Description Usage Arguments Details
exp_flexpredict returns the conditional mean E(Y|X) of a model fitted via the function exp_flexfit; where λ has been specified to be a function of covariates the required value should be specified using the ‘features’ parameter. exp_flexpredict also allows for the correlation of estimated parameters via the Cholesky decomposition of the variance-covariance matrix.
1 | exp_flexpredict(model, features, draws = 5)
|
model |
An object of class "mle2" produced using the function exp_flexfit. |
features |
A numeric vector specifying the value of covriates at which the conditional mean should be evaluated; the covariates in the vector should appear in the same order as they do in the model. Where a model does not depend on covariates the argument may be left blank. |
draws |
The number of random draws from multivariate random normal representing correlated parameters. If parameter correlation is not required draws should be set to zero. |
This function uses the most common parametrization of the Exponential distribution. The probability probability density function is used is:
f(y) = λexp(-λy)
The function returns:
E(Y|X) = λ^-1
λ may be a function of covariates; in which case, the canonical log link function is used.
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