mae_from_kernel_weights_and_centers: Compute "mean" absolute error for one time point from...
In reichlab/kcde: Kernel conditional density estimation with flexible kernel specifications
Description
Usage
Arguments
Value
Description
Compute "mean" absolute error for one time point from prediction in the form
of kernel weights and centers.
Usage
1
mae_from_kernel_weights_and_centers(kernel_weights_and_centers, obs)
Arguments
kernel_weights_and_centers
a named list with two components: weights
is a vector of kernel weights, and centers is a vector of kernel centers
obs
is a numeric with length 1 containign the observed value for one
time point
Value
abs(obs - prediction) where prediction is the weighted mean of the
kernel centers.
reichlab/kcde documentation built on May 27, 2019, 4:53 a.m.
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Description Usage Arguments Value
Description
Compute "mean" absolute error for one time point from prediction in the form of kernel weights and centers.
Usage
1 | mae_from_kernel_weights_and_centers(kernel_weights_and_centers, obs)
|
Arguments
kernel_weights_and_centers |
a named list with two components: weights is a vector of kernel weights, and centers is a vector of kernel centers |
obs |
is a numeric with length 1 containign the observed value for one time point |
Value
abs(obs - prediction) where prediction is the weighted mean of the kernel centers.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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