BoneProfileR-package: A Model for Bone Compactness.

In BoneProfileR: Tools to Study Bone Compactness

A Model for Bone Compactness.

Description

A Model for Bone Compactness.
The lastest version of this package can always been installed using:
install.packages(c("imager", "tiff", "ijtiff", "HelpersMG", "knitr", "rmarkdown", "openxlsx", "shiny"))
install.packages("https://hebergement.universite-paris-saclay.fr/marcgirondot/CRAN/HelpersMG.tar.gz", repos=NULL, type="source")
install.packages("https://hebergement.universite-paris-saclay.fr/marcgirondot/CRAN/BoneProfileR.tar.gz", repos=NULL, type="source")
BoneProfileR uses a new results management software that is developed as part of the HelpersMG package. Using this results management system (RM), all the results are stored as part of the analyzed image.
This results management software has been developed to help users to maintain the results associated with the methodology used to obtain it. It is part of the large movement in science of replicative research.
An analysis is then stored with the image in a single file with the following information:
name, timestamp, bg, fg, threshold, contour, centers, peripherie, compactness, array.compactness, cut.distance.center, cut.angle, used.centers, compactness.synthesis, partial, rotation.angle, global.compactness, optim, optimRadial
Several analyses can be stored within a single file.

Details

A model for bone compactness.

Package:	BoneProfileR
Type:	Package
Version:	2.4 build 766
Date:	2022-09-06
License:	GPL (>= 2)
LazyLoad:	yes

Author(s)

Marc Girondot [e-mail]

References

Girondot M, Laurin M (2003) Bone Profiler: a tool to quantify, model, and statistically compare bone-section compactness profiles. Journal of Vertebrate Paleontology 23: 458-461

Laurin M, Girondot M, Loth M-M (2004) The evolution of long bone microstructure and lifestyle in lissamphibians. Paleobiology 30: 589-613

Gônet, Jordan, Jérémie Bardin, Marc Girondot, John R. Hutchinson, and Michel Laurin. 2022. The Reptilian Locomotor and Postural Diversity Seen through the Prism of Femoral Microanatomy: Paleobiological Implications for Some Permian and Mesozoic Reptiles. Submitted

Gônet, Jordan, Michel Laurin, and Marc Girondot. 2022. “Bone Profiler: The Next Step to Quantify, Model and Statistically Compare Bone Section Compactness Profiles.” Paleontologica Electronica. 25(1): a12

