Measures the association between two continuous variables with Pearson, Spearman and Kendall correlations.

assoc.twocont(x,y,weights=rep(1,length(x)),
nperm=NULL,distrib="asympt")

## Arguments

x a continuous variable (must be a numeric vector) a continuous variable (must be a numeric vector) an optional numeric vector of weights (by default, a vector of 1 for uniform weights) numeric. Number of permutations for the permutation test of independence. If NULL (default), no permutation test is performed. the null distribution of permutation test of independence can be approximated by its asymptotic distribution ("asympt", default) or via Monte Carlo resampling ("approx".

## Value

A data frame with Pearson, Spearman and Kendall correlations. The correlation value is in the first row and a p-value from a permutation (so non parametric) test of independence is in the second row.

## Author

Nicolas Robette

assoc.twocat, assoc.catcont, assoc.yx, condesc, catdesc, darma, ggassoc_scatter

## Examples

## Hollander & Wolfe (1973), p. 187f.
## Assessment of tuna quality.  We compare the Hunter L measure of
##  lightness to the averages of consumer panel scores (recoded as
##  integer values from 1 to 6 and averaged over 80 such values) in
##  9 lots of canned tuna.
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6,  3.1,  2.5,  5.0,  3.6,  4.0,  5.2,  2.8,  3.8)
assoc.twocont(x,y,nperm=100)
#>                       pearson   spearman    kendall
#> value              0.57118156 0.60000000 0.44444444
#> permutation.pvalue 0.02551669 0.03068824 0.03320006