size of this uniform distribution. To obtain the lower and upper range of the
uniform distribution calculate a = t0 - t0*st0, and b = t0 + t0*st0.

A numeric vector of length `length(t)`

containing a density.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
t = seq(0, .75, .01)
V.pars = c(1, 2, .5, .5, .5)
L.pars = c(1, .5, 2, 1, 1, 1)
W.pars = V.pars[1:3]
V1 = Voss.density(t = t, pars = V.pars, boundary = 'upper', DstarM = TRUE)
V2 = Voss.density(t = t, pars = V.pars, boundary = 'lower', DstarM = TRUE)
L1 = LBA.density(t = t, pars = L.pars, boundary = 'upper', DstarM = TRUE)
L2 = LBA.density(t = t, pars = L.pars, boundary = 'lower', DstarM = TRUE)
W1 = Wiener.density(t = t, pars = W.pars, boundary = 'upper', DstarM = TRUE)
W2 = Wiener.density(t = t, pars = W.pars, boundary = 'lower', DstarM = TRUE)
densities = cbind(V1, V2, L1, L2, W1, W2)
matplot(t, densities, type = 'b', ylab = 'Density', lty = 1, las = 1, bty = 'n',
col = rep(1:3, each = 2), pch = c(0, 15, 1, 16, 2, 17), cex = .8,
main = 'Model densities')
legend('topright', legend = c('Voss', 'LBA', 'RWiener'), lty = 1,
pch = 15:17, col = 1:3, bty = 'n')
``` |

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