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tests/testthat/test_match_stata.R
In flexsurvcure: Flexible Parametric Cure Models
test_that("Weibull Mixture matches stata", {
# Weibull, Logistic
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | -.4731367 .1641441 -2.88 0.004 -.7948533 -.1514202
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -11.10519 .5901206 -18.82 0.000 -12.2618 -9.948572
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .448163 .0576425 7.77 0.000 .3351858 .5611403
# ------------------------------------------------------------------------------
logistic_params <- c( -.4731367, .448163, -11.10519)
logistic_lower <- c( -.7948533, .3351858, -12.2618)
logistic_upper <- c(-.1514202, .5611403, -9.948572)
logistic_se <- c(.1641441 , .0576425 , .5901206)
# Suppress unimportant NaN warnings from WeibullPH functions.
logistic_null <- suppressWarnings(flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="logistic", dist="weibullPH"))
# Tolerance here is very permissive
expect_equal(logistic_params, unname(logistic_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(logistic_lower, unname(logistic_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(logistic_upper, unname(logistic_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(logistic_se, unname(logistic_null$res.t[ ,4]), tolerance=1e-2)
# Weibull, Identity Link
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | .3838745 .0388224 9.89 0.000 .3077839 .4599651
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -11.10519 .5901226 -18.82 0.000 -12.26181 -9.948572
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .4481635 .0576427 7.77 0.000 .3351859 .561141
# ------------------------------------------------------------------------------
ident_params <- c(.3838745, .4481635, -11.10519)
ident_lower <- c(.3077839, .3351859, -12.26181)
ident_upper <- c(.4599651, .561141, -9.948572)
ident_se <- c(.0388224, .0576427, .5901226)
ident_null <- flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="identity", dist="weibullPH")
# Tolerance here is very permissive
expect_equal(ident_params, unname(ident_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(ident_lower, unname(ident_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(ident_upper, unname(ident_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(ident_se, unname(ident_null$res.t[ ,4]), tolerance=1e-2)
})
test_that("Lognormal Mixture matches stata", {
# Lognormal, Loglog
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | .2484884 .1977661 1.26 0.209 -.1391261 .6361029
# -------------+----------------------------------------------------------------
# mu |
# _cons | 6.982434 .1304159 53.54 0.000 6.726824 7.238045
# -------------+----------------------------------------------------------------
# ln_sigma |
# _cons | -.0933944 .0800745 -1.17 0.243 -.2503376 .0635487
# ------------------------------------------------------------------------------
loglog_params <- c(.2484884, 6.982434, -.0933944)
loglog_lower <- c( -.1391261, 6.726824, -.2503376)
loglog_upper <- c(.6361029, 7.238045, .0635487)
loglog_se <- c(.1977661, .1304159, .0800745)
loglog_null <- flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="loglog", dist="lnorm")
# Tolerance here is very permissive
expect_equal(loglog_params, unname(loglog_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(loglog_lower, unname(loglog_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(loglog_upper, unname(loglog_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(loglog_se, unname(loglog_null$res.t[ ,4]), tolerance=1e-2)
# Lognormal, Identity Link
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | .2774585 .0703503 3.94 0.000 .1395744 .4153425
# -------------+----------------------------------------------------------------
# mu |
# _cons | 6.982433 .1304159 53.54 0.000 6.726823 7.238044
# -------------+----------------------------------------------------------------
# ln_sigma |
# _cons | -.093395 .0800746 -1.17 0.243 -.2503383 .0635482
# ------------------------------------------------------------------------------
ident_params <- c(.2774585, 6.982433, -.093395)
ident_lower <- c(.1395744, 6.726823, -.2503383)
ident_upper <- c(.4153425, 7.238044, .0635482)
ident_se <- c(.0703503, .1304159, .0800746)
ident_null <- flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="identity", dist="lnorm")
# Tolerance here is very permissive
expect_equal(ident_params, unname(ident_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(ident_lower, unname(ident_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(ident_upper, unname(ident_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(ident_se, unname(ident_null$res.t[ ,4]), tolerance=1e-2)
})
test_that("Weibull Non-Mixture matches stata", {
