tests/testthat/_snaps/models-fit-with-furrr.md

In njtierney/conmat: Builds Contact Matrices using GAMs and Population Data

predict_setting_contact model prints appropriately

Code
  contact_model_pred
Message

  -- Setting Prediction Matrices -------------------------------------------------
Output

Message
  A list of matrices containing the model predicted contact rate between ages in
  each setting.
Output

Message
  There are 5 age breaks, ranging 0-20+ years, with a regular 5 year interval
Output

Message
  * home: a 5x5 <matrix>
  * work: a 5x5 <matrix>
  * school: a 5x5 <matrix>
  * other: a 5x5 <matrix>
  * all: a 5x5 <matrix>
  i Access each <matrix> with `x$name`
  i e.g., `x$home`

fit_setting_contact model prints appropriately

Code
  contact_model
Message

  -- Fitted Setting Contact Models -----------------------------------------------
Output

Message
  A list of fitted <bam> models for each setting. Each <bam> model predicts the
  contact rate between ages, for that setting.
Output

Message
  There are 20 age breaks, ranging 0-20 years, with a regular 1 year interval
Output

Message
  * home: a <bam> model (441 obs)
  * work: a <bam> model (441 obs)
  * school: a <bam> model (441 obs)
  * other: a <bam> model (441 obs)
  i Access each <bam> with `x$name`
  i e.g., `x$home`

list names are kept

Code
  names(contact_model)
Output
  [1] "home"   "work"   "school" "other"

Code
  names(contact_model_pred)
Output
  [1] "home"   "work"   "school" "other"  "all"

Model coefficients are the same

Code
  names(contact_model[[1]]$coefficients)
Output
   [1] "(Intercept)"            "school_probability"     "work_probability"      
   [4] "s(gam_age_offdiag).1"   "s(gam_age_offdiag).2"   "s(gam_age_offdiag).3"  
   [7] "s(gam_age_offdiag).4"   "s(gam_age_offdiag).5"   "s(gam_age_offdiag).6"  
  [10] "s(gam_age_offdiag).7"   "s(gam_age_offdiag).8"   "s(gam_age_offdiag).9"  
  [13] "s(gam_age_offdiag_2).1" "s(gam_age_offdiag_2).2" "s(gam_age_offdiag_2).3"
  [16] "s(gam_age_offdiag_2).4" "s(gam_age_offdiag_2).5" "s(gam_age_offdiag_2).6"
  [19] "s(gam_age_offdiag_2).7" "s(gam_age_offdiag_2).8" "s(gam_age_offdiag_2).9"
  [22] "s(gam_age_diag_prod).1" "s(gam_age_diag_prod).2" "s(gam_age_diag_prod).3"
  [25] "s(gam_age_diag_prod).4" "s(gam_age_diag_prod).5" "s(gam_age_diag_prod).6"
  [28] "s(gam_age_diag_prod).7" "s(gam_age_diag_prod).8" "s(gam_age_diag_prod).9"
  [31] "s(gam_age_diag_sum).1"  "s(gam_age_diag_sum).2"  "s(gam_age_diag_sum).3" 
  [34] "s(gam_age_diag_sum).4"  "s(gam_age_diag_sum).5"  "s(gam_age_diag_sum).6" 
  [37] "s(gam_age_diag_sum).7"  "s(gam_age_diag_sum).8"  "s(gam_age_diag_sum).9" 
  [40] "s(gam_age_pmax).1"      "s(gam_age_pmax).2"      "s(gam_age_pmax).3"     
  [43] "s(gam_age_pmax).4"      "s(gam_age_pmax).5"      "s(gam_age_pmax).6"     
  [46] "s(gam_age_pmax).7"      "s(gam_age_pmax).8"      "s(gam_age_pmax).9"     
  [49] "s(gam_age_pmin).1"      "s(gam_age_pmin).2"      "s(gam_age_pmin).3"     
  [52] "s(gam_age_pmin).4"      "s(gam_age_pmin).5"      "s(gam_age_pmin).6"     
  [55] "s(gam_age_pmin).7"      "s(gam_age_pmin).8"      "s(gam_age_pmin).9"

