# Fitting an MAR model: analyzer interfaceΒΆ

In this example, we will use the Analyzer interface to fit a multi-variate auto-regressive model with two time-series influencing each other.

We start by importing 3rd party modules:

```
import numpy as np
import matplotlib.pyplot as plt
```

And then by importing Granger analysis sub-module, which we will use for fitting the MAR model:

```
import nitime.analysis.granger as gc
```

The utils sub-module includes a function to generate auto-regressive processes based on provided coefficients:

```
import nitime.utils as utils
```

Generate some MAR processes (according to Ding and Bressler [Ding2006]),

```
a1 = np.array([[0.9, 0],
[0.16, 0.8]])
a2 = np.array([[-0.5, 0],
[-0.2, -0.5]])
am = np.array([-a1, -a2])
x_var = 1
y_var = 0.7
xy_cov = 0.4
cov = np.array([[x_var, xy_cov],
[xy_cov, y_var]])
```

Number of realizations of the process

```
N = 500
```

Length of each realization:

```
L = 1024
order = am.shape[0]
n_lags = order + 1
n_process = am.shape[-1]
z = np.empty((N, n_process, L))
nz = np.empty((N, n_process, L))
np.random.seed(1981)
for i in range(N):
z[i], nz[i] = utils.generate_mar(am, cov, L)
```

We start by estimating the order of the model from the data:

```
est_order = []
for i in range(N):
this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0], z[i][1])
est_order.append(this_order)
order = int(np.round(np.mean(est_order)))
```

Once we have estimated the order, we go ahead and fit each realization of the MAR model, constraining the model order accordingly (by setting the order key-word argument) to be always equal to the model order estimated above.

```
Rxx = np.empty((N, n_process, n_process, n_lags))
coef = np.empty((N, n_process, n_process, order))
ecov = np.empty((N, n_process, n_process))
for i in range(N):
this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0], z[i][1], order=order)
Rxx[i] = this_Rxx
coef[i] = this_coef
ecov[i] = this_ecov
```

We generate a time-series from the recovered coefficients, using the same randomization seed as the first mar. These should look pretty similar to each other:

```
np.random.seed(1981)
est_ts, _ = utils.generate_mar(np.mean(coef, axis=0), np.mean(ecov, axis=0), L)
fig01 = plt.figure()
ax = fig01.add_subplot(1, 1, 1)
ax.plot(est_ts[0][0:100])
ax.plot(z[0][0][0:100], 'g--')
```

```
plt.show()
```

[Ding2006] | M. Ding, Y. Chen and S.L. Bressler (2006) Granger causality: basic theory and application to neuroscience. In Handbook of Time Series Analysis, ed. B. Schelter, M. Winterhalder, and J. Timmer, Wiley-VCH Verlage, 2006: 451-474 |

Example source code

You can download `the full source code of this example`

.
This same script is also included in the Nitime source distribution under the
`doc/examples/`

directory.