Examples

## Not run: 
# Not run:
library(BoneProfileR)
path_Hedgehog <- system.file("extdata", "Erinaceus_europaeus_fem_2-1_small.png", 
                             package = "BoneProfileR")
 bone <- BP_OpenImage(file=path_Hedgehog)
 plot(bone, type="original")
 bone <- BP_DetectBackground(bone=bone, analysis="logistic")
 bone <- BP_DetectForeground(bone=bone, analysis="logistic")
 bone <- BP_DetectCenters(bone=bone, analysis="logistic")
 plot(bone, type="original")
 plot(bone, type="mineralized")
 plot(bone, type="unmineralized")
 plot(bone, type="section")
 plot(bone, type="colors")
 plot(bone, type="3Dcolors")
 bone <- BP_EstimateCompactness(bone, analysis="logistic", center="ontogenetic")
 plot(bone, type="original")
 plot(bone, type="mineralized")
 plot(bone, type="observations")
 bone <- BP_FitMLCompactness(bone, analysis="logistic")
 plot(bone, type="model", analysis=1)
 plot(bone, type="observations+model", analysis=1)
 fittedpar <- BP_GetFittedParameters(bone, analysis="logistic")
 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", alloptim=TRUE), 
             Flexit=BP_GetFittedParameters(bone, analysis="flexit", alloptim=TRUE))
# pdf(file = "Figure 2.pdf", width = 8, height = 10, pointsize = 12)
layout(1:2)
plot(bone, type="observations+model", analysis="logistic", restorePar=FALSE, mar=c(4, 4, 2, 5))
plot(bone, type="observations+model", analysis="flexit", restorePar=FALSE, mar=c(4, 4, 2, 5))
layout(1)
# dev.off()
 out4p <- plot(bone, type="observations+model", analysis="logistic")
 out6p <- plot(bone, type="observations+model", analysis="flexit")
 bone <- BP_FitBayesianCompactness(bone, analysis="logistic")
 plot(bone, type="observations+model", CI="MCMC")
 bone <- BP_FitBayesianCompactness(bone, analysis="flexit")
 plot(bone, type="observations+model", CI="MCMC", analysis="flexit")
 plot(bone, type="mcmc", parameter="P", 
      options.mcmc=list(xlim=c(0.55, 0.57), breaks=seq(from=0, to=1, by=0.001)))
 plot(bone, type="mcmc", parameter="S", 
      options.mcmc=list(xlim=c(0.02, 0.05), breaks=seq(from=0.02, to=.05, by=0.001)))
 plot(bone, type="mcmc", parameter="Min", 
      options.mcmc=list(xlim=c(0.05, 0.08), breaks=seq(from=0, to=1, by=0.001)))
 plot(bone, type="mcmc", parameter="Max", 
      options.mcmc=list(xlim=c(0.95, 0.97), breaks=seq(from=0, to=1, by=0.001)))
 outMCMC <- RM_get(x = bone, RM = "RM", RMname = "logistic", valuename = "mcmc")
 summary(outMCMC)
 outMCMC <- RM_get(x = bone, RM = "RM", RMname = "flexit", valuename = "mcmc")
 summary(outMCMC)
 # pdf(file = "Figure 3.pdf", width = 8, height = 10, pointsize = 12)
 layout(1:2)
 plot(bone, type="mcmc", parameter="K1", analysis="flexit", 
      options.mcmc=list(xlim=c(-1, 3), ylim=c(0,10), 
      breaks=seq(from=-1, to=3, by=0.001), 
      legend = FALSE, show.prior = FALSE, mar=c(4, 4, 1, 6)), restorePar=FALSE)
 segments(x0=1, x1=1, 
         y0=0, y1=10, lty=4, lwd=3)
 text(x=ScalePreviousPlot(x=0.95, y=0.95)$x, 
      y=ScalePreviousPlot(x=0.95, y=0.95)$y, labels="A", cex=3)
 plot(bone, type="mcmc", parameter="K2", analysis="flexit", 
      options.mcmc=list(xlim=c(-1, 3), ylim=c(0,10), 
      breaks=seq(from=-1, to=3, by=0.001), 
      legend = FALSE, show.prior = FALSE, mar=c(4, 4, 1, 6)), restorePar=FALSE)
 segments(x0=1, x1=1, 
         y0=0, y1=10, lty=4, lwd=3)
 text(x=ScalePreviousPlot(x=0.95, y=0.95)$x, 
      y=ScalePreviousPlot(x=0.95, y=0.95)$y, labels="B", cex=3)
 # dev.off()
 
 bone <- BP_FitMLRadialCompactness(bone, analysis = "flexit")
 plot(bone, type="radial", radial.variable=c("P", "S"), analysis = "flexit")
 plot(bone, type="radial", radial.variable=c("P", "S", "Min", "Max"), analysis = "flexit")
 out <- RM_get(x=bone, RMname="flexit", valuename = "optimRadial")$synthesis
 mean(out[, "P"]); sd(out[, "P"])
 range(out[, "S"])
 quantile(out[, "S"])
 # pdf(file = "Figure 4.pdf", width=7, height = 9, pointsize = 12)
 layout(1:2)
 plot(bone, type="radial", radial.variable="P", analysis = "flexit", restorePar=FALSE)
 text(x=ScalePreviousPlot(x=0.95, y=0.95)$x, 
      y=ScalePreviousPlot(x=0.95, y=0.95)$y, labels="A", cex=3)
 plot(bone, type="radial", radial.variable="S", analysis = "flexit", restorePar=FALSE)
 text(x=ScalePreviousPlot(x=0.95, y=0.95)$x, 
      y=ScalePreviousPlot(x=0.95, y=0.95)$y, labels="B", cex=3)
 # dev.off()

## End(Not run)

BoneProfileR documentation built on Sept. 7, 2022, 1:06 a.m.