predict.CopulaCenR {CopulaCenR}R Documentation

Predictions from CopulaCenR regression models


Predictions for new observations based on ic_spTran_copula, rc_spCox_copula, ic_par_copula and rc_par_copula.


## S3 method for class 'CopulaCenR'
predict(object, newdata, type = "lp", ...)



a CopulaCenR object from ic_spTran_copula, rc_spCox_copula, ic_par_copula and rc_par_copula


a data frame (see details)


"lp" for linear predictors or "survival" for marginal and joint survival probabilities


further arguments


For the newdata, when type = "survival", it must be a data frame with columns id (subject id), ind (1,2 for two margins), time (to be evaluted) and covariates; when type = "lp", the newdata needs to have id, ind and covariates, but time is not needed.

When the argument type = "lp", it gives a linear predictor for each margin (i.e., log hazards ratio in the proportional hazards model, log proportional odds in the proportional odds model).

When the argument type = "survival", the marginal and joint survival values will be evaluated at the given time points in the newdata.


If type = "lp", it returns a data frame with id, lp1 (linear predictor for margin 1), lp2. If type = "survival", it returns a data frame with id, t1 (evaluated times for the margin 1), t2, S1 (predicted marginal survival probabilities for margin 1), S2 and S12 (the predicted joint survival probabilities at t1, t2)


# fit a Copula2-Sieve model
copula2_sp <- ic_spTran_copula(data = AREDS, copula = "Copula2",
              l = 0, u = 15, m = 3, r = 3,
              var_list = c("ENROLLAGE","rs2284665","SevScaleBL"))
# Predicted probabilities for newdata
newdata = data.frame(id = rep(1:3, each=2), ind = rep(c(1,2),3),
                     time = c(2,3,5,6,7,8),
                    SevScaleBL = rep(3,6),
                    ENROLLAGE = rep(60,6),
                    rs2284665 = c(0,0,1,1,2,2))
output <- predict(object = copula2_sp, newdata = newdata)

[Package CopulaCenR version 1.2.3 Index]