Computes CI from posterior, and PI for Tweedie and scat gams.
predict_intervals(object_fit, new_data, n = 1000, CI = TRUE, PI = TRUE, ...)A data.frame with intervals.
gam1 <- gam(cyl ~ mpg, data=mtcars, family = tw)
predict_intervals(gam1)
#> CI_log_lower CI_log_upper CI_lower CI_upper PI_log_lower
#> Mazda RX4 1.695582 1.806080 5.449818 6.086542 1.837499
#> Mazda RX4 Wag 1.695582 1.806080 5.449818 6.086542 1.804583
#> Datsun 710 1.605654 1.731436 4.981118 5.648762 1.069402
#> Hornet 4 Drive 1.675459 1.788199 5.341244 5.978676 1.070734
#> Hornet Sportabout 1.804247 1.903965 6.075395 6.712457 1.916893
#> Valiant 1.830577 1.930271 6.237484 6.891381 1.965308
#> Duster 360 1.983270 2.110209 7.266469 8.249967 2.990507
#> Merc 240D 1.523636 1.671267 4.588882 5.318900 1.012239
#> Merc 230 1.605654 1.731436 4.981118 5.648762 1.059351
#> Merc 280 1.780449 1.883017 5.932521 6.573306 1.916469
#> Merc 280C 1.843366 1.944407 6.317767 6.989484 2.021251
#> Merc 450SE 1.901179 2.008202 6.693781 7.449908 2.089675
#> Merc 450SL 1.864695 1.967026 6.453966 7.149384 2.012457
#> Merc 450SLC 1.949180 2.064990 7.022926 7.885218 2.133638
#> Cadillac Fleetwood 2.129312 2.308334 8.409077 10.057651 3.941460
#> Lincoln Continental 2.129312 2.308334 8.409077 10.057651 3.714985
#> Chrysler Imperial 1.968111 2.089631 7.157144 8.081933 2.768111
#> Fiat 128 1.105738 1.377766 3.021453 3.966031 0.000000
#> Honda Civic 1.212154 1.446609 3.360716 4.248683 0.000000
#> Toyota Corolla 1.025051 1.323989 2.787237 3.758383 0.000000
#> Toyota Corona 1.670336 1.783732 5.313954 5.952026 1.711928
#> Dodge Challenger 1.937709 2.051277 6.942826 7.777827 2.749957
#> AMC Javelin 1.949180 2.064990 7.022926 7.885218 2.397059
#> Camaro Z28 2.021806 2.160467 7.551949 8.675187 2.885766
#> Pontiac Firebird 1.780449 1.883017 5.932521 6.573306 1.850931
#> Fiat X1-9 1.373076 1.559275 3.947474 4.755373 0.947229
#> Porsche 914-2 1.441452 1.609014 4.226827 4.997882 1.004318
#> Lotus Europa 1.212154 1.446609 3.360716 4.248683 0.000000
#> Ford Pantera L 1.925455 2.036797 6.858268 7.666012 2.748559
#> Ferrari Dino 1.757120 1.861139 5.795723 6.431061 1.938334
#> Maserati Bora 1.957157 2.074346 7.079173 7.959336 2.830925
#> Volvo 142E 1.675459 1.788199 5.341244 5.978676 1.097147
#> PI_log_upper PI_lower PI_upper
#> Mazda RX4 10.804241 6.280809 49229.153
#> Mazda RX4 Wag 10.903344 6.077437 54357.817
#> Datsun 710 10.224851 2.913637 27580.137
#> Hornet 4 Drive 10.493338 2.917519 36074.367
#> Hornet Sportabout 11.362047 6.799799 85995.239
#> Valiant 11.990495 7.137112 161215.202
#> Duster 360 13.594546 19.895773 801745.380
#> Merc 240D 9.533633 2.751755 13816.700
#> Merc 230 10.293878 2.884497 29551.150
#> Merc 280 11.707176 6.796918 121440.055
#> Merc 280C 12.101293 7.547760 180104.565
#> Merc 450SE 12.365603 8.082288 234591.926
#> Merc 450SL 12.360216 7.481680 233331.660
#> Merc 450SLC 13.209279 8.445533 545402.060
#> Cadillac Fleetwood 16.147148 51.493752 10294781.980
#> Lincoln Continental 16.361353 41.057969 12753972.579
#> Chrysler Imperial 13.174684 15.928512 526856.646
#> Fiat 128 7.214522 1.000000 1359.024
#> Honda Civic 7.914168 1.000000 2735.769
#> Toyota Corolla 7.121770 1.000000 1238.641
#> Toyota Corona 11.232324 5.539633 75532.963
#> Dodge Challenger 13.311771 15.641962 604266.459
#> AMC Javelin 13.583642 10.990809 793050.244
#> Camaro Z28 14.226034 17.917280 1507606.588
#> Pontiac Firebird 11.544513 6.365746 103209.135
#> Fiat X1-9 8.475659 2.578554 4796.582
#> Porsche 914-2 9.083428 2.730045 8808.111
#> Lotus Europa 8.307705 1.000000 4054.997
#> Ford Pantera L 12.877432 15.620101 391379.144
#> Ferrari Dino 11.558573 6.947164 104670.594
#> Maserati Bora 13.554351 16.961149 770157.820
#> Volvo 142E 10.914100 2.995606 54945.635