Description Usage Arguments Value Author(s) References See Also Examples
Compute error measures for GP models using Monte Carlo samples:
mean absulte error ("mae"
), mean squared error ("mse"
),
standardised mse ("smse"
), Q2 ("q2"
), predictive variance
adequation ("pva"
), confidence interval accuracy ("cia"
).
1 2 3 4 5 6 7 | errorMeasureRegressMC(
y,
ytest,
ysamples,
type = "all",
control = list(probs = c(0.05, 0.95))
)
|
y |
a vector with the output observations used for training. |
ytest |
a vector with the output observations used for testing. |
ysamples |
a matrix with posterior sample paths. Samples are indexed by columns. |
type |
a character string corresponding to the type of the measure. |
control |
an optional list with parameters to be passed (cia: "probs"). |
The values of the error measures.
A. F. Lopez-Lopera.
Rasmussen, C. E. and Williams, C. K. I. (2005), "Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)". The MIT Press. [link]
Bachoc, F. (2013), "Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model misspecification". Computational Statistics & Data Analysis, 66:55-69. [link]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # generating the toy example
n <- 100
w <- 4*pi
x <- seq(0, 1, length = n)
y <- sin(w*x)
# results with high-level noises generating the toy example
nbsamples <- 100
set.seed(1)
ynoise <- y + matrix(rnorm(n*nbsamples, 0, 10), ncol = nbsamples)
matplot(x, ynoise, type = "l", col = "gray70")
lines(x, y, lty = 2, col = "red")
legend("topright", c("target", "samples"), lty = c(2,1), col = c("red", "gray70"))
t(errorMeasureRegressMC(y, y, ynoise))
# results with low-level noises generating the toy example
set.seed(1)
ynoise <- y + matrix(rnorm(n*nbsamples, 0, 0.05), ncol = nbsamples)
matplot(x, ynoise, type = "l", col = "gray70")
lines(x, y, lty = 2, col = "red")
legend("topright", c("target", "samples"), lty = c(2,1), col = c("red", "gray70"))
t(errorMeasureRegressMC(y, y, ynoise))
|
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