Collects the fitted coefficients, the fit measures and a summary of the
residuals into a compact object for printing, and rounds them to
digits. The fit measures cover the fit window (the ages fitted and
the ages reported), the optimisation (method used, optimiser outcome), the
deviance with its degrees of freedom and dispersion, and the
R-squared and RMSE of the fit. For a likelihood-based fit the maximised
log-likelihood with its information criteria is included. When more than
four curves were fitted only the first and the last two are kept in the
printed coefficients and fit measures, so the output fits on one screen.
Value
An object of class "summary.MortalityLaw", a list holding
the model information, the matched call, the rounded coefficients, the
goodness-of-fit measures, the deviance and the degrees of freedom, the
R-squared and RMSE of the fit, the optimisation outcome, the fit window
and the residual summaries on the raw and the deviance scale.
See also
MortalityLaw to fit a law;
coef and fitted for the extracted values.
Examples
x <- 45:75
M1 <- MortalityLaw(x = x, Dx = ahmd$Dx[as.character(x), "1950"],
Ex = ahmd$Ex[as.character(x), "1950"], law = "makeham")
summary(M1)
#> Makeham model: mu[x] = A exp[Bx] + C
#> Fitted values: mx | ages 45-75 | fitted on 45-75 (31 of 31 ages)
#>
#> Call:
#> MortalityLaw(x = x, Dx = ahmd$Dx[as.character(x), "1950"], Ex = ahmd$Ex[as.character(x),
#> "1950"], law = "makeham")
#>
#> Coefficients:
#> estimate
#> A 0.0019
#> B 0.1127
#> C 0.0017
#>
#> Fit:
#> method LF2 | optimiser converged in 21 iterations
#> deviance 117.3 on 28 degrees of freedom | dispersion 4.18
#> R-squared 0.9977 | RMSE 0.000815
#>
#> Residuals:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> raw -0.0019 -0.0002 0e+00 0.0001 0.0003 0.0032
#> deviance -4.3099 -1.1418 -9e-04 0.0204 1.3164 4.5737