densityplot.angmcmc {BAMBI} | R Documentation |

## Density plots for angmcmc objects

### Description

Plot fitted angular mixture model density surfaces or curves.

### Usage

```
## S3 method for class 'angmcmc'
densityplot(
x,
data = NULL,
fn = mean,
type = "point-est",
log.density = FALSE,
xpoints = seq(0, 2 * pi, length.out = 35),
ypoints = seq(0, 2 * pi, length.out = 35),
plot = TRUE,
show.hist = ifelse(log.density, FALSE, TRUE),
xlab,
ylab,
zlab = ifelse(log.density, "Log Density", "Density"),
main,
...
)
```

### Arguments

`x` |
angmcmc object. |

`data` |
unused. The parameter is already filled with results from fitted angular model. It is kept
to ensure compatibility with the lattice S3 generic |

`fn` |
function, or a single character string specifying its name, to evaluate on MCMC samples to estimate
parameters. Defaults to |

`type` |
Passed to d_fitted. Possible choices are "point-est" and "post-pred". |

`log.density` |
logical. Should log density be used for the plot? |

`xpoints` , `ypoints` |
Points on the x and y coordinates (if bivariate) or only x coordinate (if univariate) where the density is to be evaluated. Each defaults to seq(0, 2*pi, length.out=100). |

`plot` |
logical. Should the density surface (if the fitted data is bivariate) or the density curve (if univariate) be plotted? |

`show.hist` |
logical. Should a histogram for the data points be added to the plot, if the fitted data is univariate? Ignored if data is bivariate. |

`xlab` , `ylab` , `zlab` , `main` |
graphical parameters passed to |

`...` |
additional arguments passed to |

### Details

When `plot==TRUE`

, `densityplot.angmcmc`

calls `lattice::wireframe`

or
plot from graphics to draw the surface or curve.

To estimate the mixture density, first the parameter vector `\eta`

is estimated
by applying `fn`

on the MCMC samples, yielding the (consistent) Bayes estimate `\hat{\eta}`

. Then the mixture density
`f(x|\eta)`

at any point `x`

is (consistently) estimated by `f(x|\hat{\eta})`

.

Note that `densityplot.angmcmc`

**does not** plot the kernel densitie estimates
of the MCMC parameters. (These plots can be obtained by first converting an `angmcmc`

object to an `mcmc`

object via as.mcmc.list, and then
by using `densplot`

from package coda on the resulting `mcmc.list`

object. Instead,
`densityplot.angmcmc`

returns the surface (if 2-D) or the curve (if 1-D)
of the fitted model density evaluated at the estimated parameter vector (obtain through pointest).

### Examples

```
# first fit a vmsin mixture model
# illustration only - more iterations needed for convergence
fit.vmsin.20 <- fit_vmsinmix(tim8, ncomp = 3, n.iter = 20,
n.chains = 1)
# now create density surface with the default first 1/3 as burn-in and thin = 1
library(lattice)
densityplot(fit.vmsin.20)
# the viewing angles can be changed through the argument 'screen'
# (passed to lattice::wireframe)
densityplot(fit.vmsin.20, screen = list(z=-30, x=-60))
densityplot(fit.vmsin.20, screen = list(z=30, x=-60))
# the colors can be changed through 'col.regions'
cols <- grDevices::colorRampPalette(c("blue", "green",
"yellow", "orange", "red"))(100)
densityplot(fit.vmsin.20, col.regions = cols)
# Now fit a vm mixture model
# illustration only - more iterations needed for convergence
fit.vm.20 <- fit_vmmix(wind$angle, ncomp = 3, n.iter = 20,
n.chains = 1)
densityplot(fit.vm.20)
```

*BAMBI*version 2.3.5 Index]