---
title: "GPoM : 3 Modelling"
author: "Sylvain Mangiarotti & Mireille Huc"
date: "`r Sys.Date()`"
output:
  pdf_document:
    number_sections: no
    latex_engine: xelatex
vignette: >
  %\VignetteIndexEntry{GPoM : 3 Modelling}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
load package: "`r library(GPoM)`"
---

## Global Modelling

This document gives an illustration on how to apply
the global modelling technique in various contexts.
The aim of the `GPoM` package is to obtain
models of Ordinary Differential Equations (ODEs)
of polynomial form from time series (either single
or multiple).
Such type of modelling requires a careful
time series preprocessing.
Such data preprocessing was sketched in
the vignette `2 PreProcessing`.
In the present vignette, it is assumed that
the studied time series has already been carefully
preprocessed, so that the modelling tools can now
be applied without anymore preprocessing.


## Single time series modelling

The chaotic system introduced by Rössler in 1976^[O. Rössler,
An Equation for Continuous Chaos, *Physics Letters*,
**57A**(5), 1976, 397-398.]
is well adapted to test and illustrate the performances of
the approach to model nonlinear dynamics.
Simulations of this chaotic system are available in the GPoM
package and can be directly loaded as follows:
```{r, eval = TRUE}
# load data
data("Ross76")
```

(note that the GPoM package can easily be used to produce such
chaotic data set, see vignette `1 Conventions`).

The loaded variable `Ross76` is a matrix. The time
vector is in the first column and the time series
$x(t)$, $y(t)$ and $z(t)$ of the three variables are
in the next columns two to four.
The global modelling technique from one single time series
is introduced in the present section.
The variable $y$ will be considered here:
```{r, eval = TRUE, fig.align='center'}
# time vector
tin <- Ross76[,1]
# single time series
data <- Ross76[,3]
# plot
plot(tin, data, type='l', xlab = 't', ylab = 'y(t)', main = 'Original time series')
```

Starting from single time series, the following canonical
form of equations can be expected^[G. Gouesbet & C. Letellier, 1994.
Global vector-field reconstruction by using a multivariate
polynomial L2 approximation on nets. *Physical Review E*, **49**(6), 4955-4972.]:

$dX_1/dt = X_2$

$dX_2/dt = X_3$

$...$

$dX_n/dt = P(X_1, X_2, ..., X_n)$

where the modeled variable $X_1$ will correspond to
the observed variable $y$, and $X_2$ to $X_n$ are
its successive derivatives $dy/dt$ and $dy^2/dt^2$.
To apply the global modelling technique to the time series
presented above, it is necessary to choose a trial model
dimension `nVar` (corresponding to $n$ in the previous
equation) and a maximal polynomial degree `dMax`.
To restrict the model search to a smaller ensemble of models,
the minimum and maximum numbers of parameters for the model
can be chosen manually using `nPmin` and `nPmax`.
`IstepMax` corresponds to the maximum number of iterations
to be used for the numerical integration.

```{r, echo = TRUE}
# model dimension
nVar = 3
# maximum polynomial degree
dMax = 2
```
```{r, eval = FALSE, echo = TRUE}
# generalized Polynomial modelling
out1 <- gPoMo(data, tin = tin, dMax = dMax, nS = nVar, show = 0,
              nPmin = 9, nPmax = 11, verbose = FALSE, IstepMax = 3000)
```

Note that, to follow the search process in real time,
parameter `show` should be set on (`show = 1`).
In practice, the model size (that is, its number of parameters which is 
bounded by `nPmin` and `nPmax`)
is generally unknwown. It may thus be necessary to extend its range
(be default `nPmin = 1` and `nPmax = 14`).
However, in practice, `nPmax` cannot be chosen too large in order
to keep the computation time reasonable.

