Description Usage Arguments Details Examples

Calculate model fit

1 |

`resObserved` |
either output from |

`data` |
A dataframe containing data. |

`DstarM` |
Logical. Should the DstarM fit measure be calculated or the traditional fit measure? |

`tt` |
time grid custom densities where calculated on. Should only be supplied if |

`formula` |
Optional formula argument, for when columns names in the data are different from those used to obtain the results. |

This function allows a user to manually calculate a chi-square goodness of fit measure for model densities. This is useful for comparing a traditional analysis and a D*M analysis. For completion, this function can also calculate a D*M fit measure. We do not recommend usage of the D*M measure. While the chi-square fit measure is identical to the value of the optimizer when fitting, the DstarM fit measure is not equal to that of a DstarM analysis. This is because this function calculates the DstarM fit measure on the complete distribution, not on the model distributions, as is done during the optimization.

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 | ```
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
allDat = simData(n = 3e3, pars = pars, tt = tt, pdfND = pdfND, return.pdf = TRUE)
truePdf = allDat$pdfUnnormalized
dat = allDat$dat
chisqFit(resObserved = truePdf, data = dat, tt = tt)
## Not run:
# estimate it
define restriction matrix
restr = matrix(1:5, 5, 3)
restr[2, 2:3] = 6:7 # allow drift rates to differ
# fix parameters for speed up
fixed = matrix(c('z1', 'a1 / 2', 'sz1', .5, 'sv1', .5), 2, 3)
resD = estDstarM(data = dat, tt = tt, restr = restr, fixed = fixed,
Optim = list(parallelType = 1))
resN = estND(resD, Optim = list(parallelType = 1))
resO = estObserved(resD, resN, data = dat)
resO$fit # proper fit
## End(Not run)
``` |

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