Print a summary information of the fitted model.
For regression coefficients the following information is given:
| Value | - | estimate of the coefficient |
| Std.Error | - | estimated standard error based on the pseudo-variance estimate (3.1) |
| in Kom<U+00E1>rek, Lesaffre and Hilton (2005) | ||
| Std.Error2 | - | estimated standard error based on the asymptotic variance estimate (3.2) |
| in Kom<U+00E1>rek, 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:
| Lambda | - | the optimal value of the smoothing hyperparameter |
| divided by the sample size, i.e., \(\lambda/n\) in the notation | ||
| of Kom<U+00E1>rek, Lesaffre and Hilton (2005) | ||
| Log(Lambda) | - | logarithm of the above |
| df | - | effective degrees of freedom of the model, see Section 2.2.3 |
| of Kom<U+00E1>rek, Lesaffre and Hilton (2005) | ||
| AIC | - | Akaike's information criterion of the model, see Section 2.2.3 |
| of Kom<U+00E1>rek, 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).
# S3 method for smoothSurvReg
print(x, spline, digits = min(options()$digits, 4), …)
# S3 method for smoothSurvReg
summary(object, spline, digits = min(options()$digits, 4), …)Object of class smoothSurvReg.
Object of class smoothSurvReg.
TRUE/FALSE. If TRUE an information on fitted G-spline is printed.
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.
Kom<U+00E1>rek, 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.
Lesaffre, E., Kom<U+00E1>rek, 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.