# Plot a bone section 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". ```r ## S3 method for class '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, ... ) ``` ## 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 ## Returns Nothing ## Details plot.BoneProfileR displays a bone section ## Examples ```r ## Not run: # 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") ## End(Not run) ``` ## 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()` ## Author(s) Marc Girondot marc.girondot@gmail.com

plot.BoneProfileR function