GPoM (version 1.0)

predictab: predictab : estimate the models predictability of gPoMo output

Description

The algorithm aims to estimate automatically the predictability of the models obtained with `gPoMo`.

Usage

predictab(ogp, fullt = NULL, fulldata = NULL, hp = NULL, Nech = 50,
  show = 1, selecmod = NULL, id = 1)

Arguments

ogp

The output list obtained from gPoMo.

fullt

Time vector of the data set for which predictability will be tested

fulldata

Data set for which predictability will be tested

hp

Time vector of the horizon of prediction

Nech

Number of simulations

show

Indicates (2) or not (0-1) the algorithm progress

selecmod

a vector of the model selected.

id

the type of model to identify. id = 1 correspond to the unidentified models, that is, potentialy chaotic models).

Value

ErrmodAll A list of matrix $Errmod1, $Errmod2, etc. providing the forecasting error of each model 1, 2, etc. Each column corresponds to one simulation starting from specific initial condition. Each line corresponds to the the horizon of prediction. Vectors corresponding to the initial condition time tE and the horizon of prediction hpE are also provided in $tE and $hpE, respectively.

Examples

# NOT RUN {
# load data
data("Ross76")
# time vector
tin <- Ross76[seq(1, 3000, by = 8), 1]
# single time series
data <- Ross76[seq(1, 3000, by = 8), 3]
# dev.new()
# plot(tin, data, xlab = 'time', ylab = 'y(t)')

# global modelling
# results are put in list outputGPoM
outputGPoM <- gPoMo(data[1:300], tin = tin[1:300], dMax = 2, nS=c(3),
                    show = 0, method = 'rk4',
                    nPmin = 10, nPmax = 12,
                    IstepMin = 150, IstepMax = 151)
#
visuOutGP(outputGPoM)

###########################
# and test predictability #
###########################
outpred <- predictab(outputGPoM, hp = 15, Nech = 30)

# manual visualisation of the outputs (e.g. for model 1):
dev.new()
image(outpred$tE, outpred$hpE, t(outpred$Errmod1),
xlab = 't', ylab = 'hp', main = 'Errmod1')

# }