View source: R/BP_FitMLCompactness.R
| BP_FitMLCompactness | R Documentation | 
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.
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
)
| 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? | 
BP_FitMLCompactness estimates likelihood of model of a bone section
The -Ln L
Marc Girondot marc.girondot@gmail.com
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()
## 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)
 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")
## End(Not run)
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