smoothSurv (version 0.4)

print.smoothSurvReg: Summary and Print for Objects of Class 'smoothSurvReg'

Description

Print a summary information of the fitted model.

For regression coefficients the following information is given: lll{ Value - estimate of the coefficient Std.Error - estimated standard error based on the pseudo-variance estimate (3.1) in $\mbox{Kom\'arek, Lesaffre and Hilton (2005)}$ Std.Error2 - estimated standard error based on the asymptotic variance estimate (3.2) in $\mbox{Kom\'arek, Lesaffre and Hilton (2005)}$ Z - the Wald statistic obtained as Value divided by Std.Error Z2 - the Wald statistic obtained as Value divided by Std.Error2 p - the two-sided P-value based on normality of the statistic Z p2 - the two-sided P-value based on normality of the statistic Z2 }

Further, we print: lll{ Lambda - the optimal value of the smoothing hyperparameter divided by the sample size, i.e., $\lambda/n$ in the notation of $\mbox{Kom\'arek, Lesaffre and Hilton (2005)}$ Log(Lambda) - logarithm of the above df - effective degrees of freedom of the model, see Section 2.2.3 of $\mbox{Kom\'arek, Lesaffre and Hilton (2005)}$ AIC - Akaike's information criterion of the model, see Section 2.2.3 of $\mbox{Kom\'arek, Lesaffre and Hilton (2005)}$ }

With argument spline set to TRUE, analogous table like that for the regression coefficients is printed also for the weights of the penalized Gaussian mixture (G-spline).

Usage

## S3 method for class 'smoothSurvReg':
print(x, spline, digits = min(options()$digits, 4), ...)
## S3 method for class 'smoothSurvReg':
summary(object, spline, digits = min(options()$digits, 4), ...)

Arguments

x

Object of class smoothSurvReg.

object

Object of class smoothSurvReg.

spline

TRUE/FALSE. If TRUE an information on fitted G-spline is printed.

digits

Controls the number of digits to print when printing numeric values. It is a suggestion only. Valid values are 1...22.

...

Further arguments passed to or from other methods.

References

$\mbox{Kom\'arek, A., Lesaffre, E., and Hilton, J. F. (2005).}$ Accelerated failure time model for arbitrarily censored data with smoothed error distribution. Journal of Computational and Graphical Statistics, 14, 726--745.

$\mbox{Lesaffre, E., Kom\'arek, A., and Declerck, D. (2005).}$ An overview of methods for interval-censored data with an emphasis on applications in dentistry. Statistical Methods in Medical Research, 14, 539--552.

See Also

smoothSurvReg, print, summary