statConfR (version 0.2.1)

plotConfModelFit: Plot the prediction of fitted parameters of one model of confidence over the corresponding data

Description

The plotConfModelFit function plots the predicted distribution of discrimination responses and confidence ratings created from a data.frame of parameters obtaind from fitConfModels and overlays the predicted distribution over the data to which the model parameters were fitted.

Usage

plotConfModelFit(data, fitted_pars, model = NULL)

Value

a ggplot object with empirically observed distribution of responses and confidence ratings as bars on the x-axis as a function of discriminability (in the rows) and stimulus (in the columns). Superimposed on the empirical data, the plot also shows the prediction of one selected model as dots.

Arguments

data

a data.frame where each row is one trial, containing following variables:

• diffCond (optional; different levels of discriminability, should be a factor with levels ordered from hardest to easiest),

• rating (discrete confidence judgments, should be a factor with levels ordered from lowest confidence to highest confidence; otherwise will be transformed to factor with a warning),

• stimulus (stimulus category in a binary choice task, should be a factor with two levels, otherwise it will be transformed to a factor with a warning),

• correct (encoding whether the response was correct; should be 0 for incorrect responses and 1 for correct responses)

• participant (some group ID, most often a participant identifier; the models given in the second argument are fitted to each subset of data determined by the different values of this column)

fitted_pars

a data.frame with one row for each participant and model parameters in different columns. fitted_pars also may contain a column called model specifying the model to be visualized. If there is no model column in data or if there are multiple models in fitted_pars, it is necessary to specify the model argument.

model

character. See fitConfModels for all available models

Author

Manuel Rausch,

Examples

# 1. Fit some models to each subject of the masked orientation discrimination experiment
  # Normally, the fits should be created using the function fitConfModels
  # Fits <- fitConfModels(data, models = "WEV", .parallel = TRUE)
  # Here, we create the dataframe manually because fitting models takes about
  # 10 minutes per model fit per participant on a 2.8GHz processor.
  pars <- data.frame(participant = 1:16,
  d_1 = c(0.20, 0.05, 0.41, 0.03, 0.00, 0.01, 0.11, 0.03, 0.19, 0.08, 0.00,
  0.24, 0.00, 0.00, 0.25, 0.01),
  d_2 = c(0.61, 0.19, 0.86, 0.18, 0.17, 0.39, 0.69, 0.14, 0.45, 0.30, 0.00,
  0.27, 0.00, 0.05, 0.57, 0.23),
  d_3 = c(1.08, 1.04, 2.71, 2.27, 1.50, 1.21, 1.83, 0.80, 1.06, 0.68, 0.29,
  0.83, 0.77, 2.19, 1.93, 0.54),
  d_4 = c(3.47, 4.14, 6.92, 4.79, 3.72, 3.24, 4.55, 2.51, 3.78, 2.40, 1.95,
  2.55, 4.59, 4.27, 4.08, 1.80),
  d_5 = c(4.08, 5.29, 7.99, 5.31, 4.53, 4.66, 6.21, 4.67, 5.85, 3.39, 3.39,
  4.42, 6.48, 5.35, 5.28, 2.87),
  c = c(-0.30, -0.15, -1.37, 0.17, -0.12, -0.19, -0.12, 0.41, -0.27, 0.00,
  -0.19, -0.21, -0.91, -0.26, -0.20, 0.10),
  theta_minus.4 = c(-2.07, -2.04, -2.76, -2.32, -2.21, -2.33, -2.27, -2.29,
  -2.69, -3.80, -2.83, -1.74, -2.58, -3.09, -2.20, -1.57),
  theta_minus.3 = c(-1.25, -1.95, -1.92, -2.07, -1.62, -1.68, -2.04, -2.02,
  -1.84, -3.37, -1.89, -1.44, -2.31, -2.08, -1.53, -1.46),
  theta_minus.2 = c(-0.42, -1.40, -0.37, -1.96, -1.45, -1.27, -1.98, -1.66,
  -1.11, -2.69, -1.60, -1.25, -2.21, -1.68, -1.08, -1.17),
  theta_minus.1 = c(0.13, -0.90,  0.93, -1.71, -1.25, -0.59, -1.40, -1.00,
  -0.34, -1.65, -1.21, -0.76, -1.99, -0.92, -0.28, -0.99),
  theta_plus.1 = c(-0.62, 0.82, -2.77, 2.01, 1.39, 0.60, 1.51, 0.90, 0.18,
  1.62, 0.99,0.88, 1.67, 0.92, 0.18,  0.88),
  theta_plus.2 = c(0.15, 1.45, -1.13,2.17, 1.61, 1.24, 1.99, 1.55, 0.96, 2.44,
  1.53, 1.66, 2.00, 1.51, 1.08, 1.05),
  theta_plus.3 = c(1.40, 2.24, 0.77, 2.32, 1.80, 1.58, 2.19, 2.19, 1.54, 3.17,
  1.86, 1.85, 2.16, 2.09, 1.47, 1.70),
  theta_plus.4 = c(2.19, 2.40, 1.75, 2.58, 2.53, 2.24, 2.59, 2.55, 2.58, 3.85,
  2.87, 2.15, 2.51, 3.31, 2.27, 1.79),
  sigma = c(1.01, 0.64, 1.33, 0.39, 0.30, 0.75, 0.75, 1.07, 0.65, 0.29, 0.31,
  0.78, 0.39, 0.42, 0.69, 0.52),
  w = c(0.54, 0.50, 0.38, 0.38, 0.36, 0.44, 0.48, 0.48, 0.52, 0.46, 0.53, 0.48,
   0.29, 0.45, 0.51, 0.63))

# 2. Plot the predicted probabilities based on model and fitted parameters
  # against the observed relative frequencies.

  PlotFitWEV <- plotConfModelFit(MaskOri, pars, model="WEV")
  PlotFitWEV