jac.invlogit: Jacobian for logit transform
In jmhewitt/bisque: Approximate Bayesian Inference via Sparse Grid Quadrature Evaluation (BISQuE) for Hierarchical Models
Description
Usage
Arguments
Examples
Description
Let X=logit^{-1}(Y) be a transformation of a random variable Y.
This function computes the jacobian J(x) when using the density of
Y to evaluate the density of X via
f(x) = f_y(logit(x)) J(x)
where
J(x) = d/dx logit(x).
Usage
1
jac.invlogit(x, log = TRUE)
Arguments
x
value at which to evaluate J(x)
log
TRUE to return log(J(x))
Examples
1
jac.invlogit(1)
jmhewitt/bisque documentation built on Feb. 9, 2020, 2:36 a.m.
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Description Usage Arguments Examples
Description
Let X=logit^{-1}(Y) be a transformation of a random variable Y. This function computes the jacobian J(x) when using the density of Y to evaluate the density of X via
f(x) = f_y(logit(x)) J(x)
where
J(x) = d/dx logit(x).
Usage
1 | jac.invlogit(x, log = TRUE)
|
Arguments
x |
value at which to evaluate J(x) |
log |
TRUE to return log(J(x)) |
Examples
1 | jac.invlogit(1)
|
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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