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, ...)# S3 method for default
add_surv_prob(
newdata,
object,
ci = TRUE,
se_mult = 2,
overwrite = FALSE,
time_var = NULL,
interval_length = "intlen",
boundary = TRUE,
check_grouping = TRUE,
...
)
# S3 method for pamm_ic
add_surv_prob(
newdata,
object,
ci = TRUE,
alpha = 0.05,
nsim = 500L,
time_var = NULL,
interval_length = "intlen",
check_grouping = TRUE,
...
)
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().
Further arguments passed to predict.gam and
get_hazard
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. Defaults
to "tend".
The variable in newdata containing the interval lengths.
Can be either bare unquoted variable name or character. Defaults to "intlen".
Logical. If TRUE (default), a boundary row at
time = 0 with cumulative hazard 0 is prepended (per group),
so that cumulative hazards start at the natural origin (consistent with
add_surv_prob, add_cif and
add_trans_prob).
Logical. If TRUE (default), the function checks
that newdata is grouped so that the time variable is unique within
each group and stops otherwise, guarding against
silently accumulating cumulative quantities across distinct covariate
profiles (a forgotten group_by()). Set to FALSE to skip the
check (e.g.\ for internal calls on already-validated data).
Significance level for pooled confidence intervals.
Total number of pooled posterior draws used for the interval.
When computing cumulative hazards or survival probabilities across groups,
the input data must be grouped via group_by() prior to calling
add_cumu_hazard() or add_surv_prob(), so that the cumulative
quantity is accumulated within each covariate profile rather than across the
whole dataset. If newdata still contains several profiles per group
(i.e.\ repeated time_var values within a group, typically a forgotten
group_by()), the functions now stop with an error rather than
returning silently incorrect results. Set check_grouping = FALSE to
skip this safeguard. Note the check detects the common mis-grouping
cases -- in particular any grid built with make_newdata, where
profiles share time values -- but cannot catch hand-built grids that stack
profiles with disjoint time grids, as these are indistinguishable from a
single profile with time-varying covariates.
See the workflow vignette
for a worked example.
The returned data contains one boundary row per group at time_var = 0
for plotting cumulative quantities from the time origin. On this row,
surv_prob = 1; if confidence intervals are requested,
surv_lower = surv_upper = 1. If an interval-length column is present,
it is set to 0 on the boundary row.
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)
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