Description Usage Arguments Details Value Examples

Plots histograms for each condition-response pair/ condition/ response with overlayed estimated densities.

1 2 3 4 5 6 7 8 9 10 11 12 |

`resObserved` |
output of |

`data` |
The dataset used to estimate the model. |

`what` |
What to plot. Can be 'cr' for 'condition-response pairs, 'c' for condition, and 'r' for response. |

`layout` |
An optional layout matrix. |

`main` |
an optional vector containing names for each plot. |

`linesArgs` |
A list containing named arguments to be passed to |

`ggplot` |
Deprecated and ignored. |

`prob` |
Should a qqplot of observed vs model implied quantiles be plotted?
By default, it is |

`probType` |
A numeric value defining several plotting options. 0 does nothing, 1 removes the 0% quantile, 2 removes the 100% quantile and 3 removes both the 0% and 100% quantile. |

`...` |
Further arguments to be passed to hist. |

Keep in mind when using `what = 'c'`

or `what = 'r'`

pdfs are simply
averaged, not weighted to the number of observed responses.

if ggplot is FALSE `invisible()`

, otherwise a list

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
# simulate data with three stimuli of different difficulty.
# this implies different drift rates across conditions.
# define a time grid. A more reasonable stepsize is .01; this is just for speed.
tt = seq(0, 5, .1)
pars = c(.8, 2, .5, .5, .5, # condition 1
.8, 3, .5, .5, .5, # condition 2
.8, 4, .5, .5, .5) # condition 3
pdfND = dbeta(tt, 10, 30)
# simulate data
lst = simData(n = 3e5, pars = pars, tt = tt, pdfND = pdfND, return.pdf = TRUE)
dat = lst$dat
# define restriction matrix
restr = matrix(1:5, 5, 3)
restr[2, 2:3] = 6:7 # allow drift rates to differ
# fix variance parameters
fixed = matrix(c('sz1', .5, 'sv1', .5), 2, 2)
## Not run:
# Run D*M analysis
resD = estDstarM(dat = dat, tt = tt, restr = restr, fixed = fixed)
# Estimate nondecision density
resND = estND(resD)
# Estimate observed density
resObs = estObserved(resD, resND)
# plot histograms with overlayed
# densities per condition-response pair
plotObserved(resObserved = resObs, data = dat,
xlim = c(0, 1))
# plot estimated and true densities
plot(resObs, col = rep(1:3, each = 2), xlim = 0:1)
matlines(tt, lst$pdfNormalized, col = rep(1:3, each = 2), lty = 2)
# other uses of plotObserved
plotObserved(resObserved = resObs, data = dat, what = 'cr', xlim = c(0, 1))
plotObserved(resObserved = resObs, data = dat, what = 'c', xlim = c(0, 1))
plotObserved(resObserved = resObs, data = dat, what = 'r', xlim = c(0, 1))
## End(Not run)
``` |

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