Computes CI from posterior, and PI for Tweedie and scat gams.
predict_intervals(object_fit, new_data, n = 1000, CI = TRUE, PI = TRUE, ...)[gam OR bam] The fit to use for predictions.
[data.frame] The data to predict onto.
[numeric] The number of simulations to run for parameters.
[logical] Whether to compute the CI.
[logical] Whether to compute the PI.
further arguments passed to the quantile function.
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 1.696655 1.802219 5.455667 6.063087 1.6184395
#> Mazda RX4 Wag 1.696655 1.802219 5.455667 6.063087 1.7681710
#> Datsun 710 1.608668 1.727752 4.996152 5.627989 1.0229879
#> Hornet 4 Drive 1.677740 1.784669 5.353444 5.957606 1.7630792
#> Hornet Sportabout 1.802893 1.899376 6.067176 6.681724 1.9513892
#> Valiant 1.828760 1.927339 6.226160 6.871202 1.9169612
#> Duster 360 1.981153 2.108663 7.251100 8.237220 3.0388432
#> Merc 240D 1.525914 1.666879 4.599346 5.295616 1.2012097
#> Merc 230 1.608668 1.727752 4.996152 5.627989 1.0657058
#> Merc 280 1.778969 1.877397 5.923746 6.536469 1.9086476
#> Merc 280C 1.841707 1.940798 6.307296 6.964305 2.0485863
#> Merc 450SE 1.896624 2.005722 6.663361 7.431458 2.6569580
#> Merc 450SL 1.862177 1.962742 6.437739 7.118821 2.6596228
#> Merc 450SLC 1.945714 2.063190 6.998627 7.871035 2.1134432
#> Cadillac Fleetwood 2.130481 2.307179 8.418914 10.046048 3.0998211
#> Lincoln Continental 2.130481 2.307179 8.418914 10.046048 3.9882060
#> Chrysler Imperial 1.965590 2.088430 7.139123 8.072233 2.8718627
#> Fiat 128 1.111221 1.374947 3.038066 3.954866 0.7887606
#> Honda Civic 1.215517 1.446733 3.372036 4.249209 0.8042950
#> Toyota Corolla 1.032517 1.319607 2.808126 3.741952 0.0000000
#> Toyota Corona 1.673145 1.780227 5.328899 5.931204 1.8329931
#> Dodge Challenger 1.933798 2.048144 6.915730 7.753499 2.1581064
#> AMC Javelin 1.945714 2.063190 6.998627 7.871035 2.8090910
#> Camaro Z28 2.020355 2.157973 7.541000 8.653577 2.9002678
#> Pontiac Firebird 1.778969 1.877397 5.923746 6.536469 1.9438387
#> Fiat X1-9 1.378674 1.560952 3.969633 4.763356 0.9532843
#> Porsche 914-2 1.446164 1.606902 4.246792 4.987335 0.9676876
#> Lotus Europa 1.215517 1.446733 3.372036 4.249209 0.8166978
#> Ford Pantera L 1.921124 2.033783 6.828630 7.642949 2.6228794
#> Ferrari Dino 1.757029 1.856869 5.795196 6.403657 1.8788985
#> Maserati Bora 1.953572 2.073227 7.053836 7.950437 2.1299563
#> Volvo 142E 1.677740 1.784669 5.353444 5.957606 1.7826565
#> PI_log_upper PI_lower PI_upper
#> Mazda RX4 10.545147 5.045211 37992.600
#> Mazda RX4 Wag 11.032326 5.860126 61841.246
#> Datsun 710 9.975755 2.781493 21498.851
#> Hornet 4 Drive 10.855559 5.830363 51821.404
#> Hornet Sportabout 11.894744 7.038458 146494.672
#> Valiant 12.255080 6.800263 210045.650
#> Duster 360 13.782163 20.881074 967202.544
#> Merc 240D 9.895260 3.324136 19836.123
#> Merc 230 10.307998 2.902887 29971.375
#> Merc 280 11.710292 6.743962 121819.089
#> Merc 280C 12.276301 7.756927 214550.535
#> Merc 450SE 12.924508 14.252865 410244.363
#> Merc 450SL 12.125150 14.290898 184452.918
#> Merc 450SLC 13.513979 8.276690 739684.114
#> Cadillac Fleetwood 15.171395 22.193979 3880193.004
#> Lincoln Continental 15.551967 53.958004 5677190.778
#> Chrysler Imperial 13.600583 17.669901 806599.944
#> Fiat 128 7.402888 2.200667 1640.715
#> Honda Civic 8.209344 2.235120 3675.132
#> Toyota Corolla 7.440546 1.000000 1703.680
#> Toyota Corona 10.994498 6.252573 59545.593
#> Dodge Challenger 12.824935 8.654734 371362.798
#> AMC Javelin 13.081558 16.594826 480008.129
#> Camaro Z28 14.821971 18.179013 2735899.130
#> Pontiac Firebird 11.919335 6.985515 150141.710
#> Fiat X1-9 8.885730 2.594216 7228.090
#> Porsche 914-2 9.293656 2.631851 10868.846
#> Lotus Europa 8.129313 2.263015 3392.469
#> Ford Pantera L 12.334997 13.775332 227520.732
#> Ferrari Dino 11.381486 6.546290 87683.203
#> Maserati Bora 13.497700 8.414499 727740.896
#> Volvo 142E 11.098968 5.945630 66102.931