q1Symm | R Documentation |
Qualitative correlation or covariance kernel with one input and general symmetric correlation.
q1Symm(factor, input = "x", cov = c("corr", "homo", "hete"), intAsChar = TRUE)
factor |
A factor with the wanted levels for the covariance kernel object. |
input |
Name of (qualitative) input for the kernel. |
cov |
Character telling if the result is a correlation kernel, an homoscedastic covariance kernel or an heteroscedastic covariance kernel with an arbitrary variance vector. |
intAsChar |
Logical. If |
An object with class "covQual"
with d = 1
qualitative
input.
Correlation kernels are needed in tensor products because the tensor product of two covariance kernels each with unknown variance would not be identifiable.
The corLevSymm
function used to compute the
correlation matrix and its gradients w.r.t. the correlation
parameters.
School <- factor(1L:3L, labels = c("Bad", "Mean" , "Good"))
myCor <- q1Symm(School, input = "School")
coef(myCor) <- c(theta_2_1 = pi / 3, theta_3_1 = pi / 4, theta_3_2 = pi / 8)
plot(myCor, type = "cor")
## Use a data.frame with a factor
set.seed(246)
newSchool <- factor(sample(1L:3L, size = 20, replace = TRUE),
labels = c("Bad", "Mean" , "Good"))
C1 <- covMat(myCor, X = data.frame(School = newSchool),
compGrad = FALSE, lowerSQRT = FALSE)
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