BoneProfileR (version 3.1)

BP_FitMLCompactness: Estimation of the likelihood of a bone section

Description

Estimation of the model of compactness of a bone section.
The two-steps analysis performs first a quasi-Newton method, then a Bayesian MCMC and finally again a quasi-Newton method. It generally ensures that global minimum is found. On the other hand, it doubles the time to complete.

Usage

BP_FitMLCompactness(
  bone,
  fitted.parameters = c(P = 0.5, S = 0.05, Min = 0.001, Max = 0.999),
  priors = NULL,
  fixed.parameters = c(K1 = 1, K2 = 1),
  twosteps = TRUE,
  replicates.CI = 10000,
  analysis = 1,
  silent = FALSE
)

Value

The -Ln L

Arguments

bone

The bone image to be used

fitted.parameters

Parameters of the model to be fitted

priors

Priors used for intermediate estimations

fixed.parameters

Fixed parameters of the model

twosteps

Does a 2-steps analysis be performed?

replicates.CI

Number of replicates to estimate confidence interval

analysis

Name or rank of analysis

silent

Should information be shown?

Author

Marc Girondot

Details

BP_FitMLCompactness estimates likelihood of model of 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_FitMLRadialCompactness(), BP_GetFittedParameters(), BP_ListAnalyses(), BP_LnLCompactness(), BP_OpenImage(), BP_Report(), Erinaceus_europaeus, plot.BoneProfileR(), summary.BoneProfileR()

Examples

if (FALSE) {
# Not run:
library(BoneProfileR)
 bone <- BP_OpenImage()
 # or, to use the package imager to open a tiff image
 bone <- BP_OpenImage(ijtiff=TRUE)
library(BoneProfileR)
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")
 plot(bone, type="mineralized", show.grid=FALSE)
 plot(bone, type="unmineralized", show.grid=FALSE)
 plot(bone, type="section", show.grid=FALSE)
 bone <- BP_FitMLCompactness(bone, analysis="logistic", twosteps=TRUE)
 BP_GetFittedParameters(bone)
 plot(bone)
 plot(bone, type="observations")
 plot(bone, type="observations+model", analysis=1)
 bone <- BP_DuplicateAnalysis(bone, from="logistic", to="flexit")
 fittedpar <- BP_GetFittedParameters(bone, analysis="logistic")
 bone <- BP_DuplicateAnalysis(bone, from="logistic", to="flexit")
 BP_ListAnalyses(bone)
 bone <- BP_FitMLCompactness(bone, 
                fitted.parameters=c(fittedpar, K1=1, K2=1), 
                fixed.parameters=NULL, analysis="flexit", twosteps=TRUE)
 compare_AIC(Logistic=BP_GetFittedParameters(bone, analysis="logistic", alloptim=TRUE), 
             Flexit=BP_GetFittedParameters(bone, analysis="flexit", alloptim=TRUE))
 out4p <- plot(bone, type="observations+model", analysis="logistic")
 out6p <- plot(bone, type="observations+model", analysis="flexit")
}