Compare two coins by correlation — compare_coins

Given two coins, this function returns the correlation between the two coins, for target datset dset and target indicator code(s) iCodes. Correlation is calculated as the Pearson correlation coefficient, but if compare_by = "Ranks" then this is the correlation coefficient of the ranks, which amounts to the Spearman rank correlation. Set compare_by = "Scores" to return the Pearson correlation between scores.

Usage

compare_coins_corr(coin1, coin2, dset, iCodes, compare_by = "ranks")

Arguments

coin1: A coin
coin2: A coin, with possibly alternative methodology. This should share at least two units in common with coin1.
dset: Target data set, must be present in both coin1 and coin2
iCodes: Character vector of indicator codes to correlate between the two coins.
compare_by: Either "Ranks" or "Scores".

Value

A list containing a correlation table and a list of comparison data frames.

Examples

# build example
coin <- build_example_coin()
#> iData checked and OK.
#> iMeta checked and OK.
#> Written data set to .$Data$Raw
#> Written data set to .$Data$Denominated
#> Written data set to .$Data$Imputed
#> Written data set to .$Data$Screened
#> Written data set to .$Data$Treated
#> Written data set to .$Data$Normalised
#> Written data set to .$Data$Aggregated

# copy coin
coin2 <- coin

# change to prank function (percentile ranks)
# we don't need to specify any additional parameters (f_n_para) here
coin2$Log$Normalise$global_specs <- list(f_n = "n_prank")

# regenerate
coin2 <- Regen(coin2)

# iCodes to compare: all at level 3 and 4
iCodes <- coin$Meta$Ind$iCode[which(coin$Meta$Ind$Level > 2)]

# compare index, sort by absolute rank difference
l_comp <- compare_coins_corr(coin, coin2, dset = "Aggregated", iCodes = iCodes)

# see df
l_comp$df_corr
#>       iCode Correlation
#> Conn   Conn   0.9780995
#> Sust   Sust   0.9299709
#> Index Index   0.9759276