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)

xA | A vector. Spike counts of repeated dual-stimuli trial data AB. |
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xB | A vector. Spike counts of repeated single-stimulus trial data A. |

xAB | A vector. Spike counts of repeated single-stimulus trial data B. |

labels | A vector. labels for the trials. |

remove.zeros | A logical value. Whether to remove 0s in spike counts. |

mu_l | A number. Lower bound of spike counts. "min" by default. Indicating \( {max}(0,min_{j=A,B,AB}({min}(Y_j)-2{std}(Y_j))) \) |

mu_u | A number. Upper bound of spike counts. "max" by default. Indicating \( {max_{j=A,B,AB}}({max}(Y_j)+2{std}(Y_j)) \) |

e | A number. 0 by default. Shringkage on the domain and meansurement of mixing density f under the Intermediate and Mixture hypothese. |

gamma.pars | A length 2 vector. The shape and rate of gamma prior for spike rate mu_A and mu_B. Jeffereys' prior by default. |

n_gq | A number. 20 by default. Number of grids in Gaussion quadrature. |

n_per | A number. 100 by default. Permutation of likihood estimation to obtain the order-invariant estimator. |

alpha | 0.5 by default. (For PRML filter) The range of the spike counts estimator \( [Y_{0.25}-\alpha {IQR},Y_{0.75}+\alpha {IQR}] \) |

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