Ue | R Documentation |
It estimates the Ue component from the sum of squares decomposition described by Smith & Rose (1995).
Ue(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. |
an object of class numeric
within a list
(if tidy = FALSE) or within a
data frame
(if tidy = TRUE).
The Ue estimates the proportion of the total sum of squares related to the
random error (unsystematic error or variance) following the sum of squares decomposition
suggested by Smith and Rose (1995) also known as Theil's partial inequalities.
For the formula and more details, see online-documentation
Smith & Rose (1995). Model goodness-of-fit analysis using regression and related techniques. Ecol. Model. 77, 49–64. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/0304-3800(93)E0074-D")}
set.seed(1)
X <- rnorm(n = 100, mean = 0, sd = 10)
Y <- X + rnorm(n=100, mean = 0, sd = 3)
Ue(obs = X, pred = Y)
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