`summary`

methods for class `flexrsurv`

.
Produces and prints summaries of the results of a fitted Relative Survival Model

1 2 3 4 5 6 7 |

`object` |
an object of class "flexrsurv", usually, a result of a call to |

`x` |
an object of class |

`correlation` |
logical; if |

`symbolic.cor` |
logical. If |

`digits` |
the number of significant digits to use when printing. |

`signif.stars` |
logical. If TRUE,'significance stars' are printed for each coefficient. |

`...` |
further arguments passed to or from other methods. |

`print.summary.glm`

tries to be smart about formatting the coefficients, standard errors, etc.
and additionally gives ‘significance stars’ if `signif.stars`

is `TRUE`

.

Correlations are printed to two decimal places (or symbolically): to see the actual correlations
print `summary(object)$correlation`

directly.

The dispersion of a GLM is not used in the fitting process, but it is needed to find standard errors. If dispersion is not supplied or NULL, the dispersion is taken as 1 for the binomial and Poisson families, and otherwise estimated by the residual Chisquared statistic (calculated from cases with non-zero weights) divided by the residual degrees of freedom.

The function summary.flexrsurv computes and returns a list of summary statistics of the fitted flexible relative survival model given in `object`

.
The returned value is an object of class "`summary.flexrsurv`

", which a list with components:

`call` |
the " |

`terms` |
the " |

`coefficients` |
the matrix of coefficients, standard errors, z-values and p-values. |

`cov` |
the estimated covariance matrix of the estimated coefficients. |

`correlation` |
(only if |

`symbolic.cor` |
(only if |

`loglik` |
the " |

`df.residual` |
the " |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
## Not run:
# data from package relsurv
data(rdata, package="relsurv")
# rate table from package relsurv
data(slopop, package="relsurv")
# get the death rate from slopop for rdata
rdata$iage <- findInterval(rdata$age*365.24, attr(slopop, "cutpoints")[[1]])
rdata$iyear <- findInterval(rdata$year, attr(slopop, "cutpoints")[[2]])
therate <- rep(-1, dim(rdata)[1])
for( i in 1:dim(rdata)[1]){
therate[i] <- slopop[rdata$iage[i], rdata$iyear[i], rdata$sex[i]]
}
rdata$slorate <- therate
# change sex coding
rdata$sex01 <- rdata$sex -1
# fit a relative survival model with a non linear effetc of age
fit <- flexrsurv(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3),
rate=slorate, data=rdata,
knots.Bh=1850, # one interior knot at 5 years
degree.Bh=3,
Spline = "b-spline",
initbyglm=TRUE,
initbands=seq(from=0, to=5400, by=200)
int_meth= "CAV_SIM",
step=50
)
summary(fit)
## End(Not run)
``` |

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