# PickStuck: Which chains get stuck In ggdmc: Cognitive Models

## Description

Calculate each chain separately for the mean (across many MCMC iterations) of posterior log-likelihood. If the difference of the means and the median (across chains) of the mean of posterior is greater than the `cut`, chains are considered stuck. The default value for `cut` is 10. `unstick` manually removes stuck chains from posterior samples.

## Usage

 ```1 2``` ```PickStuck(x, hyper = FALSE, cut = 10, start = 1, end = NA, verbose = FALSE, digits = 2) ```

## Arguments

 `x` posterior samples `hyper` whether x are hierarhcial samples `cut` a criterion deciding if a chain is stuck. `start` start to evaluate from which iteration. `end` end at which iteration for evaeuation. `verbose` a boolean switch to print more information `digits` print how many digits. Default is 2

## Value

`PickStuck` gives an index vector; `unstick` gives a DMC sample.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```model <- BuildModel( p.map = list(A = "1", B = "1", t0 = "1", mean_v = "M", sd_v = "1", st0 = "1"), match.map = list(M = list(s1 = 1, s2 = 2)), factors = list(S = c("s1", "s2")), constants = c(st0 = 0, sd_v = 1), responses = c("r1", "r2"), type = "norm") p.vector <- c(A = .75, B = .25, t0 = .2, mean_v.true = 2.5, mean_v.false = 1.5) p.prior <- BuildPrior( dists = c("tnorm", "tnorm", "beta", "tnorm", "tnorm"), p1 = c(A = .3, B = .3, t0 = 1, mean_v.true = 1, mean_v.false = 0), p2 = c(1, 1, 1, 3, 3), lower = c(0, 0, 0, NA, NA), upper = c(NA,NA, 1, NA, NA)) ## Not run: dat <- simulate(model, 30, ps = p.vector) dmi <- BuildDMI(dat, model) sam <- run(StartNewsamples(5e2, dmi, p.prior)) bad <- PickStuck(sam) ## End(Not run) ```

ggdmc documentation built on Sept. 2, 2018, 1:03 a.m.