Description Usage Arguments Value Note See Also Examples
Qualitative correlation or covariance kernel with one input and compound symmetric correlation.
1 | q1CompSymm(factor, input = "x", cov = c("corr", "homo"), 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 kernel is a correlation kernel or a homoscedastic covariance kernel. |
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 corLevCompSymm
function used to compute
the correlation matrix and its gradients w.r.t. the correlation
parameters.
1 2 3 4 5 6 7 8 9 10 11 | School <- factor(1L:3L, labels = c("Bad", "Mean" , "Good"))
myCor <- q1CompSymm(School, input = "School")
coef(myCor) <- 0.26
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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