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 0.74675532 0.00002219 0.74671183 0.74679882
#> Trend -0.00000721 0.00000003 -0.00000726 -0.00000716
#> Sin (Annual) -0.00274753 0.00002846 -0.00280331 -0.00269176
#> Cos (Annual) -0.00277174 0.00002821 -0.00282702 -0.00271646
#> Sin (Semi-Annual) 0.00087112 0.00002811 0.00081602 0.00092621
#> Cos (Semi-Annual) -0.00059133 0.00002807 -0.00064634 -0.00053632
#> Jump: MJD 60649 -0.00005855 0.00013204 -0.00031734 0.00020024
#> Earthquake: MJD 60649 0.00054964 0.00027413 0.00001236 0.00108692
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000241
#> Stationary powerlaw Spectral index: 0.99999630
#> Stationary powerlaw Variance: 0.00000000
#> -------------------------------------------------------------
#> Missingness parameters
#> -------------------------------------------------------------
#> P(Z_{i+1} = 0 | Z_{i} = 1): 0.00064893
#> P(Z_{i+1} = 1 | Z_{i} = 0): 0.10000000
#> \hat{E[Z]}: 0.99355255
#> -------------------------------------------------------------
#> Running time: 0.29 seconds
#> -------------------------------------------------------------
summary(fit1, scale_parameters = TRUE)
#> Summary of Estimated Model
#> -------------------------------------------------------------
#> Functional parameters
#> -------------------------------------------------------------
#> Parameter Estimate Std_Deviation 95% CI Lower 95% CI Upper
#> -------------------------------------------------------------
#> Intercept 272.75238146 0.00810577 272.73649444 272.76826847
#> Trend -0.00263366 0.00001012 -0.00265349 -0.00261383
#> Sin (Annual) -1.00353660 0.01039388 -1.02390823 -0.98316497
#> Cos (Annual) -1.01237815 0.01030228 -1.03257024 -0.99218605
#> Sin (Semi-Annual) 0.31817543 0.01026675 0.29805298 0.33829789
#> Cos (Semi-Annual) -0.21598412 0.01025188 -0.23607744 -0.19589080
#> Jump: MJD 60649 -0.02138502 0.04822759 -0.11590936 0.07313931
#> Earthquake: MJD 60649 0.20075663 0.10012471 0.00451580 0.39699745
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000241
#> Stationary powerlaw Spectral index: 0.99999630
#> Stationary powerlaw Variance: 0.00000000
#> -------------------------------------------------------------
#> Missingness parameters
#> -------------------------------------------------------------
#> P(Z_{i+1} = 0 | Z_{i} = 1): 0.00064893
#> P(Z_{i+1} = 1 | Z_{i} = 0): 0.10000000
#> \hat{E[Z]}: 0.99355255
#> -------------------------------------------------------------
#> Running time: 0.29 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 0.74675532 0.00074723 0.74529077 0.74821987
#> Trend -0.00000721 0.00000053 -0.00000824 -0.00000618
#> Sin (Annual) -0.00274753 0.00017444 -0.00308943 -0.00240563
#> Cos (Annual) -0.00277174 0.00017559 -0.00311588 -0.00242760
#> Sin (Semi-Annual) 0.00087112 0.00012511 0.00062591 0.00111633
#> Cos (Semi-Annual) -0.00059133 0.00012246 -0.00083136 -0.00035131
#> Jump: MJD 60649 -0.00005855 0.00116660 -0.00234505 0.00222795
#> Earthquake: MJD 60649 0.00054964 0.00225461 -0.00386932 0.00496860
#> -------------------------------------------------------------
#> Stochastic parameters
#> -------------------------------------------------------------
#> White Noise Variance : 0.00000056
#> Flicker Noise Variance: 0.00000080
#> -------------------------------------------------------------
#> Missingness parameters
#> -------------------------------------------------------------
#> P(Z_{i+1} = 0 | Z_{i} = 1): 0.00064893
#> P(Z_{i+1} = 1 | Z_{i} = 0): 0.10000000
#> \hat{E[Z]}: 0.99355255
#> -------------------------------------------------------------
#> Running time: 1.14 seconds
#> -------------------------------------------------------------