summary.dapp: Summary Method for Dynamic Admixture of Poisson Process

In neuromplex: Neural Multiplexing Analysis

Description

Presents post-processing labels from a DAPP model fit to binned spiking data

Usage

 
## S3 method for class 'dapp'
summary(object, flat.cut = 0.15, wavy.cut = 0.85,
    extreme.cut = 0.25, ...)

Arguments

object	a fitted model of the class 'dapp'
flat.cut	maximum range allowed to be labelled 'flat'
wavy.cut	minimum range allowed to be labelled 'wavy'
extreme.cut	for flat curves, maximum deviation from extremes (0 or 1) allowed to be labelled flat.B or flat.A (respectivel)
...	additional parameters passed on to the call of predict.dapp

Details

The summary function analyzes the prior and posterior predictive draws of the weight curves alpha(t). Each draw is assigned with one of the following labels: 'flat.A', 'flat.B', 'flat.Mid', 'wavy', or 'others'. The proportions of these categories are printed for the prior and posterior sets. Additionally, posterior draws of alpha(t), for each recorded AB trial, are also analyzed in the same way to produce similar labels for each trial, and, the trial is given the label that has the maximum posterior probability.

Value

Gives prior and posterior summaries of the range and average predicted alpha curves

See Also

dapp, plot.dapp and predict.dapp.

Examples

## Not run: 
## generate 25 A and 30 B trials with rate functions
##    lambda.A(t) = 160*exp(-2*t/1000) + 40*exp(-0.2*t/1000)
##    lambda.B(t) = 40*exp(-2*t/1000)
## where time t is measured in ms. Then, generate 40 AB trials,
## roughly half with flat weight curves with a constant intensity
## either close to A, or close to B or close to the 50-50 mark,
## (equally likely). The remaining curves are sinusoidal
## that snake between 0.01 and 0.99 with a period randomly
## drawn between 400 and 1000

ntrials <- c(nA=25, nB=30, nAB=40)
flat.range <- list(A=c(0.85, 0.95),
                   B=c(0.05, 0.15),
                   mid=c(0.45,0.55))
flat.mix <- c(A=1/3, B=1/3, mid=1/3)
wavy.span <- c(0.01, 0.99)
wavy.period <- c(400, 1000)

T.horiz <- 1000
rateB <- 40 * exp(-2*(1:T.horiz)/T.horiz)
rateA <- 4*rateB + 40 * exp(-0.2*(1:T.horiz)/T.horiz)

synth.data <- synthesis.dapp(ntrials = ntrials, pr.flat = 0.5,
                             intervals = flat.range, wts = flat.mix,
                             span = wavy.span, period.range = wavy.period,
                             lambda.A=rateA, lambda.B=rateB)

## Visualize data and generated binned spike counts
spike.counts <- mplex.preprocess(synth.data$spiketimes, visualize=TRUE)

## Fit the DAPP model to data
fit.post <- dapp(spike.counts, verbose=FALSE)

## Visualize model fit
plot(fit.post)

## Post process results to assign second order stochasticity labels
summary(fit.post)

## End(Not run)

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