BoneProfileR (version 4.0)

plot.BoneProfileR: Plot a bone section

Description

Display a bone section.
type value can be:
Image plot: original, mineralized, unmineralized, section
Original is the original image, mineralized is the mineral interpretation of the section, unmineralized is the unmineralized interpretation of the section, section is the interpretation of the section.
colors shows the histograms of pixel information with foreground and background colors if they are defined.
3Dcolors show the pixels colors in 3D
Global analysis: observations, model, observations+model
Radial analysis: radial
Periodic analysis: periodic
If angle is not null and a radial analysis exists, it will show the model for this angle.
mcmc: It will show the posterior distribution of parameter.
For periodic analysis, you can see a particular parameter with parameter.name being P, S, Min, Max, K1, or K2 or the global median compactness using parameter.name="compactness". You can use col=rainbow(128) or hcl.colors(128) to see the region of transition. You can also plot the average compactness using parameter.name="averagemodel".

Usage

# S3 method for BoneProfileR
plot(
  x,
  message = NULL,
  type = "original",
  angle = NULL,
  show.all.angles = FALSE,
  show.centers = TRUE,
  show.colors = TRUE,
  show.grid = TRUE,
  analysis = 1,
  parameter.name = "S",
  options.mcmc = list(),
  restorePar = TRUE,
  mar = NULL,
  angle.3D = 55,
  CI = "ML",
  replicates.CI = 1000,
  show.legend = TRUE,
  ...
)

Value

Nothing

Arguments

x

The bone image

message

The message to be displayed

type

The type of plot; see description

angle

Which angle model to show

show.all.angles

For periodic type and partial section, should all angles been shown?

show.centers

Should the centers be shown?

show.colors

Should the background and foreground colors be shown?

show.grid

Should the grid be shown?

analysis

Name or number of analysis to be plotted

parameter.name

The parameter to plot

options.mcmc

The option to plot type mcmc output

restorePar

If TRUE, restore the par parameter at the exit

mar

The margin for type being "model" or "observations"

angle.3D

The angle between x and y for 3Dcolors graph

CI

Which confidence interval should be plotted: MCMC or ML

replicates.CI

How many replicates to estimate CI?

show.legend

Should a legend be shown?

...

Default parameters for some functions

Author

Marc Girondot

Details

plot.BoneProfileR displays a bone section

See Also

Other BoneProfileR: BP_AutoFit(), BP_ChooseBackground(), BP_ChooseCenter(), BP_ChooseForeground(), BP_DetectBackground(), BP_DetectCenters(), BP_DetectForeground(), BP_DuplicateAnalysis(), BP_EstimateCompactness(), BP_FitBayesianCompactness(), BP_FitBayesianPeriodicCompactness(), BP_FitMLCompactness(), BP_FitMLPeriodicCompactness(), BP_FitMLRadialCompactness(), BP_GetFittedParameters(), BP_ListAnalyses(), BP_LnLCompactness(), BP_OpenImage(), BP_Report(), Erinaceus_europaeus, summary.BoneProfileR()

