Create Age Plot
plot_Ages(
object,
sample_names = NULL,
sample_order = NULL,
plot_mode = "ages",
...
)
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.
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.
numeric (optional): argument to rearrange the sample order, e.g., sample_order = c(4:1)
plots
the last sample first.
character (with default): allows to switch from displaying ages as points with lines ("ages"
)
for the credible intervals to densities ("density"
)
further arguments to control the plot output,
standard arguments are: cex
, xlim
, main
, xlab
, col
further (non-standard) arguments
are: grid
(TRUE
/FALSE
), legend
(TRUE
/FALSE
), legend.text
(character input needed), legend.pos
graphics::legend, legend.cex
. Additional arguments: d_scale
(scales density plots), show_ages
(add ages to density plots)
The function returns a plot and the data.frame used to display the data
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.
0.1.5
Kreutzer, S., Christophe, C., 2024. plot_Ages(): Create Age Plot. Function version 0.1.5. In: Christophe, C., Philippe, A., Kreutzer, S., Guérin, G., Baumgarten, F.H., 2024. BayLum: Chronological Bayesian Models Integrating Optically Stimulated. R package version 0.3.2.9000-59. https://CRAN.r-project.org/package=BayLum
## 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)
#> Warning: [plot_MCMC()] 'n.iter' out of range, reset to number of observations
#>
#>
#> >> MCMC Convergence of Age parameters <<
#> ----------------------------------------------
#> Sample name Bayes estimate Uppers credible interval
#> A_S-EVA-26510 1.002 1.01
#> A_S-EVA-26506 1.005 1.017
#> A_S-EVA-26507 0.998 1.003
#> A_S-EVA-26508 1.001 1.006
#>
#>
#> ---------------------------------------------------------------------------------------------------
#> *** 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 41.9710005168826
#> lower bound upper bound
#> at level 95% 41.552 42.328
#> at level 68% 41.816 42.169
#> ------------------------------------------------------
#> Sample name Bayes estimate Credible interval:
#> A_S-EVA-26506 45.7073390416434
#> lower bound upper bound
#> at level 95% 45.077 46.212
#> at level 68% 45.409 45.948
#> ------------------------------------------------------
#> Sample name Bayes estimate Credible interval:
#> A_S-EVA-26507 44.8779169666937
#> lower bound upper bound
#> at level 95% 43.445 45.758
#> at level 68% 44.409 45.345
#> ------------------------------------------------------
#> Sample name Bayes estimate Credible interval:
#> A_S-EVA-26508 45.084468903624
#> lower bound upper bound
#> at level 95% 44.209 46.258
#> at level 68% 44.438 45.338
#>
#> ------------------------------------------------------
## plot output
plot_Ages(Age)
#> SAMPLE AGE HPD68.MIN HPD68.MAX HPD95.MIN HPD95.MAX ALT_SAMPLE_NAME
#> 1 S-EVA-26510 41.97100 41.816 42.169 41.552 42.328 NA
#> 2 S-EVA-26506 45.70734 45.409 45.948 45.077 46.212 NA
#> 3 S-EVA-26507 44.87792 44.409 45.345 43.445 45.758 NA
#> 4 S-EVA-26508 45.08447 44.438 45.338 44.209 46.258 NA
#> AT
#> 1 4
#> 2 3
#> 3 2
#> 4 1
## plot output
plot_Ages(Age, plot_mode = "density")
#> SAMPLE AGE HPD68.MIN HPD68.MAX HPD95.MIN HPD95.MAX ALT_SAMPLE_NAME
#> 1 S-EVA-26510 41.97100 41.816 42.169 41.552 42.328 NA
#> 2 S-EVA-26506 45.70734 45.409 45.948 45.077 46.212 NA
#> 3 S-EVA-26507 44.87792 44.409 45.345 43.445 45.758 NA
#> 4 S-EVA-26508 45.08447 44.438 45.338 44.209 46.258 NA
#> AT
#> 1 4
#> 2 3
#> 3 2
#> 4 1