Title: Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals and Missing Data
Version: 0.0.2
Description: Implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Voirol, L., Xu, H., Zhang, Y., Insolia, L., Molinari, R. and Guerrier, S. (2024) <doi:10.48550/arXiv.2409.05160>. The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals in presence of missing data. The 'gmwmx2' package provides functions to load and plot Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory and functions to estimate linear model model with correlated residuals in presence of missing data.
License: AGPL-3
Encoding: UTF-8
RoxygenNote: 7.3.1
Language: en-US
Depends: R (≥ 4.0.0)
VignetteBuilder: knitr
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, data.table, dplyr, magrittr, wv, Matrix, httr2, rlang
Suggests: knitr, rmarkdown, knitcitations, raster, rnaturalearth, shape, tibble, tidygeocoder, sf, geodata
URL: https://github.com/SMAC-Group/gmwmx2
BugReports: https://github.com/SMAC-Group/gmwmx2/issues
LazyData: true
NeedsCompilation: yes
Packaged: 2025-04-10 21:56:28 UTC; lionel
Author: Lionel Voirol ORCID iD [aut, cre], Haotian Xu ORCID iD [aut], Yuming Zhang ORCID iD [aut], Luca Insolia ORCID iD [aut], Roberto Molinari ORCID iD [aut], Stéphane Guerrier ORCID iD [aut]
Maintainer: Lionel Voirol <lionelvoirol@hotmail.com>
Repository: CRAN
Date/Publication: 2025-04-10 22:20:02 UTC

Estimated northward and eastward velocity and their standard deviation using the GMWMX estimator

Description

Estimated northward and eastward velocity and standard deviation for a subset of 1202 GNSS station with more than 10 years of daily data.

Usage

df_estimated_velocities_gmwmx

Format

A data frame with 1202 rows and 12 variables:

station_name

Name of the GNSS station.

estimated_trend_N

Estimated northward velocity trend (in meters per day).

std_estimated_trend_N

Standard deviation of the estimated northward velocity trend.

estimated_trend_E

Estimated eastward velocity trend (in meters per day).

std_estimated_trend_E

Standard deviation of the estimated eastward velocity trend.

length_signal

Length of the signal (in days).

estimated_trend_N_scaled

Scaled estimated northward velocity trend (multiplying by 365.25 for yearly values).

std_estimated_trend_N_scaled

Scaled standard deviation of the estimated northward velocity trend.

estimated_trend_E_scaled

Scaled estimated eastward velocity trend (multiplying by 365.25 for yearly values).

std_estimated_trend_E_scaled

Scaled standard deviation of the estimated eastward velocity trend.

latitude

Latitude of the GNSS station.

longitude

Longitude of the GNSS station.

Download all stations name and location from the Nevada Geodetic Laboratory

Description

Download all stations name and location from the Nevada Geodetic Laboratory

Usage

download_all_stations_ngl(verbose = FALSE)

Arguments

verbose

A boolean that controls the level of detail in the output of the wget command used to load data. Default is FALSE.

Value

Return a data.frame with all stations name, latitude, longitude and heights.

Examples

df_all_stations <- download_all_stations_ngl()
head(df_all_stations)

Download estimated velocities provided by the Nevada Geodetic Laboratory for all stations.

Description

Download estimated velocities provided by the Nevada Geodetic Laboratory for all stations.

Usage

download_estimated_velocities_ngl(verbose = FALSE)

Arguments

verbose

A boolean that controls the level of detail in the output of the wget command used to load data. Default is FALSE.

Value

Return a data.frame with all stations name, information about the time series for each station, estimated velocities and estimated standard deviation of the estimated velocities.

Examples

df_estimated_velocities <- download_estimated_velocities_ngl()
head(df_estimated_velocities)

Download GNSS position time series and steps reference from the Nevada Geodetic Laboratory with IGS14 reference frame.

Description

Download GNSS position time series and steps reference from the Nevada Geodetic Laboratory with IGS14 reference frame.

Usage

download_station_ngl(station_name, verbose = FALSE)

Arguments

station_name

A string specifying the station name.

verbose

A boolean that controls the level of detail in the output of the wget command used to load data. Default is FALSE.

