mae | R Documentation |
This function computes the mean-absolute error (MAE).
mae(predicted, observed, na.rm = FALSE)
predicted |
numeric vector that contains the model predicted data points (1st parameter) |
observed |
numeric vector that contains the observed data points (2nd parameter) |
na.rm |
logical vector that determines whether the missing values should be removed or not. |
(MAE) is expressed as
n^{-1} ∑ \limits_{i=1}^n{ ≤ft| P_i - O_i \right|}
the number of observations
the "model estimates or predictions"
the "thought-to-be reliable and pairwise matched observations"
MAE is fully discussed in the Willmott reference, including a comparison to root mean square error (RMSE).
mean-absolute error (MAE) as a numeric vector using the same
units as the given variables. The default choice is that any NA values
will be kept (na.rm = FALSE
). This can be changed by
specifying na.rm = TRUE
, such as mae(pre, obs, na.rm = TRUE)
.
Cort J. Willmott and Kenji Matsuura, "Advantages of the mean-absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance", Climate Research, Vol. 30: 79-82, 2005, http://climate.geog.udel.edu/~climate/publication_html/Pdf/WM_CR_05.pdf.
mape
for mean absolute percent error (MAPE), madstat
for
mean-absolute deviation (MAD), dr
for "index of agreement (dr)", vnse
for Nash-Sutcliffe model efficiency (NSE), and rmse
for
root mean square error (RMSE).
library("ie2misc") obs <- 1:10 # observed pre <- 2:11 # predicted mae(pre, obs) library("rando") set_n(100) # makes the example reproducible obs1 <- r_norm(.seed = 103) # observed pre1 <- r_norm(.seed = 102) # predicted # using the vectors pre1 and obs1 mae(pre1, obs1) # using a matrix of the numeric vectors pre1 and obs1 mat1 <- matrix(data = c(obs1, pre1), nrow = length(pre1), ncol = 2, byrow = FALSE, dimnames = list(c(rep("", length(pre1))), c("Predicted", "Observed"))) mae(mat1[, 2], mat1[, 1]) # mat1[, 1] # observed values from column 1 of mat1 # mat1[, 2] # predicted values from column 2 of mat1 # using a data.frame of the numeric vectors pre1 and obs1 df1 <- data.frame(obs1, pre1) mae(df1[, 2], df1[, 1]) # df1[, 1] # observed values from column 1 of df1 # df1[, 2] # predicted values from column 2 of df1 library("data.table") # using a data.table of the numeric vectors pre1 and obs1 df2 <- data.table(obs1, pre1) mae(df2[, 2, with = FALSE][[1]], df2[, 1, with = FALSE][[1]]) # df2[, 1, with = FALSE][[1]] # observed values from column 1 of df2 # df2[, 2, with = FALSE][[1]] # predicted values from column 2 of df2
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