Description Usage Arguments Details References
View source: R/sinmad_predict.R
sinmad_flexpredict returns the conditional mean E(Y|X) of a model fitted via the function sinmad_flexfit; where b has been specified to be a function of covariates the required value should be specified using the ‘features’ parameter. sinmad_flexpredict also allows for the correlation of estimated parameters via the Cholesky decomposition of the variance-covariance matrix.
1 | sinmad_flexpredict(model, features, draws = 5)
|
model |
An object of class "mle2" produced using the function sinmad_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 same parametrization of the Singh-Maddala distribution as is used in Kleiber and Kotz (2003). The probability probability density function is used is:
f(y) = aqy^a-1/[b^a(1+(y/b)^a)^1+q]
The function returns:
E(Y|X) = bΓ(1+1/a)Γ(q-1/a)/Γ(q)
b may be a function of covariates; in which case, the cannonical log link function is used.
Kleiber, Christian, and Samuel Kotz. Statistical Size Distributions In Economics And Actuarial Sciences. pp. 107-147. John Wiley & Sons, 2003.
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