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 0.75007627 0.00405363 0.74213130 0.75802125
#> Trend -0.00000700 0.00000060 -0.00000818 -0.00000582
#> Sin (Annual) 0.00065832 0.00019794 0.00027035 0.00104628
#> Cos (Annual) -0.00050112 0.00020731 -0.00090744 -0.00009480
#> Sin (Semi-Annual) 0.00044663 0.00014636 0.00015977 0.00073349
#> Cos (Semi-Annual) 0.00012070 0.00014257 -0.00015874 0.00040014
#> Jump: MJD 60547 0.00229644 0.00118728 -0.00003059 0.00462347
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000091
#> Stationary powerlaw Spectral index: -0.95555862
#> Stationary powerlaw Variance: 0.00000099
#> -------------------------------------------------------------
#> 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: 0.33 seconds
#> -------------------------------------------------------------
summary(fit1, scale_parameters = TRUE)
#> Summary of Estimated Model
#> -------------------------------------------------------------
#> Functional parameters
#> -------------------------------------------------------------
#> Parameter Estimate Std_Deviation 95% CI Lower 95% CI Upper
#> -------------------------------------------------------------
#> Intercept 273.96535795 1.48058958 271.06345569 276.86726021
#> Trend -0.00255669 0.00022017 -0.00298822 -0.00212517
#> Sin (Annual) 0.24045103 0.07229927 0.09874706 0.38215501
#> Cos (Annual) -0.18303481 0.07572044 -0.33144414 -0.03462547
#> Sin (Semi-Annual) 0.16313051 0.05345811 0.05835454 0.26790647
#> Cos (Semi-Annual) 0.04408635 0.05207533 -0.05797942 0.14615212
#> Jump: MJD 60547 0.83877580 0.43365496 -0.01117230 1.68872389
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000091
#> Stationary powerlaw Spectral index: -0.95555862
#> Stationary powerlaw Variance: 0.00000099
#> -------------------------------------------------------------
#> 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: 0.33 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 0.75007627 0.00075821 0.74859022 0.75156233
#> Trend -0.00000700 0.00000059 -0.00000816 -0.00000584
#> Sin (Annual) 0.00065832 0.00020072 0.00026491 0.00105173
#> Cos (Annual) -0.00050112 0.00021071 -0.00091410 -0.00008814
#> Sin (Semi-Annual) 0.00044663 0.00014791 0.00015672 0.00073653
#> Cos (Semi-Annual) 0.00012070 0.00014389 -0.00016132 0.00040273
#> Jump: MJD 60547 0.00229644 0.00121168 -0.00007841 0.00467129
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000098
#> Flicker Noise Variance: 0.00000091
#> -------------------------------------------------------------
#> 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: 0.65 seconds
#> -------------------------------------------------------------