gmwmx2: Estimate a trajectory model for a 'gnss_ts_ngl' object...

In gmwmx2: Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals and Missing Data

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")

gmwmx2 documentation built on Aug. 21, 2025, 5:56 p.m.