aicc_full_single_cpp: Temp-function for computing the full AICc with several X's...
In shapr: Prediction Explanation with Dependence-Aware Shapley Values
aicc_full_single_cpp R Documentation
Temp-function for computing the full AICc with several X's etc
Description
Temp-function for computing the full AICc with several X's etc
Usage
aicc_full_single_cpp(X, mcov, S_scale_dist, h, y)
Arguments
X
matrix with "covariates"
mcov
covariance matrix
S_scale_dist
logical indicating whether the Mahalanobis distance should be scaled with the number of variables
h
numeric specifying the scaling (sigma)
y
vector with the "response variable"
Value
Scalar with the numeric value of the AICc formula
Author(s)
Martin Jullum
shapr documentation built on May 4, 2023, 5:10 p.m.
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| aicc_full_single_cpp | R Documentation |
Temp-function for computing the full AICc with several X's etc
Description
Temp-function for computing the full AICc with several X's etc
Usage
aicc_full_single_cpp(X, mcov, S_scale_dist, h, y)
Arguments
X |
matrix with "covariates" |
mcov |
covariance matrix |
S_scale_dist |
logical indicating whether the Mahalanobis distance should be scaled with the number of variables |
h |
numeric specifying the scaling (sigma) |
y |
vector with the "response variable" |
Value
Scalar with the numeric value of the AICc formula
Author(s)
Martin Jullum
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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