Utility Functions: Gradient and Jacobian
Utility functions for computing the gradient of a scalar-valued function or the Jacobian of a vector-valued function by numerical approximation.
lav_func_gradient_complex(func, x, h = .Machine$double.eps, ...,
fallback.simple = TRUE)
lav_func_jacobian_complex(func, x, h = .Machine$double.eps, ...,
fallback.simple = TRUE)
lav_func_gradient_simple(func, x, h = sqrt(.Machine$double.eps), ...)
lav_func_jacobian_simple(func, x, h = sqrt(.Machine$double.eps), ...)func |
A real-valued function returning a numeric scalar or a numeric vector. |
x |
A numeric vector: the point(s) at which the gradient/Jacobian of the function should be computed. |
h |
Numeric value representing a small change in ‘x’ when computing the gradient/Jacobian. |
... |
Additional arguments to be passed to the function ‘func’. |
fallback.simple |
Logical. If TRUE, and the function evaluation fails, we call the corresponding simple (non-complex) method instead. |
The complex versions use complex numbers to gain more precision, while retaining the simplicity (and speed) of the simple forward method (see references). These functions were added to lavaan (around 2012) when the complex functionality was not part of the numDeriv package. They were used internally, and made public in 0.5-17 per request of other package developers.
Squire, W. and Trapp, G. (1998). Using Complex Variables to Estimate Derivatives of Real Functions. SIAM Review, 40(1), 110-112.
# very accurate complex method lav_func_gradient_complex(func = exp, x = 1) - exp(1) # less accurate forward method lav_func_gradient_simple(func = exp, x = 1) - exp(1) # very accurate complex method diag(lav_func_jacobian_complex(func = exp, x = c(1,2,3))) - exp(c(1,2,3)) # less accurate forward method diag(lav_func_jacobian_simple(func = exp, x = c(1,2,3))) - exp(c(1,2,3))
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