pammtools (version 0.1.9)

add_hazard: Add predicted (cumulative) hazard to data set

Description

Add (cumulative) hazard based on the provided data set and model. If ci=TRUE confidence intervals are also added. Their width can be controlled via the se_mult argument. This is a wrapper around predict.gam.

Usage

add_hazard(newdata, object, type = c("response", "link"), ci = TRUE,
  se_mult = 2, ci_type = c("default", "delta", "sim"),
  overwrite = FALSE, time_var = NULL, ...)
add_cumu_hazard(newdata, object, ci = TRUE, se_mult = 2,
  overwrite = FALSE, time_var = NULL, interval_length = "intlen",
  ...)

Arguments

newdata

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}.

object

a fitted gam object as produced by gam().

type

When this has the value "link" (default) the linear predictor (possibly with associated standard errors) is returned. When type="terms" each component of the linear predictor is returned seperately (possibly with standard errors): this includes parametric model components, followed by each smooth component, but excludes any offset and any intercept. type="iterms" is the same, except that any standard errors returned for smooth components will include the uncertainty about the intercept/overall mean. When type="response" predictions on the scale of the response are returned (possibly with approximate standard errors). When type="lpmatrix" then a matrix is returned which yields the values of the linear predictor (minus any offset) when postmultiplied by the parameter vector (in this case se.fit is ignored). The latter option is most useful for getting variance estimates for quantities derived from the model: for example integrated quantities, or derivatives of smooths. A linear predictor matrix can also be used to implement approximate prediction outside R (see example code, below).

ci

Logical indicating whether to include confidence intervals. Defaults to TRUE

se_mult

Factor by which standard errors are multiplied for calculating the confidence intervals.

ci_type

The method by which standard errors/confidence intervals will be calculated. Default transforms the linear predictor at respective intervals. "delta" calculates CIs based on the standard error calculated by the Delta method. "sim" draws the property of interest from its posterior based on the normal distribution of the estimated coefficients. CIs are given by respective quantiles.

overwrite

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.

time_var

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.

...

Further arguments passed to predict.gam and get_hazard

interval_length

The variable in newdata containing the interval lengths. Can be either bare unquoted variable name or character. Defaults to "intlen".

See Also

predict.gam, add_surv_prob

Examples

# NOT RUN {
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_hazard(pam, type="link")
ped_info(ped) %>% add_hazard(pam, type = "response")
ped_info(ped) %>% add_cumu_hazard(pam)
# }