# Weibull, Logistic
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | -.5151878 .1806544 -2.85 0.004 -.8692639 -.1611116
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -12.16467 .6119361 -19.88 0.000 -13.36405 -10.9653
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .5110451 .0585201 8.73 0.000 .3963477 .6257425
# ------------------------------------------------------------------------------
logistic_params <- c( -.5151878, .5110451, -12.16467)
logistic_lower <- c( -.8692639, .3963477, -13.36405)
logistic_upper <- c(-.1611116, .6257425, -10.9653)
logistic_se <- c(.1806544 , .0585201 , .6119361)
# Suppress Nan Warnings
logistic_null <- suppressWarnings(flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="logistic", dist="weibullPH", mixture=F))
# Tolerance here is very permissive
expect_equal(logistic_params, unname(logistic_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(logistic_lower, unname(logistic_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(logistic_upper, unname(logistic_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(logistic_se, unname(logistic_null$res.t[ ,4]), tolerance=1e-2)
# Weibull, Loglog
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | -.0165781 .1149836 -0.14 0.885 -.2419419 .2087857
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -12.16465 .6119389 -19.88 0.000 -13.36402 -10.96527
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .5110431 .0585205 8.73 0.000 .3963451 .6257412
# ------------------------------------------------------------------------------
loglog_params <- c(-.0165781, .5110431, -12.16465)
loglog_lower <- c( -.2419419, .3963451, -13.36402)
loglog_upper <- c(.2087857, .6257412, -10.96527)
loglog_se <- c(.1149836, .0585205, .6119389)
# Supress NaN Warnings
loglog_null <- suppressWarnings(flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="loglog", dist="weibullPH", mixture=F))
# Tolerance here is very permissive
expect_equal(loglog_params, unname(loglog_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(loglog_lower, unname(loglog_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(loglog_upper, unname(loglog_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(loglog_se, unname(loglog_null$res.t[ ,4]), tolerance=1e-2)
})
test_that("Weibull Mixture w/ covariate matches stata", {
good_data <- bc
good_data$good <- ifelse(good_data$group == "Good", 1, 0)
# Weibull, loglog
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# good | -1.304919 .1775218 -7.35 0.000 -1.652856 -.9569831
# _cons | .360375 .1384624 2.60 0.009 .0889936 .6317564
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -11.05986 .5894734 -18.76 0.000 -12.2152 -9.90451
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .4434316 .0580233 7.64 0.000 .329708 .5571553
# ------------------------------------------------------------------------------
loglog_params <- c(.360375, .4434316, -11.05986, -1.304919)
loglog_lower <- c(.0889936, .329708, -12.2152, -1.652856)
loglog_upper <- c(.6317564, .5571553,-9.90451, -.9569831)
loglog_se <- c(.1384624, .0580233, .5894734, .1775218)
# Suppress NaN Warnings
loglog_cov <- suppressWarnings(flexsurvcure(Surv(rectime, censrec)~good,data=good_data,link="loglog", dist="weibullPH"))
# Tolerance here is very permissive
expect_equal(loglog_params, unname(loglog_cov$res.t[ ,1]), tolerance=1e-2)
expect_equal(loglog_lower, unname(loglog_cov$res.t[ ,2]), tolerance=1e-2)
expect_equal(loglog_upper, unname(loglog_cov$res.t[ ,3]), tolerance=1e-2)
expect_equal(loglog_se, unname(loglog_cov$res.t[ ,4]), tolerance=1e-2)
})
test_that("Weibull Mixture w/ baseline hazard", {
# Weibull, Logistic
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | -.4589729 .1644206 -2.79 0.005 -.7812314 -.1367144
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -1.871874 .1175981 -15.92 0.000 -2.102362 -1.641386
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .4501334 .0579905 7.76 0.000 .3364742 .5637926
# ------------------------------------------------------------------------------
#
logistic_params <- c( -.4589729, .4501334, -1.871874)
logistic_lower <- c( -.7812314, .3364742, -2.102362)
logistic_upper <- c(-.1367144, .5637926, -1.641386)
logistic_se <- c(.1644206 , .0579905 , .1175981)
# Suppress NaN Warnings
logistic_null <- suppressWarnings(flexsurvcure(Surv(recyrs, censrec)~1,data=bc,link="logistic", dist="weibullPH", bhazard=rep(0.001,686)))
# Tolerance here is very permissive
expect_equal(logistic_params, unname(logistic_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(logistic_lower, unname(logistic_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(logistic_upper, unname(logistic_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(logistic_se, unname(logistic_null$res.t[ ,4]), tolerance=1e-2)
})
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flexsurvcure documentation built on Nov. 2, 2022, 1:07 a.m.
