mtc.run | R Documentation |

`mtc.model`

using an MCMC samplerThe function `mtc.run`

is used to generate samples from a object of type `mtc.model`

using a MCMC sampler.
The resulting `mtc.result`

object can be coerced to an `mcmc.list`

for further analysis of the dataset using the `coda`

package.

```
mtc.run(model, sampler = NA, n.adapt = 5000, n.iter = 20000, thin = 1)
## S3 method for class 'mtc.result'
summary(object, ...)
## S3 method for class 'mtc.result'
plot(x, ...)
## S3 method for class 'mtc.result'
forest(x, use.description=FALSE, ...)
## S3 method for class 'mtc.result'
print(x, ...)
## S3 method for class 'mtc.result'
as.mcmc.list(x, ...)
```

`model` |
An object of S3 class |

`sampler` |
Deprecated: gemtc now only supports the JAGS sampler. Specifying a sampler will result in a warning or error. This argument will be removed in future versions. |

`n.adapt` |
Amount of adaptation (or tuning) iterations. |

`n.iter` |
Amount of simulation iterations. |

`thin` |
Thinning factor. |

`object` |
Object of S3 class |

`x` |
Object of S3 class |

`use.description` |
Display treatment descriptions instead of treatment IDs. |

`...` |
Additional arguments. |

An object of class `mtc.result`

. This is a list with the following elements:

`samples` |
The samples resulting from running the MCMC model, in |

`model` |
The |

`deviance` |
Residual deviance statistics, a list with the following elements. |

The object can be coerced to an `mcmc.list`

from the `coda`

package by the generic S3 method `as.mcmc.list`

.

Convergence of the model can be assessed using methods from the `coda`

package.
For example the Brooks-Gelman-Rubin method (`coda::gelman.diag`

, `coda::gelman.plot`

).
The `summary`

also provides useful information, such as the MCMC error and the time series and densities given by `plot`

should also be inspected.

The `forest`

function can provide forest plots for `mtc.result`

objects.
This is especially useful in combination with the `relative.effect`

function that can be used to calculate relative effects compared to any baseline for consistency models.
The `rank.probability`

function calculates rank probabilities for consistency models.

Gert van Valkenhoef, Joël Kuiper

`mtc.model`

`relative.effect.table`

,
`relative.effect`

,
`rank.probability`

`coda::gelman.diag`

,
`coda::gelman.plot`

```
model <- mtc.model(smoking)
## Not run: results <- mtc.run(model, thin=10)
results <- readRDS(system.file("extdata/luades-smoking-samples.rds", package="gemtc"))
# Convergence diagnostics
gelman.plot(results)
# Posterior summaries
summary(results)
## Iterations = 5010:25000
## Thinning interval = 10
## Number of chains = 4
## Sample size per chain = 2000
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## d.A.B 0.4965 0.4081 0.004563 0.004989
## d.A.C 0.8359 0.2433 0.002720 0.003147
## d.A.D 1.1088 0.4355 0.004869 0.005280
## sd.d 0.8465 0.1913 0.002139 0.002965
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## d.A.B -0.2985 0.2312 0.4910 0.7530 1.341
## d.A.C 0.3878 0.6720 0.8273 0.9867 1.353
## d.A.D 0.2692 0.8197 1.0983 1.3824 2.006
## sd.d 0.5509 0.7119 0.8180 0.9542 1.283
plot(results) # Shows time-series and density plots of the samples
forest(results) # Shows a forest plot
```

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