Description Usage Arguments Details Value Examples

View source: R/dstpFunctions.R

`fitMultipleDSTP_fixed`

fits the DSTP model to a single experimental
condition of human data (besides congruency, which it accounts for
simutaneously). This function explores multiple starting parameters and
allows user to fix model parameters.

1 2 3 4 5 | ```
fitMultipleDSTP_fixed(data, conditionName = NULL, parms = c(0.145, 0.08,
0.1, 0.07, 0.325, 1.3, 0.24), var = 10, nParms = 20, cdfs = c(0.1, 0.3,
0.5, 0.7, 0.9), cafs = c(0.25, 0.5, 0.75), maxParms = c(1, 1, 1, 1, 1, 2,
1), nTrials = 50000, multipleSubjects = TRUE, fixed = c(FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE))
``` |

`data` |
A data frame containing human data. See |

`conditionName` |
If there is an additional experimental manipulation (i.e., other than target congruency) the model can only be fit to one at a time. Tell the function which condition is currently being fit by passing a string to the function (e.g., "present"). The function by default assumes no additional condition (e.g., conditionName is set to NULL). |

`parms` |
A vector of starting parameters to use in the minimisation
routine. Must be in the order: |

`var` |
An integer stating the percentage of each parameter value that should be used for finding random parameter starting points. |

`nParms` |
An integer stating how many random starting points to explore |

`cdfs` |
A vector of quantile values for cumulative distribution functions to be estimated from the human data. The model will attempt to find the best-fitting parameters that match this distributional data. |

`cafs` |
A vector of quantiles for conditional accuracy functions to be estimated from the human data. The model will attempt to find the best- fitting parameters that match this distributional data. |

`maxParms` |
A vector containing upper limits on possible parameter values. |

`nTrials` |
An integer stating how many trials to simulate per iteration of the fitting cycle for each congruency type. |

`multipleSubjects` |
A boolean stating whether the fit is to multiple subjects (multipleSubjects = TRUE) or to a single subject (multipleSubjects = FALSE). |

`fixed` |
A vector of TRUE/FALSE stating whether each parameter should be
fixed (TRUE) or free (FALSE) during the fitting routine. Must be in the
order: |

This function can be employed by the user to find the best-fitting
parameters of the DSTP model to fit the human data of a single experimental
condition. The fitting procedure accounts for congruent and incongruent
trials simultaneously. The fit is obtained by a gradient-descent method
(using the Nelder-Mead method contained in R's `optim`

function) and is
fit to the proportion of data contained in human CDF and CAF distributional
data. Multiple starting points of parameters are used.

`bestParameters`

A vector of the best-fitting parameters found
by the current fit run.

`g2`

The value of Wilks likelihood ratio (G2) obtained by the
current fit run.

`bBIC`

The value of the Bayesian Information Criterion (BIC)
obtained by the current fit run. This is calculated using the BIC equation
for binned data, hence bBIC (binned BIC).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# Load the example data the comes with the \code{flankr} package
data(exampleData)
# Fit the model to the condition "present" in the example data set using
# the default settings in the model.
fit <- fitMultipleDSTP(data = exampleData, conditionName = "present")
# Fit the model whilst fixing the first parameter (A)
parms <- c(0.145, 0.08, 0.1, 0.07, 0.325, 1.3, 0.24)
fixed <- c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)
fit <- fitMultipleDSTP_fixed(exampleData, conditionName = "present",
parms = parms, fixed = fixed)
``` |

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