Description Usage Arguments Details Author(s) References See Also Examples

Produce convergence test for CSMFs from fitted `"insilico"`

objects.

1 2 3 4 5 6 7 8 9 |

`csmf` |
It could be either fitted |

`conv.csmf` |
The minimum mean CSMF to be checked. Default to be 0.02, which means any causes with mean CSMF lower than 0.02 will not be tested. |

`test` |
Type of test. Currently supporting Gelman and Rubin's test
( |

`verbose` |
Logical indicator to return the test detail instead of one logical outcome for Heidelberger and Welch's test. Default to be TRUE. |

`autoburnin` |
Logical indicator of whether to omit the first half of the
chain as burn in. Default to be FALSE since |

`which.sub` |
the name of the sub-population to test when there are multiple in the fitted object. |

`...` |
Arguments to be passed to heidel.diag or gelman.diag |

The tests are performed using heidel.diag and gelman.diag
functions in `coda`

package. The function takes either one or a list of
output from `insilico`

function, or only the iteration by CSMF matrix.
Usually in practice, many causes with very tiny CSMF are hard to converge
based on standard tests, thus it is suggested to check convergence for only
causes with mean CSMF over certain threshold by setting proper
`conv.csmf`

.

Note for Gelman and Rubin's test, all chains should have the same length. If the chains are sampled with automatically length determination, they might not be comparable by this test.

Zehang Li, Tyler McCormick, Sam Clark

Maintainer: Zehang Li <lizehang@uw.edu>

Tyler H. McCormick, Zehang R. Li, Clara Calvert, Amelia C. Crampin,
Kathleen Kahn and Samuel J. Clark Probabilistic cause-of-death assignment
using verbal autopsies, *Journal of the American Statistical
Association* (2016), 111(515):1036-1049.

Gelman, Andrew, and Donald B. Rubin. Inference from iterative simulation
using multiple sequences. *Statistical science* (1992): 457-472.

Brooks, Stephen P., and Andrew Gelman. General methods for monitoring
convergence of iterative simulations. *Journal of computational and
graphical statistics* 7.4 (1998): 434-455.

Heidelberger, Philip, and Peter D. Welch. A spectral method for confidence
interval generation and run length control in simulations.
*Communications of the ACM* 24.4 (1981): 233-245.

Heidelberger, Philip, and Peter D. Welch. Simulation run length control in
the presence of an initial transient. *Operations Research* 31.6
(1983): 1109-1144.

Schruben, Lee W. Detecting initialization bias in simulation output.
*Operations Research* 30.3 (1982): 569-590.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
# load sample data together with sub-population list
data(RandomVA2)
# extract InterVA style input data
data <- RandomVA2
# extract sub-population information.
subpop <- RandomVA2$sex
# run without sub-population
fit1a<- insilico( data, subpop = NULL,
Nsim = 400, burnin = 200, thin = 10 , seed = 1,
auto.length = FALSE)
fit1b<- insilico( data, subpop = NULL,
Nsim = 400, burnin = 200, thin = 10 , seed = 2,
auto.length = FALSE)
fit1c<- insilico( data, subpop = NULL,
Nsim = 400, burnin = 200, thin = 10 , seed = 3,
auto.length = FALSE)
# single chain check
csmf.diag(fit1a)
# multiple chains check
csmf.diag(list(fit1a, fit1b, fit1c), test = "gelman")
# with sub-populations
fit2a<- insilico( data, subpop = subpop,
Nsim = 400, burnin = 200, thin = 10 , seed = 1,
auto.length = FALSE)
fit2b<- insilico( data, subpop = subpop,
Nsim = 400, burnin = 200, thin = 10 , seed = 2,
auto.length = FALSE)
fit2c<- insilico( data, subpop = subpop,
Nsim = 400, burnin = 200, thin = 10 , seed = 3,
auto.length = FALSE)
# single chain check
csmf.diag(fit2a)
# multiple chains check
csmf.diag(list(fit2a, fit2b, fit2c), test = "gelman", which.sub = "Men")
``` |

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