Measures the association between two continuous variables

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

assoc.twocont(x, y, weights = NULL, na.rm = FALSE,
              nperm = NULL, distrib = "asympt")

Arguments

x

a continuous variable (must be a numeric vector)

y

a continuous variable (must be a numeric vector)

weights

numeric vector of weights. If NULL (default), uniform weights (i.e. all equal to 1) are used.

na.rm

logical, indicating whether NA values should be silently removed before the computation proceeds. Default is FALSE.

nperm

numeric. Number of permutations for the permutation test of independence. If NULL (default), no permutation test is performed.

distrib

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

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 0.05108689 0.04513644 0.04603937