r {metrica}

Sample Correlation Coefficient (r)

Description

It estimates the Pearson's coefficient of correlation (r) for a continuous predicted-observed dataset.

Usage

r(data = NULL, obs, pred, tidy = FALSE, na.rm = TRUE)

Arguments

data

(Optional) argument to call an existing data frame containing the data.

obs

Vector with observed values (numeric).

pred

Vector with predicted values (numeric).

tidy

Logical operator (TRUE/FALSE) to decide the type of return. TRUE returns a data.frame, FALSE returns a list; Default : FALSE.

na.rm

Logic argument to remove rows with missing values (NA). Default is na.rm = TRUE.

Details

The r coefficient measures the strength of linear relationship between two variables. It only accounts for precision, but it is not sensitive to lack of prediction accuracy. It is a normalized, dimensionless coefficient, that ranges between -1 to 1. It is expected that predicted and observed values will show 0 < r < 1. It is also known as the Pearson Product Moment Correlation, among other names. For the formula and more details, see online-documentation

Value

an object of class numeric within a list (if tidy = FALSE) or within a ⁠data frame⁠ (if tidy = TRUE).

References

Kirch (2008) Pearson’s Correlation Coefficient. In: Kirch W. (eds) Encyclopedia of Public Health. Springer, Dordrecht. doi:10.1007/978-1-4020-5614-7_2569

Examples

set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
r(obs = X, pred = Y)