fast (version 0.64)

sensitivity_rep: Repeat calculation of sensitivities for lots of data

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 ...

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)