With the parameterization chosen here, two models are obtained:

```{r, eval = FALSE}
which(out1$okMod == 1)
```
```{r, eval = TRUE, echo = FALSE}
which(data_vignetteIII$out1okMod == 1)
```

The model #3 shows a quite good performance to reproduce
the original phase portrait:

```{r, eval = FALSE, fig.align='center', fig.width=4, fig.height=4}
# obtained model #3
plot(out1$stockoutreg$model3[,1], out1$stockoutreg$model3[,2], type='l', col = 'red',
     xlab = 'y(t)', ylab = 'dy(t)/dt', main = 'Phase portrait')
# original phase portrait
lines(out1$filtdata[,1], out1$filtdata[,2])
legend(3,2,c("model", "original"), col=c('red', 'black'), lty=c(1,1), cex=0.6)

```
```{r, eval = TRUE, echo = FALSE, fig.align='center', fig.width=4, fig.height=4}
# obtained model #3
library(float)
plot(dbl(data_vignetteIII$out1_stockoutreg_m3[,1]), dbl(data_vignetteIII$out1_stockoutreg_m3[,2]),
     type='l', col = 'red',
     xlab = 'y(t)', ylab = 'dy(t)/dt', main = 'Phase portrait')
# original phase portrait
lines(dbl(data_vignetteIII$out1_filtdata[,1]), dbl(data_vignetteIII$out1_filtdata[,2]))
legend(3,2,c("model", "original"), col=c('red', 'black'), lty=c(1,1), cex=0.6)

```
The equations of the obtained model can be edited as follows
(more precision in the digits can be obtained choosing a larger
value of `approx`):

```{r, eval = FALSE}
# obtained model #3
visuEq(nVar, dMax, out1$models$model3, approx = 1)
```
```{r, eval = TRUE, echo = FALSE}
# obtained model #3
visuEq(nVar, dMax, data_vignetteIII$out1_m3, approx = 1)
```
Various methods may be used to validate such a model.
In practice, note that validation may depend on the
objective of the model (characterization, forecasting, etc.).
One way to validate the model can be done by considering
the forecasting performances of the model.
A code dedicated to such a type of validation
is presented in vignette `5 Predictability`.


## Detection of causal couplings and retro-modelling

One interest of the global modelling technique is
(1) to enable the detection of nonlinear dynamical couplings
between observed variables,
and potentially even (2) to obtain an analytical
formulation of this couplings in order
(3) to help for the inference of the causal relations.

Here again, the Rössler-1976 chaotic system is used
to illustrate the purpose.
The time series of the three variables $x$, $y$ and $z$
are used this time.


```{r, eval = TRUE}
# multiple (three) time series
data <- Ross76[,2:4]
```

The three time series are as follows:

```{r, eval = TRUE, fig.align='center'}
# x(t)
plot(tin, data[,1], ylim = c(-6.5,12), type='l', col = 'blue',
     xlab = 't', ylab = '', main = 'Original time series')
# y(t)
lines(tin, data[,2], type = 'l', col = 'orange')
# z(t)
lines(tin, data[,3], type = 'l', col = 'brown')
legend(35,12,c("x(t)", "y(t)", "z(t)"), col=c('blue', 'orange', 'brown'), lty=1, cex=0.8)

```

A set of three equations is expected, one for each
variable:

$dx/dt = P_x(x,y,z)$

$dy/dt = P_y(x,y,z)$

$dz/dt = P_z(x,y,z)$

To define such a structure, the number `nS` of equations
for each series must be defined as a vector such as:
`nS = c(1,1,1)`. (note that to discard information about
the current number of models under test, the optional
parameter `verbose` can be set to `verbose = FALSE`).


```{r, eval = FALSE}
# generalized Polynomial modelling
out2 <- gPoMo(data, tin = tin, dMax = 2, nS = c(1,1,1),  show = 0,
              IstepMin = 10, IstepMax = 3000, nPmin = 7, nPmax = 8)
```

The simplest obtained model able to reproduce
the observed dynamics is the model #5:

```{r, eval = FALSE, fig.align='center', fig.width=4, fig.height=4}
# obtained model #5
plot(out2$stockoutreg$model5[,1], out2$stockoutreg$model5[,2],
     type='l', col = 'red',
     xlab = 'x(t)', ylab = 'y(t)', main = 'Phase portrait')
# original phase portrait
lines(out2$filtdata[,1], out2$filtdata[,2])
legend(3,2,c("original", "model"), col=c('black', 'red'), lty=1, cex=0.5)

