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Agreement rate of pairs on shared variables

Source: R/FlexRecordLinkage.r
RL_agreement.Rd

For a set of pairs, computes how often the two records agree on each variable in common_vars.

Usage

RL_agreement(data1, data2, common_vars, pairs, na.rm = TRUE, na_match = NULL)

Arguments

data1

Data frame containing common_vars.

data2

Data frame containing common_vars.

common_vars

Character vector, names of the variables to compare (must exist in both data1 and data2).

pairs

Data frame/matrix/list with 2 columns of indices (into data1, data2) for the pairs to evaluate.

na.rm

Logical; if TRUE (default), pairs with a missing value on a variable are excluded from that variable's agreement rate.

na_match

Logical, required if na.rm = FALSE: should a missing value be treated as agreeing (TRUE) or disagreeing (FALSE) with any value?

Value

List with agreements (named numeric vector, one entry per variable in common_vars) and, if available, true_agreements.

Examples

PIVs_config <- list( V1 = list(dynamics = "stable",
                               bound_mistakes = c(0.10,0.10),
                               fix_mistakes = c(NA,NA)),
                     V2 = list(dynamics = "stable",
                               bound_mistakes = c(0.10,0.10),
                               fix_mistakes = c(NA,NA)),
                     V3 = list(dynamics = "flexible",
                               bound_mistakes = c(NA,NA),
                               fix_mistakes = c(NA,NA)),
                     V4 = list(dynamics = "structured",
                               bound_mistakes = c(NA,NA),
                               fix_mistakes = c(0.03,0.03),
                               cond_hazard_cov = list(cov1=c("Xe", "Xf"),
                                                      cov2=c())) )
n_values  <- c( 5, 6, 7, 12 )
p_mistake <- list( V1 = c(0.02, 0.02), V2 = c(0.02, 0.02),
                   V3 = c(0.05, 0.05), V4 = c(0.02, 0.02) )
p_missing <- list( V1 = c(0.005, 0.005), V2 = c(0.005, 0.005),
                   V3 = c(0.005, 0.005), V4 = c(0.005, 0.005) )
cond_hazard_params <- list( V1 = c(), V2 = c(), 
                             V3 = c(), V4 = log(c(0.7, 0.6, 0.5)) )
gen_data <- simulate_data( PIVs_config, n_values, c(150, 200), 100, 
                           p_mistake, p_missing, cond_hazard_params, TRUE )
prep_data <- prepare_data( gen_data$data1, gen_data$data2, "1", "2",
                           PIVs_config, TRUE, "entity_id", TRUE )
#> '2' is the larger source, saved as B; '1' saved as A.
fit <- StEM( data = prep_data, StEM_iter = 10, StEM_burnin = 5, 
             gibbs_iter = 10, gibbs_burnin = 5, n_post_sample = 10 )                         
#> FlexRL
#> Warning: PIV 'V4' has one or more values with zero estimated frequency; check for rare categories.
#> Warning: PIV 'V4' has one or more values with zero estimated frequency; check for rare categories.
#> Warning: PIV 'V4' has one or more values with zero estimated frequency; check for rare categories.
#> Warning: PIV 'V4' has one or more values with zero estimated frequency; check for rare categories.
#> Warning: PIV 'V4' has one or more values with zero estimated frequency; check for rare categories.
#> Warning: PIV 'V4' has one or more values with zero estimated frequency; check for rare categories.
linked_pairs <- fit$Delta[fit$Delta$x > 0.5, ]
RL_agreement( prep_data$encodedA, prep_data$encodedB,
              names(PIVs_config), linked_pairs )
#> $agreements
#>        V1        V2        V3        V4 
#> 0.9863014 1.0000000 0.9315068 0.7671233 
#> 
RL_agreement( prep_data$encodedA, prep_data$encodedB,
              names(PIVs_config), prep_data$true_pairs )
#> $agreements
#>   V1   V2   V3   V4 
#> 0.93 0.96 0.87 0.75 
#>