predict-methods: Predicted values for an stpm2 or pstpm2 fit

In rstpm2: Smooth Survival Models, Including Generalized Survival Models

Predicted values for an stpm2 or pstpm2 fit

Description

Given an stpm2 fit and an optional list of new data, return predictions

Usage

    ## S4 method for signature 'stpm2'
predict(object, newdata=NULL,
               type=c("surv","cumhaz","hazard","density","hr","sdiff",
               "hdiff","loghazard","link","meansurv","meansurvdiff","meanhr",
               "odds","or","margsurv","marghaz","marghr","meanhaz","af",
               "fail","margfail","meanmargsurv","uncured","rmst","probcure",
               "lpmatrix", "gradh", "gradH","rmstdiff","lpmatrixD"),
               grid=FALSE,seqLength=300,
               type.relsurv=c("excess","total","other"), scale=365.24,
               rmap, ratetable=survival::survexp.us,
               se.fit=FALSE,link=NULL,exposed=NULL,var=NULL,
               keep.attributes=FALSE, use.gr=TRUE,level=0.95,
               n.gauss.quad=100,full=FALSE,...)
    ## S4 method for signature 'pstpm2'
predict(object, newdata=NULL,
               type=c("surv","cumhaz","hazard","density","hr","sdiff",
               "hdiff","loghazard","link","meansurv","meansurvdiff","meanhr",
               "odds","or","margsurv","marghaz","marghr","meanhaz","af",
               "fail","margfail","meanmargsurv","rmst","lpmatrix",
               "gradh", "gradH","rmstdiff","lpmatrixD"),
               grid=FALSE,seqLength=300,
               se.fit=FALSE,link=NULL,exposed=NULL,var=NULL,
               keep.attributes=FALSE, use.gr=TRUE,level=0.95,
               n.gauss.quad=100,full=FALSE,...)

Arguments

object	an stpm2 or pstpm2 object
newdata	optional list of new data (required if type in ("hr","sdiff","hdiff","meansurvdiff","or","uncured")). For type in ("hr","sdiff","hdiff","meansurvdiff","or","af","uncured"), this defines the unexposed newdata. This can be combined with grid to get a regular set of event times (i.e. newdata would not include the event times).
type	specify the type of prediction: "surv" survival probabilities "cumhaz" cumulative hazard "hazard" hazard "density" density "hr" hazard ratio "sdiff" survival difference "hdiff" hazard difference "loghazard" log hazards "meansurv" mean survival "meansurvdiff" mean survival difference "odds" odds "or" odds ratio "margsurv" marginal (population) survival "marghaz" marginal (population) hazard "marghr" marginal (population) hazard ratio "meanhaz" mean hazard "meanhr" mean hazard ratio "af" attributable fraction "fail" failure (=1-survival) "margfail" marginal failure (=1-marginal survival) "meanmargsurv" mean marginal survival, averaged over the frailty distribution "uncured" distribution for the uncured "rmst" restricted mean survival time "rmstdiff" restricted mean survival time difference "probcure" probability of cure "lpmatrix" design matrix "lpmatrixD" design matrix for the derivative with respect to time
grid	whether to merge newdata with a regular sequence of event times (default=FALSE)
seqLength	length of the sequence used when grid=TRUE
type.relsurv	type of predictions for relative survival models: either "excess", "total" or "other"
scale	scale to go from the days in the ratetable object to the analysis time used in the analysis
rmap	an optional list that maps data set names to the ratetable names. See survexp
ratetable	a table of event rates used in relative survival when type.relsurv is "total" or "other"
se.fit	whether to calculate confidence intervals (default=FALSE)
link	allows a different link for the confidence interval calculation (default=NULL, such that switch(type,surv="cloglog",cumhaz="log",hazard="log",hr="log",sdiff="I", hdiff="I",loghazard="I",link="I",odds="log",or="log",margsurv="cloglog", marghaz="log",marghr="log"))
exposed	a function that takes newdata and returns a transformed data-frame for those exposed or the counterfactual. By default, this increments var (except for cure models, where it defaults to the last event time).
var	specify the variable name or names for the exposed/unexposed (names are given as characters)
keep.attributes	Boolean to determine whether the output should include the newdata as an attribute (default=TRUE)
use.gr	Boolean to determine whether to use gradients in the variance calculations when they are available (default=TRUE)
level	confidence level for the confidence intervals (default=0.95)
n.gauss.quad	number of Gauassian quadrature points used for integrations (default=100)
full	logical for whether to return a full data-frame with predictions and newdata combined. Useful for lattice and ggplot2 plots. (default=FALSE)
...	additional arguments (for generic compatibility)

Details

The confidence interval estimation is based on the delta method using numerical differentiation.

Value

A data-frame with components Estimate, lower and upper, with an attribute "newdata" for the newdata data-frame.

Methods

object= "stpm2"

an stpm2 fit

See Also

stpm2

rstpm2 documentation built on Oct. 29, 2024, 5:06 p.m.