```
```{r, eval = TRUE, echo = FALSE, fig.align='center', fig.width=4, fig.height=4}
# obtained model #5
plot(dbl(data_vignetteIII$out2_stockoutreg_m5[,1]), dbl(data_vignetteIII$out2_stockoutreg_m5[,2]),
     type='l', col = 'red',
     xlab = 'x(t)', ylab = 'y(t)', main = 'Phase portrait')
# original phase portrait
lines(dbl(data_vignetteIII$out2_filtdata[,1]), dbl(data_vignetteIII$out2_filtdata[,2]))
legend(3,2,c("original", "model"), col=c('black', 'red'), lty=1, cex=0.5)

```

The equations of the obtained model are given by:

```{r, eval = FALSE}
visuEq(3, 2, out2$models$model5, approx = 4)
```
```{r, eval = TRUE, echo = FALSE}
visuEq(3, 2, data_vignetteIII$out2_m5, approx = 4)
```

The original Rössler system is thus retrieved.
The ability of the approach to retrieve original
equations was also tested to numerous other
dynamical systems (see vignette `7 Retro-modelling`).

A practical application of the global modelling technique
under real world conditions was done for modelling
the plague epidemic of Bombay by the end of 19th century
and its coupling to the epizootics among black and brown
rats^[S. Mangiarotti, 2015. Low dimensional chaotic models
for the plague epidemic in Bombay (1896–1911).
*Chaos, Solitons & Fractals*, **81A**, 184–196.].
This analysis enabled not only the detection of
the couplings but also to obtain an algebraic
formulation of this coupling.
An interpretation could be proposed for all the terms
of the obtained model.


## Generalized global modelling and polynomial a priori structure

The GPoM package enables a generalized formulation
of the global modelling technique combining the canonical
formulation -- one single observed variables and its
derivatives -- and the multivariate formulation -- several
observed variables considered together.
Examples are given in the two previous sections.
The two variables $x$ and $y$ of the Rössler-1976
chaotic system are considered here to illustrate
the purpose.

```{r, eval = TRUE}
# time vector
tin <- Ross76[,1]
# multiple (two) time series
data <- Ross76[,2:3]
```

The two time series are as follows:

```{r, eval = TRUE, fig.align='center'}
# x(t)
plot(tin, data[,1], ylim = c(-6.5,6.5),
     type='l', col = 'blue',
     xlab = 't', ylab = '', main = 'Original time series')
# y(t)
lines(tin, data[,2], type = 'l', col = 'brown')
legend(30,6,c("x(t)", "y(t)"), col=c('blue', 'brown'), lty=1, cex=0.8)

```

Obviously, the correlation between the two time series
is not very high since close to 0.5 in magnitude
(Note that alternative examples with very low levels
of correlations will also be considered in vignette
`7 Retro-modelling`):

```{r, eval = TRUE, fig.align='center'}
# correlation between Rössler-x and Rössler-y
cor(data[,1], data[,2])
```

Though, their dynamics are linked in a causal way
since dynamically coupled as expressed by the original
(fully deterministic) system:

$dx/dt = - y - z$

$dy/dt = x + ay$

$dz/dt = b + z(x - c)$.

Because $x$ and $y$ are coupled to the third variable $z$
(assumed not to be observed in the present example),
and because of the presence of a nonlinear term
($xz$ in the last equation), the relation between
$x$ and $y$ can only be poorly detected using
a simple statistical technique such as the correlation.
This difficulty directly results from the complex
coevolution between these two variables as
illustrated by the following scatter plot (x,y):

```{r, eval = TRUE, fig.align='center', fig.width=4, fig.height=4}
plot(data[,1], data[,2],
     type='p', cex = 0.2, col = 'blue',
     xlab = 'x', ylab = 'y', main = 'Scatter plot (x,y)')
```

In its principle, the global modelling technique
is well adapted to tackle the detection of nonlinear
couplings.

