Nothing
library(pseval)
library(survival)
test_that("Testing all combinations of integration and risk models", {
set.seed(500)
fakedata <- generate_example_data(n = 500)
binary.ps <- psdesign(data = fakedata, Z = Z, Y = Y.obs, S = S.obs, BIP = BIP)
expect_is(psdesign(data = fakedata, Z = Z, Y = Y.obs, S = S.obs, BIP = BIP, CPV = CPV) + integrate_parametric(S.1 ~ BIP) +
risk_binary(D = 10, risk = risk.logit) +
ps_estimate(), "psdesign")
binfit1 <- psdesign(data = fakedata, Z = Z, Y = Y.obs, S = S.obs, BIP = BIP, CPV = CPV) + integrate_parametric(S.1 ~ BIP) +
risk_binary(D = 10, risk = risk.logit) +
ps_estimate() #+ ps_bootstrap()
#stg <- calc_STG(binfit1)
#plot(binfit1, contrast = "RD")
#stg
#hist(stg$permutation$permuted.stats)
expect_is(calc_STG(binfit1, permute = FALSE)$obsSTG, "numeric")
expect_true(calc_STG(binfit1, permute = FALSE)$obsSTG > 0)
expect_is(psdesign(data = fakedata, Z = Z, Y = Y.obs, S = S.obs, BIP = BIP, BSM = BSM) + integrate_parametric(S.1 ~ BIP) +
risk_binary(D = 10, risk = risk.logit) +
ps_estimate(), "psdesign")
fakedata$weights <- runif(nrow(fakedata))
expect_is(psdesign(data = fakedata, Z = Z, Y = Y.obs, S = S.obs, BIP = BIP, CPV = CPV, BSM = BSM) + integrate_parametric(S.1 ~ BIP) +
risk_binary(D = 10, risk = risk.logit) +
ps_estimate(), "psdesign")
expect_is(psdesign(data = fakedata, Z = Z, Y = Y.obs, S = S.obs, BIP = BIP, CPV = CPV, BSM = BSM, weights = weights) + integrate_parametric(S.1 ~ BIP) +
risk_binary(D = 10, risk = risk.logit) +
ps_estimate(), "psdesign")
expect_is(binary.ps, "psdesign")
expect_is(binary.ps +
integrate_parametric(S.1 ~ BIP) +
risk_binary(model = Y ~ S.1 * Z, D = 10, risk = risk.logit) +
ps_estimate(method = "BFGS"), "psdesign")
expect_is(binary.ps +
integrate_parametric(S.1 ~ BIP) +
risk_binary(model = Y ~ S.1 * Z, D = 10, risk = risk.probit) +
ps_estimate(method = "BFGS"), "psdesign")
expect_is(binary.ps +
integrate_bivnorm("S.1") +
risk_binary(model = Y ~ S.1 * Z, D = 10, risk = risk.logit) +
ps_estimate(method = "BFGS"), "psdesign")
expect_is(binary.ps +
integrate_semiparametric(S.1 ~ BIP, S.1 ~ 1) +
risk_binary(model = Y ~ S.1 * Z, D = 10, risk = risk.logit) +
ps_estimate(method = "BFGS"), "psdesign")
expect_error(binary.ps +
integrate_nonparametric(S.1 ~ BIP))
cat.ps <- psdesign(fakedata, Z = Z, Y = Y.obs,
S = S.obs.cat, BIP = BIP.cat)
cat.ps.num <- psdesign(fakedata, Z = Z, Y = Y.obs,
S = as.numeric(S.obs.cat), BIP = as.numeric(BIP.cat))
expect_is(cat.ps.num + integrate_nonparametric(S.1 ~ BIP) + risk_binary(D = 10) + ps_estimate(method = "pseudo-score"), "psdesign")
## categorical W with continuous S
catw.ps <- psdesign(fakedata, Z = Z, Y = Y.obs,
S = S.obs, BIP = BIP.cat)
expect_is(catw.ps + integrate_parametric(S.1 ~ BIP) + risk_binary(D = 10) + ps_estimate(method = "pseudo-score"), "psdesign")
expect_is(cat.ps +
integrate_nonparametric(formula = S.1 ~ BIP) +
risk_binary(Y ~ S.1 * Z, D = 10, risk = risk.logit) +
ps_estimate(method = "pseudo-score"), "psdesign")
expect_is(cat.ps +
integrate_nonparametric(formula = S.1 ~ BIP) +
risk_binary(Y ~ S.1 * Z, D = 10, risk = risk.logit) +
ps_estimate(method = "BFGS"), "psdesign")
expect_error(cat.ps +
integrate_parametric(formula = S.1 ~ BIP))
expect_error(cat.ps +
integrate_bivnorm("S.1"))
expect_error(cat.ps +
integrate_semiparametric(formula.location = S.1 ~ BIP, formula.scale = S.1 ~ 1))
expect_warning(psdesign(fakedata, Z = Z, Y = Surv(time.obs, event.obs),
S = S.obs, BIP = BIP))
surv.ps <- psdesign(fakedata, Z = Z, Y = Surv(time.obs, event.obs),
S = S.obs, BIP = BIP, tau = 0)
expect_is(surv.ps +
integrate_parametric(S.1 ~ BIP) +
risk_exponential(D = 10) + ps_estimate(), "psdesign")
expect_is(surv.ps +
integrate_semiparametric(S.1 ~ BIP, S.1 ~ 1) +
risk_exponential(D = 10) + ps_estimate(), "psdesign")
expect_is(surv.ps +
integrate_bivnorm() +
risk_exponential(D = 10) + ps_estimate(), "psdesign")
expect_error(surv.ps + integrate_nonparametric(S.1 ~ BIP))
expect_is(surv.ps +
integrate_parametric(S.1 ~ BIP) +
risk_weibull(D = 10) + ps_estimate(), "psdesign")
expect_is(surv.ps +
integrate_semiparametric(S.1 ~ BIP, S.1 ~ 1) +
risk_weibull(D = 10) + ps_estimate(), "psdesign")
expect_is(surv.ps +
integrate_bivnorm() +
risk_weibull(D = 10) + ps_estimate(), "psdesign")
expect_error(surv.ps + integrate_parametric(S.1 ~ BIP) + risk_weibull(D = 10) + ps_estimate(method = "pseudo-score"))
## count data
fakedata.count <- fakedata
fakedata.count$Yct <- with(fakedata.count, floor(time.obs * 100))
count.ps <- psdesign(data = fakedata.count, Z = Z, Y = Yct, S = S.obs, BIP = BIP, timeon = time.obs)
expect_is(count.ps + integrate_parametric(S.1 ~ BIP) +
risk_poisson(D = 10) +
ps_estimate(), "psdesign")
## include offset
expect_is(count.ps + integrate_parametric(S.1 ~ BIP) +
risk_poisson(model = Y ~ S.1 * Z + offset(log(timeon)), D = 10) +
ps_estimate(), "psdesign")
## continuous data
fakedata$Y.cont <- log(fakedata$time.obs + 0.01)
cont.ps <- psdesign(fakedata, Z = Z, Y = Y.cont, S = S.obs, BIP = BIP)
expect_is(cont.test <- cont.ps + integrate_parametric(S.1 ~ BIP) +
risk_continuous(D = 10) + ps_estimate(),
"psdesign")
expect_is(cont.ps + integrate_semiparametric(S.1 ~ BIP, S.1 ~ BIP) +
risk_continuous(D = 10) + ps_estimate(),
"psdesign")
#plot(cont.test)
#plot(cont.test, contrast = "risk")
})
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