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test_that("Weibull Mixture matches stata", {
# Weibull, Logistic
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | -.4731367 .1641441 -2.88 0.004 -.7948533 -.1514202
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -11.10519 .5901206 -18.82 0.000 -12.2618 -9.948572
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .448163 .0576425 7.77 0.000 .3351858 .5611403
# ------------------------------------------------------------------------------
logistic_params <- c( -.4731367, .448163, -11.10519)
logistic_lower <- c( -.7948533, .3351858, -12.2618)
logistic_upper <- c(-.1514202, .5611403, -9.948572)
logistic_se <- c(.1641441 , .0576425 , .5901206)
# Suppress unimportant NaN warnings from WeibullPH functions.
logistic_null <- suppressWarnings(flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="logistic", dist="weibullPH"))
# Tolerance here is very permissive
expect_equal(logistic_params, unname(logistic_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(logistic_lower, unname(logistic_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(logistic_upper, unname(logistic_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(logistic_se, unname(logistic_null$res.t[ ,4]), tolerance=1e-2)
# Weibull, Identity Link
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | .3838745 .0388224 9.89 0.000 .3077839 .4599651
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -11.10519 .5901226 -18.82 0.000 -12.26181 -9.948572
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .4481635 .0576427 7.77 0.000 .3351859 .561141
# ------------------------------------------------------------------------------
ident_params <- c(.3838745, .4481635, -11.10519)
ident_lower <- c(.3077839, .3351859, -12.26181)
ident_upper <- c(.4599651, .561141, -9.948572)
ident_se <- c(.0388224, .0576427, .5901226)
ident_null <- flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="identity", dist="weibullPH")
# Tolerance here is very permissive
expect_equal(ident_params, unname(ident_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(ident_lower, unname(ident_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(ident_upper, unname(ident_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(ident_se, unname(ident_null$res.t[ ,4]), tolerance=1e-2)
})
test_that("Lognormal Mixture matches stata", {
# Lognormal, Loglog
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | .2484884 .1977661 1.26 0.209 -.1391261 .6361029
# -------------+----------------------------------------------------------------
# mu |
# _cons | 6.982434 .1304159 53.54 0.000 6.726824 7.238045
# -------------+----------------------------------------------------------------
# ln_sigma |
# _cons | -.0933944 .0800745 -1.17 0.243 -.2503376 .0635487
# ------------------------------------------------------------------------------
loglog_params <- c(.2484884, 6.982434, -.0933944)
loglog_lower <- c( -.1391261, 6.726824, -.2503376)
loglog_upper <- c(.6361029, 7.238045, .0635487)
loglog_se <- c(.1977661, .1304159, .0800745)
loglog_null <- flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="loglog", dist="lnorm")
# Tolerance here is very permissive
expect_equal(loglog_params, unname(loglog_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(loglog_lower, unname(loglog_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(loglog_upper, unname(loglog_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(loglog_se, unname(loglog_null$res.t[ ,4]), tolerance=1e-2)
# Lognormal, Identity Link
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | .2774585 .0703503 3.94 0.000 .1395744 .4153425
# -------------+----------------------------------------------------------------
# mu |
# _cons | 6.982433 .1304159 53.54 0.000 6.726823 7.238044
# -------------+----------------------------------------------------------------
# ln_sigma |
# _cons | -.093395 .0800746 -1.17 0.243 -.2503383 .0635482
# ------------------------------------------------------------------------------
ident_params <- c(.2774585, 6.982433, -.093395)
ident_lower <- c(.1395744, 6.726823, -.2503383)
ident_upper <- c(.4153425, 7.238044, .0635482)
ident_se <- c(.0703503, .1304159, .0800746)
ident_null <- flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="identity", dist="lnorm")
# Tolerance here is very permissive
expect_equal(ident_params, unname(ident_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(ident_lower, unname(ident_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(ident_upper, unname(ident_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(ident_se, unname(ident_null$res.t[ ,4]), tolerance=1e-2)
})
test_that("Weibull Non-Mixture matches stata", {
# Weibull, Logistic
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | -.5151878 .1806544 -2.85 0.004 -.8692639 -.1611116
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -12.16467 .6119361 -19.88 0.000 -13.36405 -10.9653
