glmgam.fit {statmod} | R Documentation |
Estimates a gamma generalized linear model with identity link using Fisher scoring with Levenberg damping.
glmgam.fit(X, y, start=NULL, tol=1e-6, maxit=50, trace=FALSE)
X |
design matrix, assumed to be of full column rank. Missing values not allowed. |
y |
numeric vector of responses. Negative or missing values not allowed. |
start |
numeric vector of starting values for the regression coefficients |
tol |
small positive numeric value giving convergence tolerance |
maxit |
maximum number of iterations allowed |
trace |
logical value. If TRUE then output diagnostic information at each iteration. |
This function is similar to glm.fit(X,y,family=Gamma(link="identity"))
but has more secure convergence.
This function is used by randomizedBlockFit
.
List with the following components:
coefficients |
numeric vector of regression coefficients |
fitted |
numeric vector of fitted values |
deviance |
residual deviance |
maxit |
input maximum number of iterations |
iter |
number of iterations used to convergence. If convergence was not achieved then iter is set to maxit+1 . |
y <- rgamma(10,shape=5) X <- cbind(1,1:10) fit <- glmgam.fit(X,y,trace=TRUE)