Examples

if (FALSE) {
# Not run:
library(BoneProfileR)
 bone <- BP_OpenImage()
 # or 
 path_Hedgehog <- system.file("extdata", "Erinaceus_europaeus_fem_2-1_small.png", 
                             package = "BoneProfileR")
 bone <- BP_OpenImage(file=path_Hedgehog)
 bone <- BP_DetectBackground(bone=bone, analysis="logistic")
 bone <- BP_DetectForeground(bone=bone, analysis="logistic")
 plot(bone, type="colors")
 bone <- BP_DetectCenters(bone=bone, analysis="logistic")
 plot(bone, type="3Dcolors")
 bone <- BP_EstimateCompactness(bone, analysis="logistic")
 bone <- BP_FitMLCompactness(bone, analysis="logistic")
 plot(bone)
 ############################################
 # Example with comparison between two models
 ############################################
 path_Hedgehog <- system.file("extdata", "Erinaceus_europaeus_fem_2-1_small.png", 
                             package = "BoneProfileR")
 bone <- BP_OpenImage(file=path_Hedgehog)
 bone <- BP_DetectBackground(bone=bone, analysis="logistic")
 bone <- BP_DetectForeground(bone=bone, analysis="logistic")
 bone <- BP_DetectCenters(bone=bone, analysis="logistic")
 bone <- BP_EstimateCompactness(bone, analysis="logistic")
 bone <- BP_FitMLCompactness(bone, analysis="logistic")
 plot(bone)
 plot(bone, type="observations")
 plot(bone, type="observations+model", analysis=1)
 fittedpar <- BP_GetFittedParameters(bone, analysis="logistic", 
                                     ML=TRUE, return.all = FALSE)[, "mean"]
 bone <- BP_DuplicateAnalysis(bone, from="logistic", to="flexit")
 bone <- BP_FitMLCompactness(bone, 
                fitted.parameters=c(fittedpar, K1=1, K2=1), 
                fixed.parameters=NULL, analysis="flexit")
 compare_AIC(Logistic=BP_GetFittedParameters(bone, analysis="logistic", 
                                             ML=TRUE, return.all = TRUE), 
             Flexit=BP_GetFittedParameters(bone, analysis="flexit", 
                                           ML=TRUE, return.all = TRUE))
 out4p <- plot(bone, type="observations+model", analysis="logistic")
 out6p <- plot(bone, type="observations+model", analysis="flexit")
 
 ############################################
 # Fit distribution using Bayesian model
 ############################################
 bone <- BP_FitBayesianCompactness(bone, analysis="logistic", n.adapt=100)
 # Test the output - New in version 3.2
 plot(bone, type="mcmc", options.mcmc = list(what="LnL"))
 #########################################################################
 # Clearly the distribution is not stationary; the adaptation was too short
 ######################################################################### 
 bone <- BP_FitBayesianCompactness(bone, analysis="logistic", n.adapt=10000)
 # Now it is ok
 plot(bone, type="mcmc", options.mcmc = list(what="LnL"))
 #########################################################################
 # New in version 3.2
 #########################################################################
 plot(bone, type="mcmc", options.mcmc = list(what="Posterior", 
      xlim=c(0.025, 0.035), breaks=seq(from=0.025, to=0.035, by=0.001)), 
      parameter.name = "S")
 plot(bone, type="mcmc", options.mcmc = list(what="MarkovChain", 
                                            ylim=c(0.02, 0.04)), 
                                            parameter.name = "S")
 #########################################################################
 # Check the priors and the output
 #########################################################################
 mcmc <- RM_get(x=bone, RMname="logistic", valuename = "mcmc")
 priors <- mcmc$parametersMCMC$parameters
 parameters <- as.parameters(mcmc, index="median")
 #########################################################################
 # Now it is ok. It can be used
 #########################################################################
 plot(bone, type="observations+model", CI="MCMC")
 plot(bone, type="observations+model", CI="ML")
 #########################################################################
 
 #############################################
 # Radial compactness
 #############################################
 bone <- BP_FitMLRadialCompactness(bone, progressbar=TRUE)
 plot(bone, type="radial", parameter.name=c("P", "S"))
 plot(bone, type="radial", parameter.name=c("P", "S", "Min", "Max"))
 plot(bone, type="radial", parameter.name="observed.compactness")
 plot(bone, type="radial", parameter.name="linearized.observed.compactness")
 
 #############################################
 # Periodic analysis
 # This model can take 10 minutes to be fitted
 # And still more if you use large replicates.CI value
 #############################################
 bone <- BP_FitMLPeriodicCompactness(bone, analysis="logistic", control.optim=list(trace=2), 
                                     fitted.parameters=c(par, PSin=0.001, PCos=0.001, 
                                     SSin=0.001, SCos=0.001, MinSin=0.001, MinCos=0.001, 
                                     MaxSin=0.001, MaxCos=0.001), replicates.CI=2000)
 plot(bone, type="periodic", parameter.name="compactness", col=rainbow(128))
 plot(bone, type="periodic", parameter.name="compactness")
 plot(bone, type="periodic", parameter.name="P", ylim=c(0, 1), 
       col=rgb(red = 0.7, green = 0.7, blue = 0.7, alpha = 0.2))
 plot(bone, type="periodic", parameter.name="averagemodel")
 
}