sensitivity_rep: Repeat calculation of sensitivities for lots of data

In fast: Implementation of the Fourier Amplitude Sensitivity Test (FAST)

Description

This function calculates the sensitivity for a series of data, e.g. a time series.

Usage

sensitivity_rep(data.zoo, xval = index(data.zoo), direction=1, 
		data = coredata(data.zoo), numberf, 
		order=4, legend = paste("P", 1:order, sep = ""), 
		cukier=TRUE, reorder = 1:numberf, ...)

Arguments

data	Array of data to use of the form todo
data.zoo	Zoo object containing data with one row per parameter set
xval	Vector to use as x data for plotting
direction	Dimension which contains the todo
numberf	Number of parameters used
order	Order of parameter frequency independence (see Cukier1975)
legend	legend text to plot
cukier	boolean. Indicates wheter to use freq_cukier
reorder	A vector of indices that allows to use a different order for the parameters. This is important to check effects of the sampling scheme on results. Use the same as for fast_parameters
...	Arguments passed to p.sensitivity

Value

An array of sensitivities of the form ...

Author(s)

Dominik Reusser

See Also

fast, sensitivity

Examples

#The model depends on 4 parameters
#
#It produces a weighted sum of the 4 parameters and returns this sum
#
#The weights depend on an additional parameter x=1:200 
example_model2(p=c(1,3,1,1),fig=TRUE)
example_model2(p=c(1,2,2,3),fig=TRUE)
paras<-fast_parameters(min=c(0,0,0,0),max=c(1,2,2,3))
paras
model_results <- apply(paras, 1, example_model2)
model_results
sensitivity <- sensitivity_rep(data = model_results, xval=1:200, direction = 1, order=4 , numberf=4)
p.sensitivity(sen=sensitivity, xval=1:200, legend=names(paras))

#Alternatively with zoo object
result.zoo <- zoo(model_results, order.by=1:200)
sensitivity <- sensitivity_rep(data.zoo = result.zoo, numberf=4)

fast documentation built on May 2, 2019, 5:56 a.m.