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Summarize coverage predictions

Source: R/methods.R
summary.imugap_predict.Rd

Summarizes predicted coverage probabilities from an imugap_predict object by location, cohort, age, and dose for the requested quantiles.

Usage

# S3 method for class 'imugap_predict'
summary(object, probs = c(0.025, 0.5, 0.975), ...)

Arguments

object

an object of class imugap_predict returned by [predict()].

probs

numeric vector of probabilities/quantiles to compute (default: c(0.025, 0.5, 0.975)).

...

additional arguments (currently ignored).

Value

a [data.table()], containing target population parameters, posterior mean coverage (mean), and the requested quantiles (e.g. q2.5, q50, q97.5).

Examples

# Load example prediction object
data("predict_sim", package = "imuGAP")

# Summarize coverage predictions
summary(predict_sim)
#>       obs_c_id                  loc_id   age cohort  dose weight loc_c_id
#>          <int>                  <char> <int>  <num> <num>  <num>    <int>
#>    1:        1                   State     1     29     1      1        1
#>    2:        2                 Scruggs     1     29     1      1        2
#>    3:        3                  Simone     1     29     1      1        3
#>    4:        4                  Watson     1     29     1      1        4
#>    5:        5    Chickadee Elementary     1     29     1      1        8
#>   ---                                                                    
#> 1004:     1004     Mockingbird Academy    18     12     2      1       27
#> 1005:     1005 Kinglet Learning Center    18     12     2      1       25
#> 1006:     1006            Vireo School    18     12     2      1       28
#> 1007:     1007      Kingfisher Academy    18     12     2      1       24
#> 1008:     1008    Cormorant Elementary    18     12     2      1       22
#>            mean      q2_5       q50     q97_5
#>           <num>     <num>     <num>     <num>
#>    1: 0.0000000 0.0000000 0.0000000 0.0000000
#>    2: 0.0000000 0.0000000 0.0000000 0.0000000
#>    3: 0.0000000 0.0000000 0.0000000 0.0000000
#>    4: 0.0000000 0.0000000 0.0000000 0.0000000
#>    5: 0.0000000 0.0000000 0.0000000 0.0000000
#>   ---                                        
#> 1004: 0.9062623 0.8873584 0.9053190 0.9256850
#> 1005: 0.9843906 0.9760715 0.9842873 0.9937371
#> 1006: 0.9871394 0.9737656 0.9870097 0.9969445
#> 1007: 0.8921072 0.8709556 0.8927786 0.9108449
#> 1008: 0.9317356 0.9191129 0.9315590 0.9425783

# Summarize with custom quantiles
summary(predict_sim, probs = c(0.1, 0.5, 0.9))
#>       obs_c_id                  loc_id   age cohort  dose weight loc_c_id
#>          <int>                  <char> <int>  <num> <num>  <num>    <int>
#>    1:        1                   State     1     29     1      1        1
#>    2:        2                 Scruggs     1     29     1      1        2
#>    3:        3                  Simone     1     29     1      1        3
#>    4:        4                  Watson     1     29     1      1        4
#>    5:        5    Chickadee Elementary     1     29     1      1        8
#>   ---                                                                    
#> 1004:     1004     Mockingbird Academy    18     12     2      1       27
#> 1005:     1005 Kinglet Learning Center    18     12     2      1       25
#> 1006:     1006            Vireo School    18     12     2      1       28
#> 1007:     1007      Kingfisher Academy    18     12     2      1       24
#> 1008:     1008    Cormorant Elementary    18     12     2      1       22
#>            mean       q10       q50       q90
#>           <num>     <num>     <num>     <num>
#>    1: 0.0000000 0.0000000 0.0000000 0.0000000
#>    2: 0.0000000 0.0000000 0.0000000 0.0000000
#>    3: 0.0000000 0.0000000 0.0000000 0.0000000
#>    4: 0.0000000 0.0000000 0.0000000 0.0000000
#>    5: 0.0000000 0.0000000 0.0000000 0.0000000
#>   ---                                        
#> 1004: 0.9062623 0.8944733 0.9053190 0.9190568
#> 1005: 0.9843906 0.9786325 0.9842873 0.9913324
#> 1006: 0.9871394 0.9797486 0.9870097 0.9946662
#> 1007: 0.8921072 0.8815458 0.8927786 0.9038298
#> 1008: 0.9317356 0.9244273 0.9315590 0.9391698