As the Rössler-1976 system is three-dimensional,
a three-dimensional system can be expected,
that is, such as `sum(nS) = 3` (in practice, this
dimension is generally unknown and a trial dimension
can be used).
Two formulations may thus be expected,
either based on variables ($X_1$, $X_2$, $Y_1$):

$dX_1/dt = X_2$

$dX_2/dt = P_X(X_1, X_2, Y_1)$

$dY_1/dt = P_Y(X_1, X_2, Y_1)$,

which general structure will be defined
by `nS = c(2,1)` (two equations of canonical
form will be generated from the observed 
time series $x(t)$, a single one from $y(t)$);

or based on variables ($X_1$, $Y_1$, $Y_2$):

$dX_1/dt = P_X(X_1, Y_1, Y_2)$

$dY_1/dt = Y_2$

$dY_2/dt = P_Y(X_1, Y_1, Y_2)$

which general structure will be defined
by `nS = c(1,2)` (one equation generated from $x(t)$,
two ones of canonical form from $y(t)$).

For a tutorial purpose, we will assume here
that some *a priori* information is available
about the model sructure telling us that
the former formulation based on variables
$X_1$, $X_2$ and $Y_1$ is more suited
and that specific polynomial terms should
be excluded (e.g. $Y_1^2$ and $X_2Y_1$
should be excluded from polynomial $P_X$,
and only the terms $Y_1$, $X_1$ and $X_1Y_1$
should be accepted in polynomial $P_Y$).
It is then possible to provide a template
for the model formulation allowing some
terms and forbidding others.
This template structure can be provided as an
input of the `gPoMo` function through the optional
parameter `EqS`.
In the present case, the following structure
will be used as a template:

```{r, eval = TRUE}
# model template:
EqS <- matrix(1, ncol = 3, nrow = 10)
EqS[,1] <- c(0,0,0,1,0,0,0,0,0,0)
EqS[,2] <- c(1,1,0,1,0,1,1,1,1,1)
EqS[,3] <- c(0,1,0,0,0,0,1,1,0,0)
visuEq(3, 2, EqS, substit = c('X1','X2','Y1'))
```

Each term of this template can be used by the global model
if set to 1 and cannot if set to 0.
In other words, terms that are not included in this template
cannot be added, but terms that are included may be removed.

The search for a Generalized Global Model will be launched
as follows:

```{r, eval = FALSE}
# generalized Polynomial modelling
out3 <- gPoMo(data, tin = tin, dMax = 2, nS=c(2,1), EqS = EqS,
              show = 0, verbose = FALSE,
              IstepMin = 10, IstepMax = 2000, nPmin = 9, nPmax = 11)
```

The simplest obtained model able to reproduce the observed
dynamics is found to be the model #2:

```{r, eval = FALSE, fig.align='center', fig.width=4, fig.height=4}
# obtained model #2
plot(out3$stockoutreg$model2[,1], out3$stockoutreg$model2[,2],
     type='l', col = 'red',
     xlab = 'X1', ylab = 'X2', main = 'Phase portrait')
# original phase portrait
lines(out3$filtdata[,1], out3$filtdata[,2])
legend(-3,-5,c("original", "model"), col=c('black', 'red'), lty=1, cex=0.5)

```
```{r, echo = FALSE, eval = TRUE, fig.align='center', fig.width=4, fig.height=4}
# obtained model #2
plot(dbl(data_vignetteIII$out3_stockoutreg_m2[,1]), dbl(data_vignetteIII$out3_stockoutreg_m2[,2]),
     type='l', col = 'red',
     xlab = 'y(t)', ylab = 'dy(t)/dt', main = 'Phase portrait')
# original phase portrait
lines(dbl(data_vignetteIII$out3_filtdata[,1]), dbl(data_vignetteIII$out3_filtdata[,2]))
legend(-3,-5,c("original", "model"), col=c('black', 'red'), lty=1, cex=0.5)

```
The equations of the obtained model are the following :

```{r, eval = FALSE}
visuEq(3, 2, out3$models$model2, approx = 4, substit = c('X1','X2','Y1'))
```
```{r, eval = TRUE, echo = FALSE}
visuEq(3, 2, data_vignetteIII$out3_m2, approx = 4, substit = c('X1','X2','Y1'))
```
When no information is available about the polynomial
structure, the model search can be launched without using
the option `EqS`. In this situation, all the terms
are considered as possible terms (all the terms are
set to 1 in `EqS`).
When there is no reason to choose one formulation
rather than the other,
then all the formulations may be tested one by one.
In the present case, models can actually be obtained with both
sets of variables ($X_1$,$Y_1$,$Y_2$) and ($X_1$,$X_2$,$Y_1$)).

