45 lines
1.7 KiB
R
45 lines
1.7 KiB
R
options(na.action=na.exclude) # preserve missings
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options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
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library(survival)
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#
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# Verify that scale can be fixed at a value
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# coefs will differ slightly due to different iteration paths
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tol <- .001
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# Intercept only models
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fit1 <- survreg(Surv(time,status) ~ 1, lung)
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fit2 <- survreg(Surv(time,status) ~ 1, lung, scale=fit1$scale)
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all.equal(fit1$coef, fit2$coef, tolerance= tol)
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all.equal(fit1$loglik, fit2$loglik, tolerance= tol)
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# The two robust variance matrices are not the same, since removing
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# an obs has a different effect on the two models. This just
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# checks for failure, not for correctness
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fit3 <- survreg(Surv(time,status) ~ 1, lung, robust=TRUE)
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fit4 <- survreg(Surv(time,status) ~ 1, lung, scale=fit1$scale, robust=TRUE)
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# multiple covariates
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fit1 <- survreg(Surv(time,status) ~ age + ph.karno, lung)
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fit2 <- survreg(Surv(time,status) ~ age + ph.karno, lung,
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scale=fit1$scale)
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all.equal(fit1$coef, fit2$coef, tolerance=tol)
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all.equal(fit1$loglik[2], fit2$loglik[2], tolerance=tol)
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fit3 <- survreg(Surv(time,status) ~ age + ph.karno, lung, robust=TRUE)
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fit4 <- survreg(Surv(time,status) ~ age + ph.karno, lung,
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scale=fit1$scale, robust=TRUE)
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# penalized models
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fit1 <- survreg(Surv(time, status) ~ pspline(age), lung)
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fit2 <- survreg(Surv(time, status) ~ pspline(age), lung, scale=fit1$scale)
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all.equal(fit1$coef, fit2$coef, tolerance=tol)
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all.equal(fit1$loglik[2], fit2$loglik[2], tolerance=tol)
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fit3 <- survreg(Surv(time,status) ~ pspline(age) + ph.karno, lung, robust=TRUE)
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fit4 <- survreg(Surv(time,status) ~ pspline(age) + ph.karno, lung,
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scale=fit1$scale, robust=TRUE)
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