poisson.tests: Poisson Tests for Whole Trial Spike Counts

In neuromplex: Neural Multiplexing Analysis

Description

Carries out various Poisson related tests for double-stimuli spike count distribution.

Usage

 
poisson.tests(xA, xB, xAB, labels = c("A", "B", "AB"), remove.zeros = FALSE,
              gamma.pars = c(0.5, 2e-10), beta.pars = c(0.5, 0.5),
              nMC = 1000, plot = FALSE, add.poisson.fits = FALSE, 
              method.screen = c('variance', 'bincount'), ...)

Arguments

xA	an array of whole-trial spike counts under stimulus 1
xB	an array of whole-trial spike counts under stimulus 2
xAB	an array of whole-trial spike counts when both stimuli are present together
labels	labels for stimlus conditions
remove.zeros	whether to remove trials with zero spike counts
gamma.pars	shape and rate parameters of the gamma prior on Poisson mean
beta.pars	shape parameters of the beta prior for the mixture/intermediate parameter
nMC	number of Monte Carlo samples to be used in numerical approximations.
plot	logical indicating if a visualization plot should be made
add.poisson.fits	logical indicating if a fitted Poisson pmfs will be overlaid in the visualization. Ignored when plot=FALSE.
method.screen	a character string, default is 'variance' which uses the Poisson variance test to assess whether a Poisson distribution fits a sample of counts. Alternative choice is 'bincount' which uses an binned histogram based nonparametric chi-square goodness of fit test
...	additional commands to be passed on to grid.arrange() for plotting. For example, adding 'top="PLOT TITLE"' will add a title at the top of the combined plot. See grid.arrange for more details.

Value

Returns a list with the following items:

separation.logBF	the (log) Bayes factor for testing that that two single stimulus distributions are different
post.prob	posterior probabilities of the four hypotheses (Mixture, Intermediate, Outside, Single) under equal prior probabilities
pois.pvalue	minimum of the two p-values checking for Poisson-ness of each single stimulus distribution
sample.sizes	three trial counts for A, B and AB conditions

Examples

nA <- 20; nB <- 15; nAB <- 25
muA <- 25; muB <- 40
Acounts <- rpois(nA, muA)
Bcounts <- rpois(nB, muB)
ABcounts <- rpois(nAB, sample(c(muA, muB), nAB, replace = TRUE))
poisson.tests(Acounts, Bcounts, ABcounts, nMC=200, plot=FALSE)

neuromplex documentation built on April 22, 2021, 5:11 p.m.