Description Usage Arguments Details References
View source: R/lnorm_predict.R
lnorm_flexpredict returns the conditional mean E(Y|X) of a model fitted via the function lnorm_flexfit; where μ has been specified to be a function of covariates the required value should be specified using the ‘features’ parameter. lnorm_flexpredict also allows for the correlation of estimated parameters via the Cholesky decomposition of the variance-covariance matrix.
1 | lnorm_flexpredict(model, features, draws = 5)
|
model |
An object of class "mle2" produced using the function lnorm_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 two parameter parametrization of the Log-Normal distribution. The probability probability density function is used is:
f(y) = [yσ(2π)^1/2]^-1 exp(-log(y-μ)^2/(2σ^2))
The function returns:
E(Y|X) = exp(μ + 0.5σ^2)
μ may be a function of covariates; in which case, the identity link function is used.
Faith Ginos, Brenda. "Parameter Estimation For The Lognormal Distribution." Brigham Young University Scholars Archive (2018): 1-111. Web. 10 Aug. 2018.
Kleiber, Christian, and Samuel Kotz. Statistical Size Distributions In Economics And Actuarial Sciences. pp. 107-147. John Wiley & Sons, 2003. Print.
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