simConf {statConfR}

Simulate data according to a static model of confidence

Description

Simulate data according to a static model of confidence

Usage

simConf(model = "SDT", paramDf)

Arguments

model

character of length 1. Models implemented so far: 'WEV', 'SDT', 'GN', 'PDA', 'IG', 'ITGc', 'ITGcm', 'logN', and 'logWEV'.

paramDf

a data.frame that contains all parameters to simulate a data set, with one row and the different parameters in different columns. Which parameters are needed depends on the specific model:

• N (the number of trials be simulated),

• participant (optional, the participant ID of each parameter set. Should be unique to each row),

• d_1, d_2, ... (sensitivity parameters. The number of sensitivity parameters determines the number of levels of discriminability),

• c (discrimination bias),

• theta_minus.1, theta_minus.2, ... (confidence criteria associated with the response R = -1. The function simulates one more confidence category than there are confidence criteria),

• theta_plus.1, theta_plus.2, ... (confidence criteria associated with the response R = 1. The function simulates one more confidence category than there are confidence criteria),

• w (only for models WEV and logWEV: the visibility weighting parameter, bounded between 0 and 1),

• sigma (only for models WEV, GN, logN, and logWEV: confidence noise, bounded between 0 and Inf),

• m (only for IG, ITGm, and ITGcm: metacognitive efficiency parameter, bounded between 0 and Inf),

• b (only for PDA: postdecisional accumulation parameter, bounded between 0 and Inf),

• M_theta_minus.1, M_theta_minus.2, ... (only for logN: Mean confidence criteria associated with the response R = -1),

• M_theta_plus.1, M_theta_plus.2,... (only for logN: Mean confidence criteria associated with the response R = 1).

Details

see fitConf for a detailed description of the different models.

Value

a dataframe with N rows, and the columns stimulus, correct and rating. If more than 1 sensitivity parameter is provided, there is diffCond.

Author(s)

Manuel Rausch, [e-mail]

Examples

# 1. define some parameters
paramDf <- data.frame(d_1 = 0, d_2 = 2, d_3 = 4,c = .0,
theta_minus.2 = -2, theta_minus.1 = -1, theta_plus.1 = 1, theta_plus.2 = 2,
sigma = 1/2, w = 0.5, N = 500)
# 2. Simulate dataset
SimulatedData <- simConf(model = "WEV", paramDf)