Title: | Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals and Missing Data |
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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. |
Authors: | Lionel Voirol [aut, cre] |
Maintainer: | Lionel Voirol <[email protected]> |
License: | AGPL-3 |
Version: | 0.0.1 |
Built: | 2025-03-14 03:33:07 UTC |
Source: | https://github.com/smac-group/gmwmx2 |
Estimated northward and eastward velocity and standard deviation for a subset of 1202 GNSS station with more than 10 years of daily data.
df_estimated_velocities_gmwmx
df_estimated_velocities_gmwmx
A data frame with 1202 rows and 12 variables:
Name of the GNSS station.
Estimated northward velocity trend (in meters per day).
Standard deviation of the estimated northward velocity trend.
Estimated eastward velocity trend (in meters per day).
Standard deviation of the estimated eastward velocity trend.
Length of the signal (in days).
Scaled estimated northward velocity trend (multiplying by 365.25 for yearly values).
Scaled standard deviation of the estimated northward velocity trend.
Scaled estimated eastward velocity trend (multiplying by 365.25 for yearly values).
Scaled standard deviation of the estimated eastward velocity trend.
Latitude of the GNSS station.
Longitude of the GNSS station.
Download all stations name and location from the Nevada Geodetic Laboratory
download_all_stations_ngl(verbose = FALSE)
download_all_stations_ngl(verbose = FALSE)
verbose |
A |
Return a data.frame
with all stations name, latitude, longitude and heights.
df_all_stations <- download_all_stations_ngl() head(df_all_stations)
df_all_stations <- download_all_stations_ngl() head(df_all_stations)
Download estimated velocities provided by the Nevada Geodetic Laboratory for all stations.
download_estimated_velocities_ngl(verbose = FALSE)
download_estimated_velocities_ngl(verbose = FALSE)
verbose |
A |
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.
df_estimated_velocities <- download_estimated_velocities_ngl() head(df_estimated_velocities)
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.
download_station_ngl(station_name, verbose = FALSE)
download_station_ngl(station_name, verbose = FALSE)
station_name |
A |
verbose |
A |
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.
station_1LSU <- download_station_ngl("1LSU") attributes(station_1LSU)
station_1LSU <- download_station_ngl("1LSU") attributes(station_1LSU)
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.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.
gmwmx2( x, n_seasonal = 2, vec_earthquakes_relaxation_time = NULL, component = "N", toeplitz_approx_var_cov_wv = TRUE, stochastic_model = "wn + fl" )
gmwmx2( x, n_seasonal = 2, vec_earthquakes_relaxation_time = NULL, component = "N", toeplitz_approx_var_cov_wv = TRUE, stochastic_model = "wn + fl" )
x |
A |
n_seasonal |
An |
vec_earthquakes_relaxation_time |
A |
component |
A |
toeplitz_approx_var_cov_wv |
A |
stochastic_model |
A |
x <- download_station_ngl("CHML") fit <- gmwmx2(x, n_seasonal = 2, component = "N")
x <- download_station_ngl("CHML") fit <- gmwmx2(x, n_seasonal = 2, component = "N")
fit_gnss_ts_ngl
objectPlot a fit_gnss_ts_ngl
object
## S3 method for class 'fit_gnss_ts_ngl' plot(x, ...)
## S3 method for class 'fit_gnss_ts_ngl' plot(x, ...)
x |
A |
... |
Additional graphical parameters. |
No return value. Plot a fit_gnss_ts_ngl
object.
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)
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)
gnss_ts_ngl
objectPlot a gnss_ts_ngl
object
## S3 method for class 'gnss_ts_ngl' plot(x, component = NULL, ...)
## S3 method for class 'gnss_ts_ngl' plot(x, component = NULL, ...)
x |
A |
component |
A |
... |
Additional graphical parameters. |
No return value. Plot a gnss_ts_ngl
object.
station_1LSU <- download_station_ngl("1LSU") plot(station_1LSU) plot(station_1LSU, component = "N") plot(station_1LSU, component = "E") plot(station_1LSU, component = "V")
station_1LSU <- download_station_ngl("1LSU") plot(station_1LSU) plot(station_1LSU, component = "N") plot(station_1LSU, component = "E") plot(station_1LSU, component = "V")
fit_gnss_ts_ngl
Extract estimated parameters from a fit_gnss_ts_ngl
## S3 method for class 'fit_gnss_ts_ngl' summary(object, scale_parameters = FALSE, ...)
## S3 method for class 'fit_gnss_ts_ngl' summary(object, scale_parameters = FALSE, ...)
object |
A |
scale_parameters |
A |
... |
Additional parameters. |
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)
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)