A practical application of the generalized
global modelling technique 
could be performed from observational data under real world
conditions for the modelling of the West-Africa epidemic
of Ebola Virus Desease^[S. Mangiarotti, M. Peyre & M. Huc, 2016.
A chaotic model for the epidemic of Ebola virus disease in West
Africa (2013–2016). *Chaos*, **26**, 113112.].


## Blind separation and modelling of two independant sets of equations

Finally, since the approach enables -- in its principle -- 
to detect nonlinear couplings, it may also be used to detect
separated sets of equations.
Two separated systems are considered here:
the Rössler-1976 system already presented upper
and the Sprott-K system^[J.C. Sprott, 1994. Some simple chaotic
flows. *Physical Review E*, **50**(2), 647-650].

```{r, eval = TRUE}
# load data
data("TSallMod_nVar3_dMax2")
# multiple (six) time series
tin <- TSallMod_nVar3_dMax2$SprK$reconstr[1:400,1]
TSRo76 <- TSallMod_nVar3_dMax2$R76$reconstr[,2:4]
TSSprK <- TSallMod_nVar3_dMax2$SprK$reconstr[,2:4]
data <- cbind(TSRo76,TSSprK)[1:400,]
```

Six time series are thus considered in this
illustration, three for each system:
```{r, eval = TRUE, fig.align='center'}
# For the Rössler-1976 system
# x(t)
plot(tin, data[,1], ylim = c(-6.5,8.5), type='l', col = 'red',
     xlab = 't', ylab = '', main = 'Original time series')
# y(t)
lines(tin, data[,2], type = 'l', col = 'brown')
# z(t)
lines(tin, data[,3], type = 'l', col = 'orange')
# For the Sprott-K system
# u(t)
lines(tin, data[,4], type = 'l', col = 'green')
# v(t)
lines(tin, data[,5], type = 'l', col = 'darkgreen')
# w(t)
lines(tin, data[,6], type = 'l', col = 'lightgreen')
legend(3, 8,title = 'Rössler', c("x(t)", "y(t)", "z(t)"),
       col=c('red', 'brown', 'orange'), lty=1, cex=0.8)
legend(17, 8,title='Sprott-K', c("u(t)", "v(t)", "w(t)"),
       col=c('green', 'darkgreen', 'lightgreen'), lty=1, cex=0.8)

```

Since one equation is expected from each variable,
vector `nS` is defined as `nS = c(1,1,1,1,1,1)`.
To speed up the computation, the fourth order Runge-Kutta
numerical integration `method = 'rk4'` is
preferred^[package `deSolve` is used for this purpose.].

```{r, eval = FALSE}
# generalized Polynomial modelling
out4 <- gPoMo(data, dtFixe = 1/20, dMax = 2, nS = c(1,1,1,1,1,1),
              show = 0, method = 'rk4',
              IstepMin = 2, IstepMax = 3,
              nPmin = 13, nPmax = 13)
```

Due to the huge number of models to be tested,
numerical integration is sketched here on
a quite short duration and the model search
is directly focused on models of 13-parameter size.
A larger range of model sizes and a longer
numerical integration can be applied to
check the ability of the technique
to retrieve the original models.
This can easily be done, except that a
much longer running time will be required.

