Description Usage Arguments Value Examples
Fit the mean and covariance of a bivariate Gaussian distribution for each stimulus class, subject to given constraints. Standard case uses confusion matrix from a 2x2 full-report identification experiment, but will also work in designs with N levels of confidence associated with each dimension (e.g. in Wickens, 1992).
1 |
freq |
Can be entered in two ways: 1) a 4x4 confusion matrix containing counts, with each row corresponding to a stimulus and each column corresponding to a response. row/col order must be a_1b_1, a_1b_2, a_2b_1, a_2b_2. 2) A three-way 'xtabs' table with the stimuli as the third index and the NxN possible responses as the first two indices. |
PS_x |
if TRUE, will fit model with assumption of perceptual separability on the x dimension (FALSE by default) |
PS_y |
if TRUE, will fit model with assumption of perceptual separability on the y dimension (FALSE by default) |
PI |
'none' by default, imposing no restrictions and fitting different correlations for all distributions. If 'same_rho', will constrain all distributions to have same correlation parameter. If 'all', will constain all distribution to have 0 correlation. |
method |
The optimization method used to fit the Gaussian model. Newton-Raphson gradient descent by default, but
may also specify any method available in |
An S3 grt
object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # Fit unconstrained model
data(thomas01b);
grt_obj <- fit.grt(thomas01b);
# Use standard S3 generics to examine
print(grt_obj);
summary(grt_obj);
plot(grt_obj);
# Fit model with assumption of perceptual separability on both dimensions
grt_obj_PS <- fit.grt(thomas01b, PS_x = TRUE, PS_y = TRUE);
summary(grt_obj_PS);
plot(grt_obj_PS);
# Compare models
GOF(grt_obj, teststat = 'AIC');
GOF(grt_obj_PS, teststat = 'AIC');
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