plot.BIPOD: Plot function for class BIPOD
In BIPOD: BIPOD (Bayesian Inference for Partially Observed diffusions)

Description Usage Arguments Details

View source: R/plot.BIPOD.r

Graphical summaries of output from Gibbs sampler

## S3 method for class 'BIPOD'
plot(x, theta = NULL, subset = NULL, type, lag = 20,
       interval = NULL, treshold = 0.1, speed = 0.1, truepath = NULL,
       BY = 1, prop = c(.05,.95), diffPriorMean = NULL,
       diffPriorCovar = NULL, log = FALSE, ...)

`x`	x: An object of class BIPOD.
`theta`	Optional numerical vector with true parameter values.
`subset`	Numeric vector. Which parameters should be used for plotting. Defaults to all.
`type`	Character choosing plotting type: Either "trace", "hist", "acp", "pairs","SDtrace", "accept", "movie" or "cover". See details.
`lag`	Positive integer: Number of lags used in autocorrelation plot
`interval`	Positive integer or numericla vector: If integer, used as burn in for the Gibbs sampler. If vector, used to subsample Gibbs output. Defaults to no subsampling and no burn in.
`treshold`	Positive numeric: Cut off for acceptance rate.
`speed`	Positive number used for type="movie": How much time to pause between each frame of the movie?
`truepath`	Vector or matrix with latent data. Optional.
`BY`	integer: Only relevant for type="movie". How many frames to skip for each iteration?
`prop`	Numeric vector with values between 0 and 1: Only relevant for type="cover". Specifies quantiles for the sampled paths
`diffPriorMean`	Numeric of length 2, only for type=="hist". The prior mean for the diffusion coefficients.
`diffPriorCovar`	2*2 matrix, only for type=="hist". The prior covariance for the diffusion coefficients.
`log`	Boolean, only for type=="hist". If TRUE, the prior density and the estimate of the diffusion coefficients are log transformed before plotting.
`...`	Additional arguments to be passed to matplot, density or acf.