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Default covariance structures

covariance_structures.Rd

These are multidimensional arrays of lists where the entry list[[i]][[j]] exists represents the covariance between elements of the i-th potential impact fraction and the j-th potential impact fraction. This is particularly useful when handling ensembles and totals.

Usage

covariance_structure(pif, is_variance = FALSE)

covariance_structure2(pif1, pif2)

default_weight_covariance_structure(pif, is_variance = FALSE)

default_weight_covariance_structure2(pif1, pif2)

default_parameter_covariance_structure(
  pif,
  parameter = "p",
  is_variance = FALSE
)

default_parameter_covariance_structure2(pif1, pif2, parameter = "p")

default_pif_covariance_structure(pif, is_variance = FALSE)

default_pif_covariance_structure2(pif1, pif2)

default_weight_pif_covariance_structure(pif, is_variance = FALSE)

default_weight_pif_covariance_structure2(pif1, pif2)

Arguments

pif

A potential impact fraction

is_variance

Whether the covariance structure corresponds to a variance (i.e. when pif1 and pif2 are identical)

pif1

A potential impact fraction to obtain a covariance structure with pif2.

pif2

A potential impact fraction to obtain a covariance structure with pif1,

parameter

Either beta or p. Indicating which parameter we are calculating covariance for.

Value

A nested list of lists with the entry [[i]][[j]] representing the covariance between elements i and j.

Note

The covariance_structures ending in 2 are meant to obtain the default covariance structure between two fractions pif1 and pif2 while the ones that don't end in 2 are meant to obtain the covariance structure of a fraction with itself.

Examples

pif_lead_women <- paf(0.27, 2.2, quiet = TRUE, var_p = 0.001, var_beta = 0.015,
                      label = "Women lead")
pif_rad_women  <- paf(0.12, 1.2, quiet = TRUE, var_p = 0.001, var_beta = 0.022,
                      label = "Women radiation")
pif_women      <- pif_ensemble(pif_lead_women, pif_rad_women, label = "Women",
                               weights = c(0.8, 0.72),
                               var_weights = matrix(c(0.3, 0.1, 0.1, 0.4), ncol = 2))

pif_lead_men   <- paf(0.30, 2.2, quiet = TRUE, var_p = 0.001, var_beta = 0.015,
                       label = "Men lead")
pif_rad_men    <- paf(0.10, 1.2, quiet = TRUE, var_p = 0.001, var_beta = 0.022,
                        label = "Men radiation")
pif_men        <- pif_ensemble(pif_lead_men, pif_rad_men, label = "Men",
                        weights = c(0.65, 0.68),
                        var_weights = matrix(c(0.1, -0.2, -0.2, 0.5), ncol = 2))
pif_tot        <- pif_total(pif_men, pif_women,
                        weights = c(0.49, 0.51), label = "Population",
                        var_weights = matrix(c(0.22, 0.4, 0.4, 0.8), ncol = 2))

#This is the default constructor of a covariance. Use it for custom covariances
covariance_structure(pif_lead_women)
#>            Women lead
#> Women lead .         
covariance_structure2(pif_lead_women, pif_lead_men)
#>            Women lead Men lead
#> Women lead .          .       
#> Men lead   .          .       
default_weight_covariance_structure2(pif_men, pif_men)
#>               Men Men lead Men radiation
#> Men           2x2 .        .            
#> Men lead      .   .        .            
#> Men radiation .   .        .            
default_weight_covariance_structure(pif_tot)
#>                 Population Men Men lead Men radiation Women Women lead
#> Population      2x2        .   .        .             .     .         
#> Men             .          2x2 .        .             .     .         
#> Men lead        .          .   .        .             .     .         
#> Men radiation   .          .   .        .             .     .         
#> Women           .          .   .        .             2x2   .         
#> Women lead      .          .   .        .             .     .         
#> Women radiation .          .   .        .             .     .         
#>                 Women radiation
#> Population      .              
#> Men             .              
#> Men lead        .              
#> Men radiation   .              
#> Women           .              
#> Women lead      .              
#> Women radiation .              
default_weight_covariance_structure2(pif_men, pif_women)
#>                 Men Men lead Men radiation Women Women lead Women radiation
#> Men             2x2 .        .             .     .          .              
#> Men lead        .   .        .             .     .          .              
#> Men radiation   .   .        .             .     .          .              
#> Women           .   .        .             2x2   .          .              
#> Women lead      .   .        .             .     .          .              
#> Women radiation .   .        .             .     .          .              
default_parameter_covariance_structure(pif_tot, parameter = "beta")
#>                 Population Men Men lead Men radiation Women Women lead
#> Population      .          .   .        .             .     .         
#> Men             .          .   .        .             .     .         
#> Men lead        .          .   0.015    .             .     0.015     
#> Men radiation   .          .   .        0.022         .     .         
#> Women           .          .   .        .             .     .         
#> Women lead      .          .   0.015    .             .     0.015     
#> Women radiation .          .   .        0.022         .     .         
#>                 Women radiation
#> Population      .              
#> Men             .              
#> Men lead        .              
#> Men radiation   0.022          
#> Women           .              
#> Women lead      .              
#> Women radiation 0.022          
default_parameter_covariance_structure2(pif_lead_women, pif_lead_men, parameter = "beta")
#>            Women lead Men lead
#> Women lead 0.015      0.015   
#> Men lead   0.015      0.015