Given suitable data (i.e. data with all columns used for estimation of the model),
this functions adds a column surv_prob containing survival probabilities
for the specified covariate and follow-up information (and CIs
surv_lower, surv_upper if ci=TRUE).
add_surv_prob(
newdata,
object,
ci = TRUE,
se_mult = 2,
overwrite = FALSE,
time_var = NULL,
interval_length = "intlen",
...
)A data frame or list containing the values of the model covariates at which predictions
are required. If this is not provided then predictions corresponding to the
original data are returned. If newdata is provided then
it should contain all the variables needed for prediction: a
warning is generated if not. See details for use with link{linear.functional.terms}.
a fitted gam object as produced by gam().
logical. Indicates if confidence intervals should be
calculated. Defaults to TRUE.
Factor by which standard errors are multiplied for calculating the confidence intervals.
Should hazard columns be overwritten if already present in
the data set? Defaults to FALSE. If TRUE, columns with names
c("hazard", "se", "lower", "upper") will be overwritten.
Name of the variable used for the baseline hazard. If
not given, defaults to "tend" for gam fits, else
"interval". The latter is assumed to be a factor, the former
numeric.
The variable in newdata containing the interval lengths.
Can be either bare unquoted variable name or character. Defaults to "intlen".
Further arguments passed to predict.gam and
get_hazard
predict.gam,
add_surv_prob
ped <- tumor[1:50,] %>% as_ped(Surv(days, status)~ age)
pam <- mgcv::gam(ped_status ~ s(tend)+age, data=ped, family=poisson(), offset=offset)
ped_info(ped) %>% add_surv_prob(pam, ci=TRUE)
#> # A tibble: 22 × 9
#> tstart tend intlen intmid interval age surv_prob surv_upper surv_lower
#> <dbl> <dbl> <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 0 27 27 13.5 (0,27] 59.7 0.977 0.989 0.950
#> 2 27 33 6 30 (27,33] 59.7 0.972 0.987 0.939
#> 3 33 55 22 44 (33,55] 59.7 0.954 0.978 0.904
#> 4 55 62 7 58.5 (55,62] 59.7 0.949 0.976 0.893
#> 5 62 139 77 100. (62,139] 59.7 0.896 0.946 0.803
#> 6 139 209 70 174 (139,209] 59.7 0.854 0.920 0.739
#> 7 209 214 5 212. (209,214] 59.7 0.851 0.919 0.734
#> 8 214 257 43 236. (214,257] 59.7 0.828 0.904 0.700
#> 9 257 304 47 280. (257,304] 59.7 0.805 0.889 0.667
#> 10 304 308 4 306 (304,308] 59.7 0.803 0.888 0.665
#> # ℹ 12 more rows