README.md

In levimcclenny/BoolFilter: Optimal Estimation of Partially Observed Boolean Dynamical Systems

Description

This package contains tools for optimal and approximate state estimation as well as network inference of Partially-Observed Boolean Dynamical Systems.

Use

Deploy the package:

library('BoolFilter')
#> Loading required package: Rlab
#> Rlab 2.15.1 attached.
#> 
#> Attaching package: 'Rlab'
#> The following objects are masked from 'package:stats':
#> 
#>     dexp, dgamma, dweibull, pexp, pgamma, pweibull, qexp, qgamma,
#>     qweibull, rexp, rgamma, rweibull
#> The following object is masked from 'package:datasets':
#> 
#>     precip
#> Loading required package: BoolNet

A few examples of the basic use of the package are shown below:

Data Generation, Filtering, and visualization

data(p53net_DNAdsb0) 

#Simulate data from a Bernoulli observation model
data <- simulateNetwork(p53net_DNAdsb0, n.data = 100, p = 0.02,
                        obsModel = list(type = "Bernoulli",
                                          p = 0.02))

#Derive an estimate of the network using a BKF approach
Results <- BKF(data$Y, p53net_DNAdsb0, .02,
                        obsModel = list(type = "Bernoulli",
                                          p = 0.02))

#View network approximation vs. correct trajectory
plotTrajectory(Results$Xhat,
                labels = p53net_DNAdsb0$genes,
                dataset2 = data$X,
                compare = TRUE)

Model Selection

BoolFilter comes with capibilites for Multiple-Model Adaptive Estimation (citation in vignette), in which model selection and parameter estimation is made possible by implementing a bank of Boolean Kalman Filters running in parallel.

data(p53net_DNAdsb1)

net1 <- p53net_DNAdsb0
net2 <- p53net_DNAdsb1

#define observation model
observation = list(type = 'NB', s = 10.875, mu = 0.01, delta = c(2, 2, 2, 2), phi = c(3, 3, 3, 3))

#simulate data using one of the networks and a given 'p'
data <- simulateNetwork(net1, n.data = 100, p = 0.02, obsModel = observation)

#run MMAE to determine model selection and parameter estimation
MMAE(data, net=c("net1","net2"), p=c(0.02,0.1,0.15), threshold=0.8, obsModel = observation)
#>  The infered model is
#>  p = 0.02
#>  Net = net1
#>  The selected model is infered with 7 data.

More Information

More information can be found in the packages vignette, including more detailed examples and explainations of the individual algorithms included in the package.

References

All references for the above can be found in the vignette references section.

levimcclenny/BoolFilter documentation built on May 21, 2019, 5:11 a.m.