Code
  names(contact_model[[2]]$coefficients)
Output
   [1] "(Intercept)"            "school_probability"     "work_probability"      
   [4] "s(gam_age_offdiag).1"   "s(gam_age_offdiag).2"   "s(gam_age_offdiag).3"  
   [7] "s(gam_age_offdiag).4"   "s(gam_age_offdiag).5"   "s(gam_age_offdiag).6"  
  [10] "s(gam_age_offdiag).7"   "s(gam_age_offdiag).8"   "s(gam_age_offdiag).9"  
  [13] "s(gam_age_offdiag_2).1" "s(gam_age_offdiag_2).2" "s(gam_age_offdiag_2).3"
  [16] "s(gam_age_offdiag_2).4" "s(gam_age_offdiag_2).5" "s(gam_age_offdiag_2).6"
  [19] "s(gam_age_offdiag_2).7" "s(gam_age_offdiag_2).8" "s(gam_age_offdiag_2).9"
  [22] "s(gam_age_diag_prod).1" "s(gam_age_diag_prod).2" "s(gam_age_diag_prod).3"
  [25] "s(gam_age_diag_prod).4" "s(gam_age_diag_prod).5" "s(gam_age_diag_prod).6"
  [28] "s(gam_age_diag_prod).7" "s(gam_age_diag_prod).8" "s(gam_age_diag_prod).9"
  [31] "s(gam_age_diag_sum).1"  "s(gam_age_diag_sum).2"  "s(gam_age_diag_sum).3" 
  [34] "s(gam_age_diag_sum).4"  "s(gam_age_diag_sum).5"  "s(gam_age_diag_sum).6" 
  [37] "s(gam_age_diag_sum).7"  "s(gam_age_diag_sum).8"  "s(gam_age_diag_sum).9" 
  [40] "s(gam_age_pmax).1"      "s(gam_age_pmax).2"      "s(gam_age_pmax).3"     
  [43] "s(gam_age_pmax).4"      "s(gam_age_pmax).5"      "s(gam_age_pmax).6"     
  [46] "s(gam_age_pmax).7"      "s(gam_age_pmax).8"      "s(gam_age_pmax).9"     
  [49] "s(gam_age_pmin).1"      "s(gam_age_pmin).2"      "s(gam_age_pmin).3"     
  [52] "s(gam_age_pmin).4"      "s(gam_age_pmin).5"      "s(gam_age_pmin).6"     
  [55] "s(gam_age_pmin).7"      "s(gam_age_pmin).8"      "s(gam_age_pmin).9"

Code
  names(contact_model[[3]]$coefficients)
Output
   [1] "(Intercept)"            "school_probability"     "work_probability"      
   [4] "s(gam_age_offdiag).1"   "s(gam_age_offdiag).2"   "s(gam_age_offdiag).3"  
   [7] "s(gam_age_offdiag).4"   "s(gam_age_offdiag).5"   "s(gam_age_offdiag).6"  
  [10] "s(gam_age_offdiag).7"   "s(gam_age_offdiag).8"   "s(gam_age_offdiag).9"  
  [13] "s(gam_age_offdiag_2).1" "s(gam_age_offdiag_2).2" "s(gam_age_offdiag_2).3"
  [16] "s(gam_age_offdiag_2).4" "s(gam_age_offdiag_2).5" "s(gam_age_offdiag_2).6"
  [19] "s(gam_age_offdiag_2).7" "s(gam_age_offdiag_2).8" "s(gam_age_offdiag_2).9"
  [22] "s(gam_age_diag_prod).1" "s(gam_age_diag_prod).2" "s(gam_age_diag_prod).3"
  [25] "s(gam_age_diag_prod).4" "s(gam_age_diag_prod).5" "s(gam_age_diag_prod).6"
  [28] "s(gam_age_diag_prod).7" "s(gam_age_diag_prod).8" "s(gam_age_diag_prod).9"
  [31] "s(gam_age_diag_sum).1"  "s(gam_age_diag_sum).2"  "s(gam_age_diag_sum).3" 
  [34] "s(gam_age_diag_sum).4"  "s(gam_age_diag_sum).5"  "s(gam_age_diag_sum).6" 
  [37] "s(gam_age_diag_sum).7"  "s(gam_age_diag_sum).8"  "s(gam_age_diag_sum).9" 
  [40] "s(gam_age_pmax).1"      "s(gam_age_pmax).2"      "s(gam_age_pmax).3"     
  [43] "s(gam_age_pmax).4"      "s(gam_age_pmax).5"      "s(gam_age_pmax).6"     
  [46] "s(gam_age_pmax).7"      "s(gam_age_pmax).8"      "s(gam_age_pmax).9"     
  [49] "s(gam_age_pmin).1"      "s(gam_age_pmin).2"      "s(gam_age_pmin).3"     
  [52] "s(gam_age_pmin).4"      "s(gam_age_pmin).5"      "s(gam_age_pmin).6"     
  [55] "s(gam_age_pmin).7"      "s(gam_age_pmin).8"      "s(gam_age_pmin).9"

