`.mom`

file as a `gnssts`

objectLet us first load the `gmwmx`

package.

`library(gmwmx)`

Consider that you want to estimate a model on data saved in a
`.mom`

file located at a specific `file_path`

on
your computer, where `file_path`

is the path where is located
the `.mom`

file (for example
`file_path = "/home/name_of_the_user/Documents/data.mom"`

)

For example, the corresponding `.mom`

file could have a
similar looking:

```
# sampling period 1.000000
# offset 55285.000000
# offset 58287.770833
52759.5 -0.01165
52760.5 -0.01102
52761.5 -0.01147
...
```

You can import the `.mom`

file as a with the function
`read.gnssts()`

as such:

`= read.gnssts(filename = file_path) data_dobs `

Objects created or imported with `create.gnss()`

or
`read.gnssts()`

are of class `gnssts`

.

`class(data_dobs)`

`## [1] "gnssts"`

By inspecting the structure of a `gnssts`

object, we
observe that `gnssts`

objects specify the time vector, the
observation vector, the sampling period and the times at which there are
location shifts (jumps).

`str(data_dobs)`

```
## List of 4
## $ t : num [1:5559] 52760 52760 52762 52764 52766 ...
## $ y : num [1:5559] -0.0117 -0.011 -0.0115 -0.0131 -0.0106 ...
## $ sampling_period: num 1
## $ jumps : num [1:2] 55285 58288
## - attr(*, "class")= chr "gnssts"
```

We can represent the signal as such:

`plot(data_dobs$t, data_dobs$y, type="l")`

The `gmwmx`

package allows to estimate linear model with
correlated residuals that are described by a functional model and a
stochastic noise model.

More precisely, for the functional model, we consider a linear model which can be expressed as:

\[\begin{equation} \mathbf{Y} = \mathbf{A} {{\bf x}}_0 + \boldsymbol{\varepsilon}, \end{equation}\]

where \(\mathbf{Y} \in {\rm I\!R}^n\) denotes the response variable of interest (i.e., vector of GNSS observations), \(\mathbf{A} \in {\rm I\!R}^{n \times p}\) a fixed design matrix, \({{\bf x}}_0 \in \mathcal{X} \subset {\rm I\!R}^p\) a vector of unknown constants and \(\boldsymbol{\varepsilon} \in {\rm I\!R}^n\) a vector of (zero mean) residuals.

The `gmwmx`

package allows to estimate functional models
for which the \(i\)-th component of the
vector \(\mathbf{A} {{\bf x}}_0\) can
be described as follows:

\[\begin{equation} \mathbb{E}[\mathbf{Y}_i] = \mathbf{A}_i^T {{\bf x}}_0 = a+b\left(t_{i}-t_{0}\right)+\sum_{h=1}^{2}\left[c_{h} \sin \left(2 \pi f_{h} t_{i}\right)+d_{h} \cos \left(2 \pi f_{h} t_{i}\right)\right] + \sum_{k=1}^{n_{g}} g_{k} H\left(t_{i}-t_{k}\right), \end{equation}\]

where \(a\) is the initial position at the reference epoch \(t_0\), \(b\) is the velocity parameter, \(c_k\) and \(d_k\) are the periodic motion parameters (\(h = 1\) and \(h = 2\) represent the annual and semi-annual seasonal terms, respectively). The offset terms models earthquakes, equipment changes or human intervention in which \(g_k\) is the magnitude of the change at epochs \(t_k\), \(n_g\) is the total number of offsets, and \(H\) is the Heaviside step function. Note that the estimates of the parameters of the functional model are provided in unit/day.

Regarding the stochastic model, we assume that \(\boldsymbol{\varepsilon}_i=\mathbf{Y}_i-\mathbb{E}[\mathbf{Y}_i]\) is a strictly (intrinsically) stationary process and that

\[\begin{equation} \boldsymbol{\varepsilon} \sim \mathcal{F} \left\{\mathbf{0}, \boldsymbol{\Sigma}(\boldsymbol{\gamma}_0)\right\} , \label{eq:model:noise} \end{equation}\]

where \(\mathcal{F}\) denotes some probability distribution in \({\rm I\!R}^n\) with mean \({\bf 0}\) and covariance \(\boldsymbol{\Sigma}(\boldsymbol{\gamma}_0)\).

We assume that \(\boldsymbol{\Sigma}(\boldsymbol{\gamma}_0) > 0\) and that it depends on the unknown parameter vector \(\boldsymbol{\gamma}_0 \in \boldsymbol{\Gamma} \subset {\rm I\!R}^q\). This parameter vector specifies the covariance of the observations and is often referred to as the stochastic parameters.

Hence, we let \(\boldsymbol{\theta}_0 = \left[\boldsymbol{{\bf x}}_0^{\rm T} \;\; \boldsymbol{\gamma}_0^{\rm T}\right]^{\rm T} \in \boldsymbol{\Theta} = \mathcal{X} \times \boldsymbol{\Gamma} \subset {\rm I\!R}^{p + k}\) denote the unknown parameter vector of the model described above.

The `gmwmx`

allows to estimate parameters of a specified
functional model as well as parameters of a stochastic model (i.e. \(\hat{\boldsymbol{\theta}} =
\left[\boldsymbol{\hat{\boldsymbol{x}}}^{T} \;\;
\hat{\boldsymbol{\gamma}}^{T}\right]\)) defined by a combinations
of

- White noise (
`wn`

) - Matérn process (
`matern`

) - Fractional Gaussian noise (
`fgn`

) and - Power Law process (
`powerlaw`

).

