Compositional (version 5.0)

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.

See Also

propreg, beta.reg

Examples

# NOT RUN {
y <- rbeta(100, 1, 4)
x <- matrix(rnorm(100 * 2), ncol = 2)
a1 <- ols.prop.reg(y, x)
a2 <- helling.prop.reg(y, x)
# }