checkGrad: Check the Gradient Provided in a 'covMan' Object
In kergp: Gaussian Process Laboratory

Description Usage Arguments Details Value Caution Note Author(s)

Check the gradient provided in a covMan object.

checkGrad(object, sym = TRUE,
          x1 = NULL, n1 = 10,
          x2 = NULL, n2 = NULL,
          XLower = NULL, XUpper = NULL,
          plot = TRUE)

`object`	A `covMan` object.
`sym`	Logical. If `TRUE`, the check is done assuming that `x2` is identical to `x1`, so the provided values for `x2` and `n2` (if any) will be ignored.
`x1`	Matrix to be used as the first argument of the kernel.
`n1`	Number of rows for the matrix `x1`. Used only when `x1` is not provided.
`x2`	Matrix to be used as the second argument of the kernel.
`n2`	Number of rows for the matrix `x2`. Used only when `x2` is not provided.
`XLower`	Vector of lower bounds to draw `x1` and `x2` when needed.
`XUpper`	Vector of upper bounds to draw `x1` and `x2` when needed.
`plot`	`Logical`. If `TRUE`, a plot is shown comparing the two arrays of gradients.

Each of the two matrices x1 and x2 with n1 and n2 rows can be given or instead be drawn at random. The matrix of kernel values with dimension c(n1, n2) is computed, together with its gradient with dimension c(n1, n2, npar) where npar is the number of parameters of the kernel. A numerical differentiation w.r.t. the kernel parameters is performed for the kernel value at x1 and x2, and the result is compared to that provided by the kernel function (the function described in the slot named "kernel" of object). Note that the value of the parameter vector is the value provided by coef(object) and it can be changed by using the replacement method `coef<-` if needed.

A list of results related to the Jacobians

test Max of the absolute difference between the gradient obtained by numeric differentiation and the gradient provided by the kernel object.
Jnum, J Jacobians (arrays) computed with numDeriv::jacobian and provided by the kernel object.
x1, x2, K The matrices used for the check, and the matrix of kernel values with dimension c(n1, n2). The element x2 can be NULL if the determination of the matrix x2 was not necessary.