# Estimation of the likelihood of a bone section 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. ```r BP_FitMLCompactness( bone, fitted.parameters = c(P = 0.5, S = 0.02, Min = 0.001, Max = 0.999), priors = NULL, fixed.parameters = c(K1 = 1, K2 = 1), twosteps = TRUE, replicates.CI = 10000, analysis = 1, control.optim = list(trace = 1), silent = FALSE ) ``` ## 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 - `control.optim`: The list of options for optim. - `silent`: Should information be shown? ## Returns The -Ln L ## Details BP_FitMLCompactness estimates likelihood of model of a bone section ## Examples ```r ## Not run: # 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, analysis="logistic", ML=TRUE, return.all=FALSE) 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", ML=TRUE, return.all=FALSE)[, "mean"] 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", , 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") ## 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_FitMLPeriodicCompactness()`, `BP_FitMLRadialCompactness()`, `BP_GetFittedParameters()`, `BP_ListAnalyses()`, `BP_LnLCompactness()`, `BP_OpenImage()`, `BP_Report()`, `Erinaceus_europaeus`, `plot.BoneProfileR()`, `summary.BoneProfileR()` ## Author(s) Marc Girondot marc.girondot@gmail.com

BP_FitMLCompactness function