Allow prediction to predict for ages <1 or other regular times #75
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Assigning to @MikeLydeamore |
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It seems that this now works? It is unclear what the issue to me was: library(conmat)
polymod_setting_models$home
#>
#> Call: mgcv::bam(formula = formula, family = stats::poisson, data = .,
#> offset = log(participants))
#>
#> Coefficients:
#> (Intercept) school_probability work_probability
#> 6.493e-01 -1.442e-01 1.481e-01
#> s(gam_age_offdiag).1 s(gam_age_offdiag).2 s(gam_age_offdiag).3
#> -7.637e+01 3.727e+00 1.333e+01
#> s(gam_age_offdiag).4 s(gam_age_offdiag).5 s(gam_age_offdiag).6
#> 1.264e+00 4.240e+00 1.775e+00
#> s(gam_age_offdiag).7 s(gam_age_offdiag).8 s(gam_age_offdiag).9
#> -1.031e+00 1.662e-01 0.000e+00
#> s(gam_age_offdiag_2).1 s(gam_age_offdiag_2).2 s(gam_age_offdiag_2).3
#> -3.461e-04 -1.245e-05 3.558e-05
#> s(gam_age_offdiag_2).4 s(gam_age_offdiag_2).5 s(gam_age_offdiag_2).6
#> 7.667e-06 1.673e-05 -1.517e-05
#> s(gam_age_offdiag_2).7 s(gam_age_offdiag_2).8 s(gam_age_offdiag_2).9
#> -1.454e-05 -1.592e-05 -3.658e+01
#> s(gam_age_diag_prod).1 s(gam_age_diag_prod).2 s(gam_age_diag_prod).3
#> 1.732e+00 -2.292e-01 6.706e-01
#> s(gam_age_diag_prod).4 s(gam_age_diag_prod).5 s(gam_age_diag_prod).6
#> 1.252e-01 1.412e-01 1.773e-01
#> s(gam_age_diag_prod).7 s(gam_age_diag_prod).8 s(gam_age_diag_prod).9
#> -3.042e-01 1.141e-01 7.966e-01
#> s(gam_age_diag_sum).1 s(gam_age_diag_sum).2 s(gam_age_diag_sum).3
#> -6.203e-01 -2.365e-01 3.936e-01
#> s(gam_age_diag_sum).4 s(gam_age_diag_sum).5 s(gam_age_diag_sum).6
#> -1.735e-01 2.407e-02 -1.014e-02
#> s(gam_age_diag_sum).7 s(gam_age_diag_sum).8 s(gam_age_diag_sum).9
#> 3.443e-03 7.260e-02 0.000e+00
#> s(gam_age_pmax).1 s(gam_age_pmax).2 s(gam_age_pmax).3
#> 5.904e+00 -4.460e-01 -1.303e+00
#> s(gam_age_pmax).4 s(gam_age_pmax).5 s(gam_age_pmax).6
#> -2.804e-01 -8.263e-01 -4.846e-01
#> s(gam_age_pmax).7 s(gam_age_pmax).8 s(gam_age_pmax).9
#> 5.319e-01 3.135e-01 4.053e+01
#> s(gam_age_pmin).1 s(gam_age_pmin).2 s(gam_age_pmin).3
#> -5.091e-02 2.375e-02 -2.782e-02
#> s(gam_age_pmin).4 s(gam_age_pmin).5 s(gam_age_pmin).6
#> -2.755e-02 -1.336e-02 3.870e-02
#> s(gam_age_pmin).7 s(gam_age_pmin).8 s(gam_age_pmin).9
#> 2.465e-02 -5.669e-03 -3.865e+01
#>
#> Degrees of Freedom: 8787 Total (i.e. Null); 8754.398 Residual
#> Null Deviance: 44000
#> Residual Deviance: 10280 AIC: 26970
fairfield_abs_data <- abs_age_lga("Fairfield (C)")
fairfield_abs_data
#> # A tibble: 18 × 4 (conmat_population)
#> - age: lower.age.limit
#> - population: population
#> lga lower.age.limit year population
#> <chr> <dbl> <dbl> <dbl>
#> 1 Fairfield (C) 0 2020 12261
#> 2 Fairfield (C) 5 2020 13093
#> 3 Fairfield (C) 10 2020 13602
#> 4 Fairfield (C) 15 2020 14323
#> 5 Fairfield (C) 20 2020 15932
#> 6 Fairfield (C) 25 2020 16190
#> 7 Fairfield (C) 30 2020 14134
#> 8 Fairfield (C) 35 2020 13034
#> 9 Fairfield (C) 40 2020 12217
#> 10 Fairfield (C) 45 2020 13449
#> 11 Fairfield (C) 50 2020 13419
#> 12 Fairfield (C) 55 2020 13652
#> 13 Fairfield (C) 60 2020 12907
#> 14 Fairfield (C) 65 2020 10541
#> 15 Fairfield (C) 70 2020 8227
#> 16 Fairfield (C) 75 2020 5598
#> 17 Fairfield (C) 80 2020 4006
#> 18 Fairfield (C) 85 2020 4240
# We can predict the contact rate for Fairfield from the existing contact
# data, say, between the age groups of 0-15 in 5 year bins for school:
fairfield_contacts_0_5_quarterly <- predict_setting_contacts(
contact_model = polymod_setting_models,
population = fairfield_abs_data,
# three monthly - quarterly
age_breaks = seq(from = 0, to = 5, length.out = 5*4)
)
autoplot(fairfield_contacts_0_5_quarterly)Created on 2025-01-15 with reprex v2.1.1
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https://idem-lab.github.io/conmat/reference/predict_contacts.html
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