# chisqFit: Calculate model fit In DstarM: Analyze Two Choice Reaction Time Data with the D*M Method

## Description

Calculate model fit

## Usage

 `1` ```chisqFit(resObserved, data, DstarM = FALSE, tt = NULL, formula = NULL) ```

## Arguments

 `resObserved` either output from `estObserved` or a matrix containing custom densities to calculate the fitness for. `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 `resOberved` is a matrix containing custom densities `formula` Optional formula argument, for when columns names in the data are different from those used to obtain the results.

## Details

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.

## Examples

 ``` 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) ```

DstarM documentation built on Aug. 29, 2020, 1:06 a.m.