Code
  names(contact_model[[4]]$coefficients)
Output
   [1] "(Intercept)"            "school_probability"     "work_probability"      
   [4] "s(gam_age_offdiag).1"   "s(gam_age_offdiag).2"   "s(gam_age_offdiag).3"  
   [7] "s(gam_age_offdiag).4"   "s(gam_age_offdiag).5"   "s(gam_age_offdiag).6"  
  [10] "s(gam_age_offdiag).7"   "s(gam_age_offdiag).8"   "s(gam_age_offdiag).9"  
  [13] "s(gam_age_offdiag_2).1" "s(gam_age_offdiag_2).2" "s(gam_age_offdiag_2).3"
  [16] "s(gam_age_offdiag_2).4" "s(gam_age_offdiag_2).5" "s(gam_age_offdiag_2).6"
  [19] "s(gam_age_offdiag_2).7" "s(gam_age_offdiag_2).8" "s(gam_age_offdiag_2).9"
  [22] "s(gam_age_diag_prod).1" "s(gam_age_diag_prod).2" "s(gam_age_diag_prod).3"
  [25] "s(gam_age_diag_prod).4" "s(gam_age_diag_prod).5" "s(gam_age_diag_prod).6"
  [28] "s(gam_age_diag_prod).7" "s(gam_age_diag_prod).8" "s(gam_age_diag_prod).9"
  [31] "s(gam_age_diag_sum).1"  "s(gam_age_diag_sum).2"  "s(gam_age_diag_sum).3" 
  [34] "s(gam_age_diag_sum).4"  "s(gam_age_diag_sum).5"  "s(gam_age_diag_sum).6" 
  [37] "s(gam_age_diag_sum).7"  "s(gam_age_diag_sum).8"  "s(gam_age_diag_sum).9" 
  [40] "s(gam_age_pmax).1"      "s(gam_age_pmax).2"      "s(gam_age_pmax).3"     
  [43] "s(gam_age_pmax).4"      "s(gam_age_pmax).5"      "s(gam_age_pmax).6"     
  [46] "s(gam_age_pmax).7"      "s(gam_age_pmax).8"      "s(gam_age_pmax).9"     
  [49] "s(gam_age_pmin).1"      "s(gam_age_pmin).2"      "s(gam_age_pmin).3"     
  [52] "s(gam_age_pmin).4"      "s(gam_age_pmin).5"      "s(gam_age_pmin).6"     
  [55] "s(gam_age_pmin).7"      "s(gam_age_pmin).8"      "s(gam_age_pmin).9"

Matrix dims are kept

Code
  map(contact_model_pred, dim)
Output
  $home
  [1] 5 5

  $work
  [1] 5 5

  $school
  [1] 5 5

  $other
  [1] 5 5

  $all
  [1] 5 5

njtierney/conmat documentation built on April 17, 2025, 10:27 p.m.