Description Usage Arguments Value Author(s) References See Also Examples
Compute error measures for GP models:
mean absulte error ("mae"
), mean squared error ("mse"
),
standardised mse ("smse"
), mean standardised log loss ("msll"
),
Q2 ("q2"
), predictive variance adequation ("pva"
),
confidence interval accuracy ("cia"
).
1 2 3 4 5 6 7 8 | errorMeasureRegress(
y,
ytest,
mu,
varsigma,
type = "all",
control = list(nsigma = 1.96)
)
|
y |
a vector with the output observations used for training. |
ytest |
a vector with the output observations used for testing. |
mu |
a vector with the posterior mean. |
varsigma |
a vector with the posterior variances. |
type |
a character string corresponding to the type of the measure. |
control |
an optional list with parameters to be passed (e.g. cia: "nsigma"). |
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 23 24 25 26 27 28 29 30 31 32 33 34 | # 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)
mu <- apply(ynoise, 1, mean)
sigma <- apply(ynoise, 1, sd)
matplot(x, ynoise, type = "l", col = "gray70")
lines(x, y, lty = 2, col = "red")
lines(x, mu, col = "blue")
lines(x, mu+1.98*sigma, lty = 2)
lines(x, mu-1.98*sigma, lty = 2)
legend("topright", c("target", "mean", "confidence", "samples"),
lty = c(2,1,2,1), col = c("red", "blue", "black", "gray70"))
t(errorMeasureRegress(y, y, mu, sigma^2))
# results with low-level noises generating the toy example
set.seed(1)
ynoise <- y + matrix(rnorm(n*nbsamples, 0, 0.05), ncol = nbsamples)
mu <- apply(ynoise, 1, mean)
sigma <- apply(ynoise, 1, sd)
matplot(x, ynoise, type = "l", col = "gray70")
lines(x, y, lty = 2, col = "red")
lines(x, mu, col = "blue")
lines(x, mu+1.98*sigma, lty = 2)
lines(x, mu-1.98*sigma, lty = 2)
legend("topright", c("target", "mean", "confidence", "samples"),
lty = c(2,1,2,1), col = c("red", "blue", "black", "gray70"))
t(errorMeasureRegress(y, y, mu, sigma^2))
|
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