fitSSP: Fit the SSP model to human data In JimGrange/flankr: Implements computational models of attentional selectivity

Description

`fitSSP` fits the SSP model to a single experimental condition of human data (besides congruency, which it accounts for simutaneously).

Usage

 ```1 2 3``` ```fitSSP(data, conditionName = NULL, parms = c(0.05, 0.3, 0.4, 0.05, 1.5), 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, 3), nTrials = 50000, multipleSubjects = TRUE) ```

Arguments

 `data` A data frame containing human data. See `?exampleData` for data formatted correctly. `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: `A`, `ter`, `p`, `rd`, `sda`. `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).

Details

This function can be employed by the user to find the best-fitting parameters of the SSP 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.

Value

`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).

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# 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 <- fitSSP(data = exampleData, conditionName = "present") # Fit the model using different CDF and CAF values, and 100,000 trials per # fit cycle cdfs <- c(.2, .4, .6, .8) cafs <- c(.2, .4, .6, .8) fit <- fitSSP(exampleData, conditionName = "present", cdfs = cdfs, cafs = cafs, nTrials = 100000) ```

JimGrange/flankr documentation built on May 8, 2017, 11:29 p.m.