However, to distinguish between integrable
and non integrable models (in a numerical
sense), it is necessary to consider
the numerical integration of large lengths.
With longer integration durations
(e.g. from `IstepMin = 10` to `IstepMax = 10000`),
many of the models will be rejected or lead
to trivial solutions (fixed points, periodic
cycles)

In the present case, only one obtained set of equations
(model #347) able to reproduce the original
phase portraits of the two original systems
(Rössler-1976 and Sprott-K) is found:

```{r, eval = FALSE, fig.show='hold', fig.width=4, fig.height=4}
KL <- out4$models$model347
v0 <- as.numeric(head(data,1))
outNumi <- numicano(nVar = 6, dMax = 2, Istep=5000, onestep=1/20, KL=KL, v0=v0)
# obtained model #347
plot(outNumi$reconstr[,2], outNumi$reconstr[,3],
     type='l', col = 'red',
     xlab = 'x(t)', ylab = 'y(t)', main = 'Phase portrait')
# original phase portrait
lines(out4$filtdata[,1], out4$filtdata[,2])
legend(4,2,c("original", "model"), col=c('black', 'red'), lty=1, cex=0.5)

# original phase portrait
plot(outNumi$reconstr[,5], outNumi$reconstr[,6],
     type='l', col = 'green',
     xlab = 'u(t)', ylab = 'v(t)', main = 'Phase portrait')
# original phase portrait
lines(out4$filtdata[,4], out4$filtdata[,5])
legend(-3,1,c("original", "model"), col=c('black', 'green'), lty=1, cex=0.5)

```
```{r, eval = TRUE, echo = FALSE, fig.show='hold', fig.width=4, fig.height=4}
KL <- data_vignetteIII$out4_m347
v0 <- as.numeric(head(data,1))
outNumi <- numicano(nVar = 6, dMax = 2, Istep=5000, onestep=1/20, KL=KL, v0=v0)
# obtained model #347
plot(outNumi$reconstr[,2], outNumi$reconstr[,3],
     type='l', col = 'red',
     xlab = 'x(t)', ylab = 'y(t)', main = 'Phase portrait')
# original phase portrait
lines(dbl(data_vignetteIII$out4_filtdata[,1]), dbl(data_vignetteIII$out4_filtdata[,2]))
legend(4,2,c("original", "model"), col=c('black', 'red'), lty=1, cex=0.5)

# original phase portrait
plot(outNumi$reconstr[,5], outNumi$reconstr[,6],
     type='l', col = 'green',
     xlab = 'u(t)', ylab = 'v(t)', main = 'Phase portrait')
# original phase portrait
lines(dbl(data_vignetteIII$out4_filtdata[,4]), dbl(data_vignetteIII$out4_filtdata[,5]))
legend(-3,1,c("original", "model"), col=c('black', 'green'), lty=1, cex=0.5)

```
The estimated model reads

```{r, eval = FALSE}
visuEq(6, 2, out4$models$model347, approx = 2, 
       substit = c("x", "y", "z", "u", "v", "w"))
```
```{r, eval = TRUE, echo = FALSE}
visuEq(6, 2, data_vignetteIII$out4_m347, approx = 2, 
       substit = c("x", "y", "z", "u", "v", "w"))
```

The global modelling technique enables to distinguish
two independant sets of equations: one for the Rössler-1976
system (equations one to three), the other one for
the Sprott-K system  (equations four to six).
Moreover, the obtained equations are identical in their
structure and very close in their parameterization
to the original equations from which the observed
time series were derived.
This latter point is interesting since it shows
that -- in the present case at least -- not only
the couplings, but also each of the nonlinear terms
of the equations can be retrieved.
The approach may thus be used as a retro-modelling
technique in order to interpret the model terms.
This problem will be investigated in more details
in vignette  `7 Retromodelling`.

## Time series with gaps

When measurements are gathered under real environmental
conditions, the time series may have gaps,
or may be contaminated by temporary perturbations
which should be removed.
The `GPoM` package can be applied to such types of data
sets^[S. Mangiarotti, F. Le Jean, M. Huc, C. Letellier,
2016. Global modelling of aggregated and associated chaotic
dynamics. *Chaos, Solitons & Fractals*, **83**, 82-96].

