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 1.695320 1.800861 5.448389 6.054856 1.0396569
#> Mazda RX4 Wag 1.695320 1.800861 5.448389 6.054856 1.7189414
#> Datsun 710 1.606906 1.727368 4.987355 5.625825 1.0495733
#> Hornet 4 Drive 1.676612 1.784035 5.347406 5.953834 1.6570193
#> Hornet Sportabout 1.801375 1.902728 6.057973 6.704159 2.0308820
#> Valiant 1.826321 1.930732 6.210995 6.894554 1.9599240
#> Duster 360 1.981599 2.112842 7.254334 8.271712 2.8280579
#> Merc 240D 1.523937 1.663476 4.590263 5.277626 1.0680470
#> Merc 230 1.606906 1.727368 4.987355 5.625825 1.0129108
#> Merc 280 1.778923 1.879482 5.923475 6.550111 1.9999173
#> Merc 280C 1.838613 1.944110 6.287808 6.987408 1.9476299
#> Merc 450SE 1.897332 2.008918 6.668083 7.455248 2.1161183
#> Merc 450SL 1.859424 1.965389 6.420036 7.137691 2.0280133
#> Merc 450SLC 1.946059 2.066369 7.001045 7.896099 2.7521614
#> Cadillac Fleetwood 2.128647 2.312955 8.403485 10.104234 3.8216149
#> Lincoln Continental 2.128647 2.312955 8.403485 10.104234 3.7911721
#> Chrysler Imperial 1.965001 2.091724 7.134916 8.098868 2.8767035
#> Fiat 128 1.104391 1.367764 3.017386 3.926559 0.0000000
#> Honda Civic 1.209543 1.440955 3.351954 4.224728 0.8604535
#> Toyota Corolla 1.023425 1.312220 2.782709 3.714409 0.0000000
#> Toyota Corona 1.671812 1.779742 5.321800 5.928326 1.7159516
#> Dodge Challenger 1.934149 2.051601 6.918151 7.780347 2.2011519
#> AMC Javelin 1.946059 2.066369 7.001045 7.896099 2.8119707
#> Camaro Z28 2.018595 2.161537 7.527743 8.684474 2.9673390
#> Pontiac Firebird 1.778923 1.879482 5.923475 6.550111 1.8535369
#> Fiat X1-9 1.374830 1.554393 3.954406 4.732214 0.9460408
#> Porsche 914-2 1.441619 1.603100 4.227536 4.968409 0.9960459
#> Lotus Europa 1.209543 1.440955 3.351954 4.224728 0.9191842
#> Ford Pantera L 1.922662 2.037729 6.839138 7.673164 2.8302432
#> Ferrari Dino 1.756814 1.857803 5.793950 6.409638 1.8338950
#> Maserati Bora 1.953567 2.076398 7.053806 7.975690 2.2464257
#> Volvo 142E 1.676612 1.784035 5.347406 5.953834 1.0370966
#> PI_log_upper PI_lower PI_upper
#> Mazda RX4 10.555782 2.828247 38398.835
#> Mazda RX4 Wag 11.101323 5.578620 66258.763
#> Datsun 710 10.426103 2.856432 33728.665
#> Hornet 4 Drive 10.203353 5.243658 26993.554
#> Hornet Sportabout 11.195095 7.620805 72772.603
#> Valiant 12.260140 7.098788 211111.126
#> Duster 360 13.443529 16.912583 689367.107
#> Merc 240D 9.531657 2.909691 13789.421
#> Merc 230 10.252529 2.753604 28354.153
#> Merc 280 12.049472 7.388445 171009.083
#> Merc 280C 12.038201 7.012049 169092.438
#> Merc 450SE 12.434391 8.298861 251296.945
#> Merc 450SL 12.171330 7.598975 193170.880
#> Merc 450SLC 13.031238 15.676479 456451.717
#> Cadillac Fleetwood 15.481235 45.677913 5289501.992
#> Lincoln Continental 15.917209 44.308305 8180050.752
#> Chrysler Imperial 13.542130 17.755645 760803.174
#> Fiat 128 7.293946 1.000000 1471.366
#> Honda Civic 8.174635 2.364233 3549.759
#> Toyota Corolla 7.256063 1.000000 1416.669
#> Toyota Corona 11.125328 5.561966 67868.553
#> Dodge Challenger 13.222067 9.035415 552421.437
#> AMC Javelin 13.061979 16.642684 470701.509
#> Camaro Z28 14.603280 19.440120 2198487.055
#> Pontiac Firebird 11.561309 6.382353 104957.284
#> Fiat X1-9 8.885048 2.575493 7223.161
#> Porsche 914-2 9.419399 2.707555 12325.173
#> Lotus Europa 8.139736 2.507244 3428.014
#> Ford Pantera L 13.479407 16.949583 714548.959
#> Ferrari Dino 11.428388 6.258215 91893.682
#> Maserati Bora 13.615207 9.453884 818482.094
#> Volvo 142E 10.794147 2.821014 48734.733