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .5110451 .0585201 8.73 0.000 .3963477 .6257425
# ------------------------------------------------------------------------------
logistic_params <- c( -.5151878, .5110451, -12.16467)
logistic_lower <- c( -.8692639, .3963477, -13.36405)
logistic_upper <- c(-.1611116, .6257425, -10.9653)
logistic_se <- c(.1806544 , .0585201 , .6119361)
# Suppress Nan Warnings
logistic_null <- suppressWarnings(flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="logistic", dist="weibullPH", mixture=F))
# Tolerance here is very permissive
expect_equal(logistic_params, unname(logistic_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(logistic_lower, unname(logistic_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(logistic_upper, unname(logistic_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(logistic_se, unname(logistic_null$res.t[ ,4]), tolerance=1e-2)
# Weibull, Loglog
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | -.0165781 .1149836 -0.14 0.885 -.2419419 .2087857
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -12.16465 .6119389 -19.88 0.000 -13.36402 -10.96527
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .5110431 .0585205 8.73 0.000 .3963451 .6257412
# ------------------------------------------------------------------------------
loglog_params <- c(-.0165781, .5110431, -12.16465)
loglog_lower <- c( -.2419419, .3963451, -13.36402)
loglog_upper <- c(.2087857, .6257412, -10.96527)
loglog_se <- c(.1149836, .0585205, .6119389)
# Supress NaN Warnings
loglog_null <- suppressWarnings(flexsurvcure(Surv(rectime, censrec)~1,data=bc,link="loglog", dist="weibullPH", mixture=F))
# Tolerance here is very permissive
expect_equal(loglog_params, unname(loglog_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(loglog_lower, unname(loglog_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(loglog_upper, unname(loglog_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(loglog_se, unname(loglog_null$res.t[ ,4]), tolerance=1e-2)
})
test_that("Weibull Mixture w/ covariate matches stata", {
good_data <- bc
good_data$good <- ifelse(good_data$group == "Good", 1, 0)
# Weibull, loglog
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# good | -1.304919 .1775218 -7.35 0.000 -1.652856 -.9569831
# _cons | .360375 .1384624 2.60 0.009 .0889936 .6317564
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -11.05986 .5894734 -18.76 0.000 -12.2152 -9.90451
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .4434316 .0580233 7.64 0.000 .329708 .5571553
# ------------------------------------------------------------------------------
loglog_params <- c(.360375, .4434316, -11.05986, -1.304919)
loglog_lower <- c(.0889936, .329708, -12.2152, -1.652856)
loglog_upper <- c(.6317564, .5571553,-9.90451, -.9569831)
loglog_se <- c(.1384624, .0580233, .5894734, .1775218)
# Suppress NaN Warnings
loglog_cov <- suppressWarnings(flexsurvcure(Surv(rectime, censrec)~good,data=good_data,link="loglog", dist="weibullPH"))
# Tolerance here is very permissive
expect_equal(loglog_params, unname(loglog_cov$res.t[ ,1]), tolerance=1e-2)
expect_equal(loglog_lower, unname(loglog_cov$res.t[ ,2]), tolerance=1e-2)
expect_equal(loglog_upper, unname(loglog_cov$res.t[ ,3]), tolerance=1e-2)
expect_equal(loglog_se, unname(loglog_cov$res.t[ ,4]), tolerance=1e-2)
})
test_that("Weibull Mixture w/ baseline hazard", {
# Weibull, Logistic
# ------------------------------------------------------------------------------
# _t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
# -------------+----------------------------------------------------------------
# pi |
# _cons | -.4589729 .1644206 -2.79 0.005 -.7812314 -.1367144
# -------------+----------------------------------------------------------------
# ln_lambda |
# _cons | -1.871874 .1175981 -15.92 0.000 -2.102362 -1.641386
# -------------+----------------------------------------------------------------
# ln_gamma |
# _cons | .4501334 .0579905 7.76 0.000 .3364742 .5637926
# ------------------------------------------------------------------------------
#
logistic_params <- c( -.4589729, .4501334, -1.871874)
logistic_lower <- c( -.7812314, .3364742, -2.102362)
logistic_upper <- c(-.1367144, .5637926, -1.641386)
logistic_se <- c(.1644206 , .0579905 , .1175981)
# Suppress NaN Warnings
logistic_null <- suppressWarnings(flexsurvcure(Surv(recyrs, censrec)~1,data=bc,link="logistic", dist="weibullPH", bhazard=rep(0.001,686)))
# Tolerance here is very permissive
expect_equal(logistic_params, unname(logistic_null$res.t[ ,1]), tolerance=1e-2)
expect_equal(logistic_lower, unname(logistic_null$res.t[ ,2]), tolerance=1e-2)
expect_equal(logistic_upper, unname(logistic_null$res.t[ ,3]), tolerance=1e-2)
expect_equal(logistic_se, unname(logistic_null$res.t[ ,4]), tolerance=1e-2)
})
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