Distance based regression models for proportions: Distance based regression models for proportions
Description
Distance based regression models for proportions.
Usage
ols.prop.reg(y, x, cov = FALSE, tol = 1e-07, maxiters = 100)
helling.prop.reg(y, x, tol = 1e-07, maxiters = 100)
Arguments
y
A numerical vector proportions. 0s and 1s are allowed.
x
A matrix or a data frame with the predictor variables.
cov
Should the covariance matrix be returned? TRUE or FALSE.
tol
The tolerance value to terminate the Newton-Raphson algorithm. This is set to \(10^{-9}\) by default.
maxiters
The maximum number of iterations before the Newton-Raphson is terminated automatically.
Value
A list including:
sse
The sum of squres of errors for the "ols.prop.reg" function.
be
The estimated regression coefficients.
seb
The standard error of the regression coefficients if "cov" is TRUE.
covb
The covariance matrix of the regression coefficients in "ols.prop.reg" if "cov" is TRUE.
H
The Hellinger distance between the true and the obseervd proportions in "helling.prop.reg".
iters
The number of iterations required by the Newton-Raphson.
Details
We are using the Newton-Raphson, but unlike R's built-in function "glm" we do no checks and no extra calculations, or whatever. Simply the model. The functions accept binary responses as well (0 or 1).
References
Papke L. E. & Wooldridge J. (1996). Econometric methods for fractional response variables with
an application to 401(K) plan participation rates. Journal of Applied Econometrics, 11(6): 619--632.
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.