Extract estimated parameters from a fit_gnss_ts_ngl
# S3 method for class 'fit_gnss_ts_ngl'
summary(object, scale_parameters = FALSE, ...)x <- download_station_ngl("P820")
fit1 <- gmwmx2(x, n_seasonal = 2, component = "N", stochastic_model = "wn + pl")
summary(fit1)
#> Summary of Estimated Model
#> -------------------------------------------------------------
#> Functional parameters
#> -------------------------------------------------------------
#> Parameter Estimate Std_Deviation 95% CI Lower 95% CI Upper
#> -------------------------------------------------------------
#> Intercept 0.75010524 0.00560599 0.73911770 0.76109277
#> Trend -0.00000693 0.00000062 -0.00000814 -0.00000572
#> Sin (Annual) 0.00060825 0.00020361 0.00020919 0.00100732
#> Cos (Annual) -0.00047066 0.00021055 -0.00088333 -0.00005800
#> Sin (Semi-Annual) 0.00040375 0.00014749 0.00011468 0.00069283
#> Cos (Semi-Annual) 0.00008541 0.00014720 -0.00020311 0.00037393
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000088
#> Stationary powerlaw Spectral index: -0.95767473
#> Stationary powerlaw Variance: 0.00000103
#> -------------------------------------------------------------
#> Missingness parameters
#> -------------------------------------------------------------
#> P(Z_{i+1} = 0 | Z_{i} = 1): 0.00076834
#> P(Z_{i+1} = 1 | Z_{i} = 0): 1.00000000
#> \hat{E[Z]}: 0.99923225
#> -------------------------------------------------------------
#> Running time: 0.34 seconds
#> -------------------------------------------------------------
summary(fit1, scale_parameters = TRUE)
#> Summary of Estimated Model
#> -------------------------------------------------------------
#> Functional parameters
#> -------------------------------------------------------------
#> Parameter Estimate Std_Deviation 95% CI Lower 95% CI Upper
#> -------------------------------------------------------------
#> Intercept 273.97593721 2.04758744 269.96273957 277.98913484
#> Trend -0.00253039 0.00022526 -0.00297188 -0.00208890
#> Sin (Annual) 0.22216452 0.07436781 0.07640630 0.36792275
#> Cos (Annual) -0.17190992 0.07690285 -0.32263674 -0.02118310
#> Sin (Semi-Annual) 0.14747050 0.05387064 0.04188598 0.25305502
#> Cos (Semi-Annual) 0.03119569 0.05376653 -0.07418477 0.13657615
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000088
#> Stationary powerlaw Spectral index: -0.95767473
#> Stationary powerlaw Variance: 0.00000103
#> -------------------------------------------------------------
#> Missingness parameters
#> -------------------------------------------------------------
#> P(Z_{i+1} = 0 | Z_{i} = 1): 0.00076834
#> P(Z_{i+1} = 1 | Z_{i} = 0): 1.00000000
#> \hat{E[Z]}: 0.99923225
#> -------------------------------------------------------------
#> Running time: 0.34 seconds
#> -------------------------------------------------------------
fit2 <- gmwmx2(x, n_seasonal = 2, component = "N", stochastic_model = "wn + fl")
summary(fit2)
#> Summary of Estimated Model
#> -------------------------------------------------------------
#> Functional parameters
#> -------------------------------------------------------------
#> Parameter Estimate Std_Deviation 95% CI Lower 95% CI Upper
#> -------------------------------------------------------------
#> Intercept 0.75010524 0.00077387 0.74858849 0.75162198
#> Trend -0.00000693 0.00000060 -0.00000810 -0.00000576
#> Sin (Annual) 0.00060825 0.00020543 0.00020561 0.00101089
#> Cos (Annual) -0.00047066 0.00021283 -0.00088780 -0.00005353
#> Sin (Semi-Annual) 0.00040375 0.00014855 0.00011260 0.00069490
#> Cos (Semi-Annual) 0.00008541 0.00014818 -0.00020502 0.00037584
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000095
#> Flicker Noise Variance: 0.00000095
#> -------------------------------------------------------------
#> Missingness parameters
#> -------------------------------------------------------------
#> P(Z_{i+1} = 0 | Z_{i} = 1): 0.00076834
#> P(Z_{i+1} = 1 | Z_{i} = 0): 1.00000000
#> \hat{E[Z]}: 0.99923225
#> -------------------------------------------------------------
#> Running time: 0.68 seconds
#> -------------------------------------------------------------