Description Usage Arguments Details Examples

Trace and density plots of MCMC chains for specific parameters of interest. Print plots to pdf by default.

1 2 3 |

`object` |
Object containing MCMC output. See DETAILS below. |

`params` |
Character string (or vector of character strings) denoting parameters of interest. Default |

`excl` |
Character string (or vector of character strings) denoting parameters to exclude. Used in conjunction with |

`ISB` |
Ignore Square Brackets (ISB). Logical specifying whether square brackets should be ignored in the |

`iter` |
Number of iterations to plot for trace and density plots. The default value is 5000, meaning the last 5000 iterations of the chain will be plotted. |

`priors` |
Matrix containing random draws from prior distributions corresponding to parameters of interest. If specified, priors are plotted along with posterior density plots. Percent overlap between prior and posterior is also calculated and displayed on each plot. Each column of the matrix represents a prior for a different parameter. Parameters are plotted alphabetically - priors should be sorted accordingly. If |

`pdf` |
Logical - if |

`filename` |
Name of pdf file to be printed. Default is 'MCMCtrace'. |

`wd` |
Working directory for pdf output. Default is current directory. |

`type` |
Type of plot to be output. |

`ind` |
Logical - if |

`object`

argument can be a `stanfit`

object (`rstan`

package), an `mcmc.list`

object
(`coda`

package), an `R2jags`

model object (`R2jags`

package), or a matrix containing MCMC chains (each column representing MCMC output for a single parameter, rows representing iterations in the chain). The function automatically detects the object type and proceeds accordingly.

Matrices for the `priors`

argument can be generated using commands such as rnorm, rgamma, runif, etc. Distributions not supported by base R can be generated by using the appropriate packages. It is important to note that some discrepancies between MCMC samplers and R may exist regarding the parameterization of distributions - one example of this is the use of precision in JAGS but standard deviation in R for the 'second parameter' of the normal distribution. If the number of draws for each prior distribution is greater than the total number used for the density plot (`iter`

times the number of chains), the function will use a subset of the prior draws. If the number of draws for each prior distribution is less than the total number used for the density plot, the function will resample (with replacement) from the prior to obtain the appropriate number of draws.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
#Load data
data(MCMC_data)
#Traceplots for all 'beta' parameters - a pdf of the traceplots is generated by default
MCMCtrace(MCMC_data, params = 'beta', pdf = FALSE)
#Traceplots (individual density lines for each chain) just for 'beta[1]'
#'params' takes regular expressions when ISB = FALSE, square brackets must be escaped with '\\'
MCMCtrace(MCMC_data, params = 'beta\\[1\\]', ISB = FALSE, ind = TRUE, pdf = FALSE)
#Plot prior on top of posterior and calculate prior/posterior overlap just for 'beta[1]'
#'params' takes regular expressions when ISB = FALSE, square brackets must be escaped with '\\'
PR <- rnorm(15000, 0, 32)
MCMCtrace(MCMC_data, params = 'beta\\[1\\]', ISB = FALSE, priors = PR, pdf = FALSE)
``` |

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