Computes a (possibly weighted) dependence measure between x
and y
if
these are vectors. If x
and y
are matrices then the measure between the
columns of x
and the columns of y
are computed.
indep_test( x, y, method = "pearson", weights = NULL, remove_missing = TRUE, alternative = "two-sided" )
x, y | numeric vectors of data values. |
---|---|
method | the dependence measure; see Details for possible values. |
weights | an optional vector of weights for the observations. |
remove_missing | if |
alternative | indicates the alternative hypothesis and must be one of
|
Available methods:
"pearson"
: Pearson correlation
"spearman"
: Spearman's \(\rho\)
"kendall"
: Kendall's \(\tau\)
"blomqvist"
: Blomqvist's \(\beta\)
"hoeffding"
: Hoeffding's \(D\)
Partial matching of method names is enabled.
All methods except "hoeffding"
work with discrete variables.
x <- rnorm(100) y <- rpois(100, 1) # all but Hoeffding's D can handle ties w <- runif(100) indep_test(x, y, method = "kendall") # unweighted#> estimate statistic p_value n_eff method alternative #> 1 -0.01762804 -0.2129434 0.8313711 100 kendall two-sidedindep_test(x, y, method = "kendall", weights = w) # weighted#> estimate statistic p_value n_eff method alternative #> 1 0.01242062 0.1281651 0.8980183 74.08639 kendall two-sided