BP_FitBayesianPeriodicCompactness {BoneProfileR} | R Documentation |
Estimation of the likelihood of a bone section
Description
Estimation of the compactness of a bone section using Bayesian periodic model.
To control the parallel computing, use:
options(mc.cores = [put here the number of cores you want use])
options(forking = FALSE) or options(forking = TRUE)
The maximum number of cores is obtained by: parallel::detectCores()
Usage
BP_FitBayesianPeriodicCompactness(
bone,
fitted.parameters = NULL,
priors = NULL,
fixed.parameters = NULL,
analysis = 1,
silent = FALSE,
replicates.CI = 2000,
amplitude.max = 0.1,
control.MHalgoGen = list(n.iter = 10000, n.chains = 1, trace = TRUE, n.adapt = 5000,
thin = 1, adaptive = TRUE)
)
Arguments
bone |
The bone image to be used |
fitted.parameters |
Parameters of the model to be fitted |
priors |
The priors of Bayesian analysis |
fixed.parameters |
Fixed parameters of the model |
analysis |
Name or rank of analysis |
silent |
Should the function displays some information? |
replicates.CI |
Number of replicates to estimate confidence interval using Hessian |
amplitude.max |
The maximum allowed amplitude for each parameter |
control.MHalgoGen |
The control parameters of MHalgoGen() |
Details
BP_FitBayesianPeriodicCompactness estimates likelihood of global model of a bone section
Value
The -Ln L
Author(s)
Marc Girondot marc.girondot@gmail.com
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_FitMLCompactness()
,
BP_FitMLPeriodicCompactness()
,
BP_FitMLRadialCompactness()
,
BP_GetFittedParameters()
,
BP_ListAnalyses()
,
BP_LnLCompactness()
,
BP_OpenImage()
,
BP_Report()
,
Erinaceus_europaeus
,
plot.BoneProfileR()
,
summary.BoneProfileR()
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)
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", cut.angle = 60)
bone <- BP_FitMLCompactness(bone, analysis="logistic", twosteps=TRUE)
plot(bone, type="observations+model", analysis="logistic")
par <- BP_GetFittedParameters(bone, analysis="logistic", ML=TRUE, return.all=FALSE)[, "mean"]
options(mc.cores=parallel::detectCores())
#############################################
# Periodic analysis
#############################################
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)
bone <- BP_FitBayesianPeriodicCompactness(bone, analysis="logistic", replicates.CI=2000)
mcmc <- RM_get(bone, RMname="logistic", valuename="mcmcPeriodic")
plot(mcmc, parameters="P", what="MarkovChain", ylim=c(0.555, 0.565), main="P parameter")
plot(bone, type="mcmcPeriodic", parameter.name="compactness", col=rainbow(128))
plot(bone, type="mcmcPeriodic", parameter.name="compactness",
col=hcl.colors(12, "YlOrRd", rev = TRUE))
plot(bone, type="mcmcPeriodic", parameter.name="averagemodel")
plot(bone, type="mcmcPeriodic", parameter.name="P",
rgb(red = 0.7, green = 0.7, blue = 0.7, alpha = 0.2))
plot(bone, type="mcmcPeriodic", parameter.name="P", ylim=c(0, 1),
rgb(red = 0.7, green = 0.7, blue = 0.7, alpha = 0.2))
## End(Not run)