plotConfModelFit {statConfR} | R Documentation |
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)
Arguments
data |
a
|
fitted_pars |
a |
model |
|
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.
Author(s)
Manuel Rausch, manuel.rausch@hochschule-rhein-waal.de
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