An example of this kind of situation is given in the present
section. The Rössler system is used again in this example:

```{r, eval = TRUE, fig.align='center'}
# load data
data("Ross76")
# time vector
tin <- Ross76[1:1500,1]
# single time series
series0 <- series <- Ross76[1:1500,3]
# plot
plot(tin, series0, type = 'l', col = 'gray', xlab = 'time', ylab = 'Observational data')
```

To illustrate our purpose, some noise is added to the original
time series and a weighting function is defined to indicate
on what part of the signal the analysis should focus.
This function is equal to 1 when the time series is free of noise,
and equal to 0 when it is noise-contaminated.


```{r, eval = TRUE, fig.align='center'}
# some noise is added
series[1:100] <- series[1:100] + 0.1 * runif(1:100, min = -1, max = 1)
series[301:320] <- series[301:320] + 0.5 * runif(1:20, min = -1, max = 1)
plot(tin, series, ylab='', type = 'l', col = 'black')
#
# weighting function
W <- tin * 0 + 1
W[1:100] <- 0  # the first fourty values will be discarded
W[301:320] <- 0  # twenty other values will be discarded too
lines(tin, W, type = 'l', col = 'brown')
legend(0, -2,c("observed data", "weighting function"),
       col=c('black', 'brown'), lty=1, cex=0.8)
```

The global modelling is then applied:

```{r, echo = TRUE, eval = FALSE}
# Weighted time series
GPout1 <- gPoMo(data = series, tin = tin, weight = W,
                dMax = 2, nS = 3, winL = 9, show = 1,
                IstepMin = 10, IstepMax = 6000,
                nPmin = 5, nPmax = 11, method = 'rk4')
```               
```{r, echo = FALSE, eval = TRUE}
# Weighted time series
GPout1 <- gPoMo(data = series, tin = tin, weight = W,
                dMax = 2, nS = 3, winL = 9, show = 0,
                IstepMin = 10, IstepMax = 6000,
                nPmin = 5, nPmax = 11, method = 'rk4')
```               

The following equations are retrieved (the option `weight`
indicates when the observations provides in `series` should
be considered in the analysis, or not):

```{r, eval = TRUE}
visuEq(3, 2, GPout1$models$model7, approx = 2)
```

This set of equations can then be integrated numerically.
Integration will be launched from the first
state for which weight is equal to 1.

```{r, eval = TRUE}
# first weight which value not equal to zero:
i1 = which(GPout1$Wfiltdata== 1)[1]
v0 <-  GPout1$filtdata[i1,1:3]
rcstr <- numicano(nVar=3, dMax=2, Istep=5000, onestep=1/250,
                  KL = GPout1$models$model7,
                  v0=v0, method="rk4")
```

The best obtained model is #7 that efectively reproduces
the dynamic of the original system:

```{r, eval = TRUE, fig.align='center', fig.width=4, fig.height=4}
plot(rcstr$reconstr[,2], rcstr$reconstr[,3], type='l', lwd = 3,
     main='phase portrait', xlab='y', ylab = 'dy/dt', col='orange')
# original data:
lines(GPout1$filtdata[,1], GPout1$filtdata[,2], type='l',
      main='phase portrait', col='black')
# initial condition
lines(v0[1], v0[2], type = 'p', col = 'red')
legend(-2,1,c("original", "model"), col=c('black', 'orange'), lty=1, cex=0.5)
legend(-2.1,-0.7,"initial conditions : ", cex = 0.5,  bty="n")
```

Note that model #3 also produces a good approximation
of the original system.

```{r, eval = TRUE}
visuEq(3, 2, GPout1$models$model3, approx = 2)
```

This model converges to a periodic attractor but
with a slight modification of its parameterization,
a chaotic solutions can be obtained.

```{r, eval = TRUE}
# first weight which value not equal to zero:
i1 = which(GPout1$Wfiltdata== 1)[1]
v0 <-  GPout1$filtdata[i1,1:3]
KL3bis <- KL3 <- GPout1$models$model7
KL3bis[2,3] <- KL3[2,3] * 1.1
rcstr <- numicano(nVar = 3, dMax = 2,
                     Istep = 40000, onestep = 1/250, KL = KL3bis,
                     v0 = v0, method = "rk4")
plot(rcstr$reconstr[35000:40000,2], rcstr$reconstr[35000:40000,3],
     main='phase portrait', xlab='y', ylab = 'dy/dt',
     type='l', lwd = 3, col='orange')
```

## Time series in associassion

Equivalent records of the same dynamical behavior
may have been performed by several sensors,
concurently or not and at same or different locations.
It may then be useful, and simetimes necessary, to consider
these time series simultaneously in the modelling process.
The `GPoM` package can also be applied to such type
of data sets^[Ibid].

