# fit_meta_d_SSE: Function for calculating meta-d' by minimizing SSE In craddm/metaSDT: Calculate Type 1 and Type 2 Signal Detection Measures

## Description

Provides a type-2 SDT analysis of data from a typical experiment in which observers discriminate between two response alternatives and provide ratings of confidence in their judgements.

## Usage

 ```1 2``` ```fit_meta_d_SSE(nR_S1, nR_S2, s = 1, d_min = -5, d_max = 5, d_grain = 0.01, add_constant = TRUE) ```

## Arguments

 `nR_S1` Responses to S1 stimulus. See below for advice. `nR_S2` Responses to S2 stimulus. See below for advice. `s` Ratio of standard deviations for the S1 and S2 stimulus. Defaults to 1. `d_min` Minimum bound for d' `d_max` Maximum bound for d' `d_grain` Resolution of grid of possible parameters between the bounds. `add_constant` Adds a small constant to the data (1/number of possible responses) to account for 0 or 1 values. Defaults to TRUE for ease of use across multiple datasets.

## Details

Where `fit_meta_d_MLE` uses Maximum Likelihood Estimation, `fit_meta_d_SSE` works by finding the minimum sum of squared errors. As with the MLE method, input is expected as counts for each of two stimulus types.

The expected input is two vectors, one for responses to each stimulus, encoding the observers response and confidence. For example, for two stimului labelled A and B, with three confidence ratings, participants could respond to stimulus A as follows: Response: A, rating: 3, count: 60 Response: A, rating: 2, count: 30 Response: A, rating: 1, count: 10 Response: B, rating: 1, count: 7 Response: B, rating: 2, count: 4 Response: B, rating: 3, count: 1

The appropriate vector would be nR_S1 <- c(60,30,10,7,4,1)

For stimulus B, we would have the respective vector for responses to stimulus B, eg: Response: A, rating: 3, count: 4 Response: A, rating: 2, count: 6 Response: A, rating: 1, count: 11 Response: B, rating: 1, count: 13 Response: B, rating: 2, count: 23 Response: B, rating: 3, count: 61

nR_S2 <- c(4,6,11,13,23,61)

The output is a dataframe with various metacognitive measures, including m-ratio and meta-d, estimated through minimization of SSE.

Currently, multiple rows will be returned when there are more than 2 confidence ratings, with some values varying, as they represent cutpoints between confidence ratings, and others simply duplicated.

For more details, see Maniscalco & Lau's webpage http://www.columbia.edu/~bsm2105/type2sdt/ Please cite that page and their articles if using this command.

## Author(s)

Maniscalco & Lau. Ported to R by Matt Craddock [email protected]

craddm/metaSDT documentation built on May 24, 2018, 4:16 p.m.