Note that only the `gmwmx`

current version accepts only
one process of each kind.

You can estimate a model using the GMWMX estimator with the function
`estimate_gmwmx()`

.

The stochastic model considered is specified by a string provided to
the argument `model_string`

which is a combination of the
strings `wn`

, `powerlaw`

, `matern`

and
`fgn`

separated by the character `+`

.

You specify the initialization values for solving the optimization
problem at the GMWM estimation step that estimate the stochastic model
by providing a numeric vector of the correct length (the total number of
parameters of the stochastic model specified in
`model_string`

) to the argument `theta_0`

.

You can compute confidence intervals for estimated functional
parameters of an estimated model by setting the argument `ci`

to `TRUE`

.

Let us consider a single sinusoidal signal with the jumps specified
in the `gnssts`

object and a combination of a White noise and
a Power Law process for the stochastic model.

```
= estimate_gmwmx(x = data_dobs, theta_0 = c(0.1, 0.1, 0.1),
fit_dobs_wn_plp_gmwmx model_string = "wn+powerlaw",
n_seasonal = 1, ci = T)
```

Estimated models are of class `gnsstsmodel`

`class(fit_dobs_wn_plp_gmwmx)`

`## [1] "gnsstsmodel"`

We can print the estimated model or extract estimated parameters (functional and stochastic) as such:

`print(fit_dobs_wn_plp_gmwmx)`

```
## GNSS time series model
##
## * Model: wn + powerlaw
##
## * Functional parameters:
## bias : +0.012013 +/- 0.0200366449
## trend : +0.000008 +/- 0.0000001991
## A*cos(U) : -0.000655 +/- 0.0000754528
## A*sin(U) : +0.000016 +/- 0.0000762463
## jump : -0.003758 +/- 0.0005738092
## jump : +0.001568 +/- 0.0006045750
##
## * Stochastic parameters:
## wn_sigma2 : +0.00000079
## powerlaw_sigma2 : +0.00000025
## powerlaw_d : +0.49990000
##
## * Estimation time: 1.66 s
```

`$beta_hat fit_dobs_wn_plp_gmwmx`

```
## bias trend A*cos(U) A*sin(U) jump
## 1.201297e-02 8.351512e-06 -6.553985e-04 1.578605e-05 -3.758074e-03
## jump
## 1.567881e-03
```

`$theta_hat fit_dobs_wn_plp_gmwmx`

```
## wn_sigma2 powerlaw_sigma2 powerlaw_d
## 7.916985e-07 2.525867e-07 4.999000e-01
```

We can also plot graphically the estimated functional model on the
time series and the Wavelet variance of residuals by calling the
`plot.gnsstsmodel`

method on a `gnsstsmodel`

object.

`plot(fit_dobs_wn_plp_gmwmx)`

We can specify the number of iterations of the GMWMX to compute
respectively the GMWMX-1 and GMWMX-2 or other iteration of the GMWMX
with the argument `k_iter`

. For example we can compute the
GMWMX-2 as such:

```
= estimate_gmwmx(x = data_dobs,
fit_dobs_wn_plp_gmwmx_2 theta_0 = c(0.1, 0.1, 0.1),
model_string = "wn+powerlaw",
n_seasonal = 1,
k_iter = 2)
```

Assuming that you have Hector available on the `PATH`

, an
estimation of the model can the be performed using the Maximum
Likelihood Estimation (MLE) method implemented in Hector as such:

```
= estimate_hector(x = data_dobs,
fit_dobs_wn_plp_mle model_string = "wn+powerlaw",
n_seasonal = 1)
```

Similarly we can plot and extract the model parameters of the estimated model:

`plot(fit_dobs_wn_plp_mle)`

`$beta_hat fit_dobs_wn_plp_mle`

```
## bias trend A*cos(U) A*sin(U) jump
## 1.200000e-02 8.451006e-06 -6.672690e-04 1.359080e-06 -4.194170e-03
## jump
## 9.071300e-04
```

`$theta_hat fit_dobs_wn_plp_mle`

```
## wn_sigma2 powerlaw_sigma2 powerlaw_d
## 7.779727e-07 2.700504e-07 4.854970e-01
```

We can load time series data from the Plate
Boundary Observatory (PBO) as `gnssts`

object with
`PBO_get_station()`

:

`= PBO_get_station("COLA", column = "dE") cola `

Let us consider three potential models for the stochastic model of this signal. More precisely let us consider:

- a White noise and a PowerLaw process
- a White noise coupled with a Matérn process
- a White noise and a Fractional Gaussian noise

```
= estimate_gmwmx(cola, model_string = "wn+powerlaw",
fit_cola_wn_plp theta_0 = c(0.1,0.1,0.1),
n_seasonal = 1,
ci = T)
```

`plot(fit_cola_wn_plp)`

```
= estimate_gmwmx(cola, model_string = "wn+fgn", theta_0 = c(0.1,0.1,0.2),
fit_cola_wn_fgn n_seasonal = 1,
ci = T)
```

`plot(fit_cola_wn_fgn)`

```
= estimate_gmwmx(cola, model_string = "wn+matern",
fit_cola_wn_matern theta_0 = c(0.1,0.1,0.1,0.1),
n_seasonal = 1,
ci = T)
```

`plot(fit_cola_wn_matern)`

You can compare estimated models with the function
`compare_fits()`

`compare_fits(fit_cola_wn_plp, fit_cola_wn_matern)`

```
## Warning in compare_fits(fit_cola_wn_plp, fit_cola_wn_matern): Provided fits do
## not esimate the same model.
```