Summarise a filtration
Usage
# S3 method for class 'qpm_filtration'
summary(object, ...)Value
A data frame with, for each model variable, whether it was observed and the mean, standard deviation and range of its smoothed path, plus the mean estimation standard error for latent states.
Examples
sol <- qpm_solve(qpm_template("bkl"))
obs <- simulate(sol, nsim = 40, seed = 1, burn = 20)
summary(qpm_filter(sol, obs[, c("period", "pi", "i", "q")]))
#> variable observed mean sd min max se
#> 1 y_gap FALSE 0.04519559 1.54784127 -2.8505898 3.1067732 7.020476e-01
#> 2 pi TRUE 5.04893869 3.18063241 -0.7145051 11.0223756 4.757478e-16
#> 3 pi4 FALSE 5.00742119 2.77174497 0.2513834 9.9821936 3.658192e-02
#> 4 i TRUE 8.79328569 2.26363254 4.7734276 14.2616921 2.451105e-16
#> 5 r FALSE 3.79193663 2.12811527 0.6103611 9.8832048 2.585730e-01
#> 6 r_gap FALSE -0.22335620 2.08082042 -3.4703693 5.6069414 4.122907e-01
#> 7 q TRUE -0.41534943 3.23538008 -8.6234934 5.3182994 4.031918e-16
#> 8 q_gap FALSE -0.34318267 3.23983308 -8.7061285 5.4990956 6.144938e-01
#> 9 q_bar FALSE -0.07216676 0.20561635 -0.3919603 0.2248449 6.144938e-01
#> 10 r_bar FALSE 4.01529283 0.19619457 3.7457290 4.2908480 4.118036e-01
#> 11 dy_obs FALSE 3.62226029 3.14190201 -4.4097589 8.4451094 2.532576e+00
#> 12 dy_bar FALSE 3.50000000 0.00000000 3.5000000 3.5000000 3.796632e-01
#> 13 ystar_gap FALSE 0.02624463 0.15748631 -0.2585446 0.2734507 4.691442e-01
#> 14 istar FALSE 2.93719109 0.14990160 2.5868953 3.1850594 5.182067e-01
#> 15 pistar FALSE 2.01735334 0.08915607 1.8443020 2.2971455 6.843049e-01
#> 16 rstar FALSE 0.92504375 0.19766030 0.3788935 1.2592933 6.743169e-01
#> 17 prem FALSE 2.82553081 0.41639335 1.8524869 3.5140539 6.858641e-01