Value

A list of class gnss_ts_ngl that contains three data.frame: The data.frame df_position which contains the position time series extracted from the .tenv3 file available from the Nevada Geodetic Laboratory, the data.frame df_equipment_software_changes which specify the equipment or software changes for that stations and the data.frame df_earthquakes that specify the earthquakes associated with that station.

Examples

station_1LSU <- download_station_ngl("1LSU")
attributes(station_1LSU)

Estimate a trajectory model for a gnss_ts_ngl object considering a white noise plus colored noise as the stochastic model for the residuals and model missingness with a Markov process using the GMWMX estimator.

Description

Estimate a trajectory model for a gnss_ts_ngl object considering a white noise plus colored noise as the stochastic model for the residuals and model missingness with a Markov process using the GMWMX estimator.

Usage

gmwmx2(
  x,
  n_seasonal = 2,
  vec_earthquakes_relaxation_time = NULL,
  component = "N",
  toeplitz_approx_var_cov_wv = TRUE,
  stochastic_model = "wn + fl"
)

Arguments

x

A gnss_ts_ngl object.

n_seasonal

An integer specifying the number of seasonal signals in the time series. "1" specify only one annual periodic signal and "2"specify an annual and a semiannual periodic signal.

vec_earthquakes_relaxation_time

A vector specifying the relaxation time for each earthquakes indicated for the time series.

component

A string with value either "N", "E" or "V" that specify which component to estimate (Northing, Easting or Vertical).

toeplitz_approx_var_cov_wv

A boolean that specify if the variance of the wavelet variance should be computed based on a toeplitz approximation of the variance covariance matrix of the residuals.

stochastic_model

A string that specify the stochastic model considered for the residuals. Either "wn + fl" for white noise and flicker/pink noise or "wn + pl" for white noise and stationary power-law noise.

Examples

x <- download_station_ngl("CHML")
fit <- gmwmx2(x, n_seasonal = 2, component = "N")

Plot a fit_gnss_ts_ngl object

Description

Plot a fit_gnss_ts_ngl object

Usage

## S3 method for class 'fit_gnss_ts_ngl'
plot(x, ...)

Arguments

x

A fit_gnss_ts_ngl object.

...

Additional graphical parameters.

Value

No return value. Plot a fit_gnss_ts_ngl object.

Examples

x <- download_station_ngl("0AMB")
fit_N <- gmwmx2(x, n_seasonal = 2, component = "N")
plot(fit_N)
fit_E <- gmwmx2(x, n_seasonal = 2, component = "E")
plot(fit_E)

Plot a gnss_ts_ngl object

Description

Plot a gnss_ts_ngl object

Usage

## S3 method for class 'gnss_ts_ngl'
plot(x, component = NULL, ...)

Arguments

x

A gnss_ts_ngl object.

component

A string with value either "N", "E" or "V" that specify which component to plot (Northing, Easting or Vertical).

...

Additional graphical parameters.

Value

No return value. Plot a gnss_ts_ngl object.

Examples

station_1LSU <- download_station_ngl("1LSU")
plot(station_1LSU)
plot(station_1LSU, component = "N")
plot(station_1LSU, component = "E")
plot(station_1LSU, component = "V")

Extract estimated parameters from a fit_gnss_ts_ngl

Description

Extract estimated parameters from a fit_gnss_ts_ngl

Usage

## S3 method for class 'fit_gnss_ts_ngl'
summary(object, scale_parameters = FALSE, ...)

Arguments

object

A fit_gnss_ts_ngl object.

scale_parameters

A boolean indicating whether or not to scale estimated parameters so that the returned estimated trend is provided in m/year instead of m/day. Default is FALSE.

...

Additional parameters.

Examples

x <- download_station_ngl("P820")
fit1 <- gmwmx2(x, n_seasonal = 2, component = "N", stochastic_model = "wn + pl")
summary(fit1)
summary(fit1, scale_parameters = TRUE)
fit2 <- gmwmx2(x, n_seasonal = 2, component = "N", stochastic_model = "wn + fl")
summary(fit2)