r | R Documentation |
It estimates the Pearson's coefficient of correlation (r) for a continuous predicted-observed dataset.
r(data = NULL, obs, pred, tidy = FALSE, na.rm = TRUE)
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. |
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
an object of class numeric
within a list
(if tidy = FALSE) or within a
data frame
(if tidy = TRUE).
Kirch (2008) Pearson’s Correlation Coefficient. In: Kirch W. (eds) Encyclopedia of Public Health. Springer, Dordrecht. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1007/978-1-4020-5614-7_2569")}
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)
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