fit_meta_d_SSE: Function for calculating meta-d' by minimizing SSE

In craddm/metaSDT: Calculate Type 1 and Type 2 Signal Detection Measures

Function for calculating meta-d' by minimizing SSE

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

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 [e-mail]

See Also

Other [meta_d]: fit_meta_d_bal()

Examples

nr_s1 <- c(60,30,10,7,4,1)
nr_s2 <- c(4,6,11,13,23,61)
fit_meta_d_SSE(nr_s1, nr_s2)

craddm/metaSDT documentation built on Feb. 10, 2024, 6:57 a.m.