inst/tests/test-coxph.R

In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

context("test 'the coxph extention'")

test_that("Case 1", {
    set.seed(123)
    n = 5000
    strata = c(rep(1, n/2), rep(2, n/2))
    x = runif(n)
    lambda = exp(1+x)
    y = rexp(n, rate=lambda)
    event = rep(1,n)
    data = list(y=y, event=event, x=x)
    y.surv = inla.surv(y, event)
    intercept1 = rep(1, n)
    p = inla.coxph(y.surv ~ -1 + intercept1 + x,
            list(y.surv = y.surv,  x=x, intercept1 = intercept1, strata = strata),
            control.hazard = list(strata.name = "strata"))
    r = inla(p$formula, 
            family = p$family, 
            data=c(as.list(p$data), p$data.list), 
            E = p$E)
    expect_true(abs(r$summary.fixed[1, "mean"] - 1) < 0.05)
    expect_true(abs(r$summary.fixed[2, "mean"] - 1) < 0.05)
    ## joint model
    intercept2 = rep(1, n)
    y = 1 + x + rnorm(n, sd=0.1)
    df = data.frame(intercept2, x, y)
    ## new need to cbind the data.frames, and then add the list-part of
    ## the data
    df.joint = c(as.list(inla.cbind.data.frames(p$data, df)), p$data.list)
    df.joint$Y = cbind(df.joint$y..coxph, df.joint$y)
    ## merge the formulas, recall to add '-1' and to use the new joint
    ## reponse 'Y'
    formula = update(p$formula, Y ~ intercept2 -1 + .)
    rr = inla(formula,
            family = c(p$family, "gaussian"),
            data = df.joint,
            E = df.joint$E)
    expect_true(abs(rr$summary.fixed[1, "mean"] - 1) < 0.025)
    expect_true(abs(rr$summary.fixed[2, "mean"] - 1) < 0.025)
    expect_true(abs(rr$summary.fixed[3, "mean"] - 1) < 0.025)
})