A set of four time series of different time legnth,
all derived from the Rössler system, are made available
to show how to apply the global modelling technique
to such a set of time series.

```{r, eval = TRUE, fig.align='center'}
# load data
data("severalTS")
# plot
plot(svrlTS$data1$TS[,1] - svrlTS$data1$TS[1,1], svrlTS$data1$TS[,2],
     type = 'l', col = 'gray',
     xlab = 'time', ylab = 'y(t)', main = 'Observational data',
     xlim = c(0, 20), ylim = c(-6, 2.2))
lines(svrlTS$data2$TS[,1] - svrlTS$data2$TS[1,1], svrlTS$data2$TS[,2],
      type = 'l', col = 'blue')
lines(svrlTS$data3$TS[,1] - svrlTS$data3$TS[1,1], svrlTS$data3$TS[,2],
      type = 'l', col = 'orange')
lines(svrlTS$data4$TS[,1] - svrlTS$data4$TS[1,1], svrlTS$data4$TS[,2],
      type = 'l', col = 'brown')
```

The function `concat` concatenates the set of separated time series
into a single time series, and provides a corresponding weight vector `W`
that takes the bundary conditions between successive time series
into account, in order to avoid any discontinuous effect.

```{r, eval = TRUE, fig.align='center'}
# Concatenate the data set into a single time series
winL = 55
concaTS <- concat(svrlTS, winL = winL)
```

A large value is chosen for parameter `winL` just to highlight
its effect on the next plots.
The gray line enables to clearly distinguish the discontinuities
at the connexion of originally separated time series.
Data points rejected at the boudary are plotted as red dots
and kept ones as green dots.
The corresponding weighted vector is plotted as a black line.

```{r, eval = TRUE, fig.align='center'}
# Plot the concatenated time series
plot(concaTS$sglTS$TS[,1], concaTS$sglTS$TS[,2],
     main = 'Concatenated time series',
     xlab = 'Time (concatenated)', ylab = 'y(t)',
     type = 'l', col = 'gray')
lines(concaTS$sglTS$TS[concaTS$sglTS$W == 1,1],
      concaTS$sglTS$TS[concaTS$sglTS$W == 1,2], type = 'p', col = 'green', cex = 0.5)
lines(concaTS$sglTS$TS[concaTS$sglTS$W == 0,1],
      concaTS$sglTS$TS[concaTS$sglTS$W == 0,2], type = 'p', col = 'red', cex = 0.5)
lines(concaTS$sglTS$TS[,1], concaTS$sglTS$W, type = 'l')
```

The output of function `concat` can directly be used for global modelling:

```{r, echo = TRUE, eval = FALSE}
GPout2 <- gPoMo(data = concaTS$sglTS$TS[,2], tin = concaTS$sglTS$TS[,1],
                dMax = 2, nS = 3, winL = winL, weight = concaTS$sglTS$W, show = 1,
                IstepMin = 10, IstepMax = 12000, nPmin = 6, nPmax = 12, method = 'rk4')

```
```{r, echo = TRUE, eval = FALSE}
GPout2 <- gPoMo(data = concaTS$sglTS$TS[,2], tin = concaTS$sglTS$TS[,1],
                dMax = 2, nS = 3, winL = winL, weight = concaTS$sglTS$W, show = 0,
                IstepMin = 10, IstepMax = 12000, nPmin = 6, nPmax = 12, method = 'rk4')

```

The dynamic of the original system is effectively reproduced
by sereral models.

## Output visualization and global models validation

The outputs of the `gPoMo` function are gathered in one
single list.
The next vignette `4 VisualizeOutputs` aims to show
how the results are organized in this list.
Examples of model validation are then presented in the
vignette `5 Predictability`.
