Result of PRML classifier and PRML filter.

prml_tests(xA, xB, xAB, labels = c("A", "B", "AB"),
remove.zeros = FALSE, mu_l = "min", mu_u = "max", e = 0,
gamma.pars = c(0.5, 2e-10), n_gq = 20, n_per = 100, alpha = 0.5)

## Arguments

xA A vector. Spike counts of repeated dual-stimuli trial data AB. A vector. Spike counts of repeated single-stimulus trial data A. A vector. Spike counts of repeated single-stimulus trial data B. A vector. labels for the trials. A logical value. Whether to remove 0s in spike counts. A number. Lower bound of spike counts. "min" by default. Indicating $${max}(0,min_{j=A,B,AB}({min}(Y_j)-2{std}(Y_j)))$$ A number. Upper bound of spike counts. "max" by default. Indicating $${max_{j=A,B,AB}}({max}(Y_j)+2{std}(Y_j))$$ A number. 0 by default. Shringkage on the domain and meansurement of mixing density f under the Intermediate and Mixture hypothese. A length 2 vector. The shape and rate of gamma prior for spike rate mu_A and mu_B. Jeffereys' prior by default. A number. 20 by default. Number of grids in Gaussion quadrature. A number. 100 by default. Permutation of likihood estimation to obtain the order-invariant estimator. 0.5 by default. (For PRML filter) The range of the spike counts estimator $$[Y_{0.25}-\alpha {IQR},Y_{0.75}+\alpha {IQR}]$$

## Value

A list.

separation.logBF

log Bayes factor for the hypothesis $$mu_A=mu_B$$ versus $$mu_A \neq mu_B$$.

post.prob

posterior probabilities under Mixture, Intermediate, Outside, Single hypotheses.

win.model

the model has largest post.prob.

prml.filter.bf

Bayes factor of PRML filter for single-stimulus trial A and B

samp.sizes

number of repeated trials under condition A, B, AB