summary.invGauss {invGauss}

Summarize the estimation result from invGauss

Description

To be used on a result from invGauss. The summary function provides estimates, standard errors etc.

Usage

## S3 method for class 'invGauss'
summary(object, covariance = FALSE, ...) 

Arguments

object

Result from running invGauss.

covariance

Logical, indicates whether the asymptotic variance-covariance matrix for the parameter estimates should be returned.

...

Other arguments (ignored).

Details

See web page http://www.uib.no/smis/gjessing/projects/invgauss/ for more details.

Value

Returns a list with the most important results from invGauss, including coefficients with standard errors and Wald tests:

coefficients:

Estimated coefficients, with standard errors and Wald tests

cov.unscaled:

The variance-covariance matrix of the estimated coefficients

loglik:

The maximized log-likelihood

AIC:

AIC value

Note

Further information is found on the web page

Author(s)

Hakon K. Gjessing
Professor of Biostatistics
Division of Epidemiology
Norwegian Institute of Public Health
[e-mail]
http://www.uib.no/smis/gjessing

References

Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis: A Process Point of View. Springer-Verlag, 2008.
Aalen OO and Gjessing HK. Understanding the Shape of the Hazard Rate: A Process Point of View. Statistical Science, 2001, Vol. 1, No. 1, 1-22.
Aalen OO. Phase type distributions in survival analysis. Scandinavian Journal of Statistics, 1995, Vol. 22, Issue 4, 447-463.
Web Site: http://www.uib.no/smis/gjessing/projects/invgauss/

See Also

invGauss

Examples

# Simple run:
data(d.oropha.rec)
res <- invGauss(formula.mu = Surv(time, status) ~ 1, data = d.oropha.rec)
summary(res)

# Use covariates for c, with exponential link function
data(d.oropha.rec)
res <- invGauss(formula.mu = Surv(time, status) ~ 1, formula.c = ~ cond + nstage + tstage, 
data = d.oropha.rec) # MODEL 5 (TABLE 10.2) IN SPRINGER BOOK
summary(res)