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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 imugap_predict object returned by [predict()]

probs

numeric vector of probabilities/quantiles to compute. Defaults to 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: 0.8926001 0.8744692 0.8922801 0.9114879
#> 1005: 0.9747594 0.9625232 0.9750352 0.9847424
#> 1006: 0.9771210 0.9598872 0.9779763 0.9904439
#> 1007: 0.8772717 0.8550566 0.8777533 0.8961703
#> 1008: 0.9190308 0.9105220 0.9183768 0.9277653

# 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: 0.8926001 0.8808246 0.8922801 0.9039639
#> 1005: 0.9747594 0.9675553 0.9750352 0.9821826
#> 1006: 0.9771210 0.9668028 0.9779763 0.9846868
#> 1007: 0.8772717 0.8640975 0.8777533 0.8901798
#> 1008: 0.9190308 0.9131487 0.9183768 0.9254217