Description Details Author(s) References Examples
Inference of interaction networks based on the parameterization of generalized Lotka Volterra models on timeseries data
The DESCRIPTION file:
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~~ An overview of how to use the package, including the most important functions ~~
Lukas Hirsch, Florian Centler Maintainer: Lukas Hirsch <lukashirsch@gmail.com>
~~ Literature or other references for background information ~~
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | library(gLVInterNetworks)
data <- gLVgenerateData(species = 2, number_of_interactions = 2, timepoints = 100, noise = 0.01, testData = 20)
## Not run: plot(data, type = "l")
lr <- gLVlinearRegression(data, regularization = TRUE, alpha = 0)
## Not run: summary(lr)
## Not run: plot(lr, type = "l")
## Not run: points(data)
nlr <- gLVnonlinearRegression(data, parms0 = lr$Parms)
## Not run: summary(nlr)
## Not run: plot(nlr, type = "l")
## Not run: points(data)
## Not run: par(mfrow = c(1,2))
## Not run: plotGraph(data, vsize = 0.2, main = "Original interaction network", verbose = TRUE )
## Not run: plotGraph(nlr, vsize = 0.2, main = "Inferred interaction network", verbose = TRUE)
ident <- sensitivityAnalysis(nlr$Parms)
## Print summary of sensitivity matrix
summary(ident$sens)
## Print collinearity index for all parameters together
ident$coll[ident$coll[,"N"]==length(data$Parms),]
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