Inverse Gaussian regression with a log-link {Rfast}

Inverese Gaussian regression with a log-link

Description

Inverse Gaussian regression with a log-link.

Usage

invgauss.reg(y, x, tol = 1e-07, maxiters = 100)

Arguments

y

The dependent variable, a numerical variable with non negative numbers.

x

A matrix or data.frame with the indendent variables.

tol

The tolerance value to terminate the Newton-Raphson algorithm.

maxiters

The maximum number of iterations that can take place in the regression.

Details

An inverse Gaussian regression with a log-link is fitted.

Value

A list including:

i

The number of iterations required by the Newton-Raphson

loglik

The log-likelihood value.

deviance

The deviance value.

phi

The dispersion parameter (\phi) of the regression. This is necessary if you want to perform an F hypothesis test for the significance of one or more independent variables.

be

The regression coefficients

Author(s)

Michail Tsagris

R implementation and documentation: Stefanos Fafalios <[e-mail]>

References

McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.

Zakariya Yahya Algamal and Intisar Ibrahim Allyas (2017). Prediction of blood lead level in maternal and fetal using generalized linear model. International Journal of Advanced Statistics and Probability, 5(2): 65-69.

See Also

invgauss.regs, normlog.reg, score.glms

Examples

y <- abs( rnorm(100) )
x <- matrix( rnorm(100 * 2), ncol = 2)
a <- invgauss.reg(y, x)
a