Create Age Plot
Usage
plot_Ages(
object,
sample_names = NULL,
sample_order = NULL,
plot_mode = "ages",
model = "Baylum",
...
)Arguments
- object
list or data.frame (required): Output as created by functions like AgeC14_Computation, which is a list of class
BayLum.list. Alternatively the function supports a data.frame as input, however, in such a case the data.frame must resemble the ages data.frame created by the computation functions otherwise the input will be silently ignored.- sample_names
character (optional): alternative sample names used for the plotting. If the length of the provided character vector is shorter than the real number of samples, the names are recycled.
- sample_order
numeric (optional): argument to rearrange the sample order, e.g.,
sample_order = c(4:1)plots the last sample first.- plot_mode
character (with default): allows to switch from displaying ages as points with lines (
"ages") for the credible intervals to densities ("density")- model
character "BayLum" by default. Name of the used Bayesian Age model (see
ModelAgePrior).- ...
further arguments to control the plot output, standard arguments are:
cex,xlim,main,xlab,colfurther (non-standard) arguments are:grid(TRUE/FALSE),legend(TRUE/FALSE),legend.text(character input needed),legend.posgraphics::legend,legend.cex. Additional arguments:d_scale(scales density plots),show_ages(add ages to density plots)
Value
The function returns a plot and the data.frame used to display the data
Details
This function creates an age plot showing the mean ages along with the credible intervals. The function provides various arguments to modify the plot output, however, for an ultimate control the function returns the data.frame extracted from the input object for own plots.
Author
Sebastian Kreutzer, Institute of Geography, Ruprecht-Karl-University of Heidelberg (Germany), based on code written by Claire Christophe
Examples
## load data
data(DATA_C14,envir = environment())
C14Cal <- DATA_C14$C14[,1]
SigmaC14Cal <- DATA_C14$C14[,2]
Names <- DATA_C14$Names
nb_sample <- length(Names)
## Age computation
Age <- AgeC14_Computation(
Data_C14Cal = C14Cal,
Data_SigmaC14Cal = SigmaC14Cal,
SampleNames = Names,
Nb_sample = nb_sample,
PriorAge = rep(c(20,60),nb_sample),
Iter = 500,
quiet = TRUE)
#> Finished running the simulation
#> Warning: [plot_MCMC()] Argument 'n.iter' out of range, reset to number of observations in mcmc.list / mcmc
#>
#>
#> >> MCMC Convergence of Age parameters <<
#> ----------------------------------------------
#> Sample name Bayes estimate Uppers credible interval
#> A_S-EVA-26510 1 1.002
#> A_S-EVA-26506 1.001 1.001
#> A_S-EVA-26507 1.001 1.008
#> A_S-EVA-26508 1.001 1.002
#>
#>
#> ---------------------------------------------------------------------------------------------------
#> *** WARNING: The following information are only valid if the MCMC chains have converged ***
#> ---------------------------------------------------------------------------------------------------
#>
#>
#>
#> >> Bayes estimates of Age for each sample and credible interval <<
#> ------------------------------------------------------
#> Sample name Bayes estimate Credible interval:
#> A_S-EVA-26510 41.9641180799974
#> lower bound upper bound
#> at level 95% 41.493 42.351
#> at level 68% 41.832 42.237
#> ------------------------------------------------------
#> Sample name Bayes estimate Credible interval:
#> A_S-EVA-26506 45.7040657358624
#> lower bound upper bound
#> at level 95% 45.076 46.284
#> at level 68% 45.452 45.976
#> ------------------------------------------------------
#> Sample name Bayes estimate Credible interval:
#> A_S-EVA-26507 44.8602743145111
#> lower bound upper bound
#> at level 95% 43.841 45.904
#> at level 68% 44.312 45.256
#> ------------------------------------------------------
#> Sample name Bayes estimate Credible interval:
#> A_S-EVA-26508 45.0530387640756
#> lower bound upper bound
#> at level 95% 44.067 46.104
#> at level 68% 44.472 45.442
#>
#> ------------------------------------------------------
## plot output
plot_Ages(Age)
#> SAMPLE AGE HPD68.MIN HPD68.MAX HPD95.MIN HPD95.MAX ALT_SAMPLE_NAME
#> 1 S-EVA-26510 41.96412 41.832 42.237 41.493 42.351 NA
#> 2 S-EVA-26506 45.70407 45.452 45.976 45.076 46.284 NA
#> 3 S-EVA-26507 44.86027 44.312 45.256 43.841 45.904 NA
#> 4 S-EVA-26508 45.05304 44.472 45.442 44.067 46.104 NA
#> AT
#> 1 4
#> 2 3
#> 3 2
#> 4 1
## plot output
plot_Ages(Age, plot_mode = "density", legend.pos = "topright")
#> SAMPLE AGE HPD68.MIN HPD68.MAX HPD95.MIN HPD95.MAX ALT_SAMPLE_NAME
#> 1 S-EVA-26510 41.96412 41.832 42.237 41.493 42.351 NA
#> 2 S-EVA-26506 45.70407 45.452 45.976 45.076 46.284 NA
#> 3 S-EVA-26507 44.86027 44.312 45.256 43.841 45.904 NA
#> 4 S-EVA-26508 45.05304 44.472 45.442 44.067 46.104 NA
#> AT
#> 1 4
#> 2 3
#> 3 2
#> 4 1