Last updated on 2026-10-24 12:51:45 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.5.1 | 13.82 | 376.22 | 390.04 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.5.1 | 8.51 | 173.15 | 181.66 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.5.1 | 10.00 | 244.46 | 254.46 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.5.1 | 251.99 | OK | |||
| r-devel-windows-x86_64 | 1.5.1 | 18.00 | 418.00 | 436.00 | OK | |
| r-patched-linux-x86_64 | 1.5.1 | 11.21 | 355.36 | 366.57 | OK | |
| r-release-linux-x86_64 | 1.5.1 | 13.44 | 363.15 | 376.59 | OK | |
| r-release-macos-arm64 | 1.5.1 | 3.00 | 97.00 | 100.00 | OK | |
| r-release-macos-x86_64 | 1.5.1 | 10.00 | 272.00 | 282.00 | OK | |
| r-release-windows-x86_64 | 1.5.1 | 14.00 | 399.00 | 413.00 | OK | |
| r-oldrel-macos-arm64 | 1.5.1 | 3.00 | 96.00 | 99.00 | OK | |
| r-oldrel-macos-x86_64 | 1.5.1 | 9.00 | 327.00 | 336.00 | OK | |
| r-oldrel-windows-x86_64 | 1.5.1 | 22.00 | 576.00 | 598.00 | OK |
Version: 1.5.1
Check: examples
Result: ERROR
Running examples in ‘RESI-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: resi
> ### Title: Robust Effect Size Index (RESI) point and interval estimation
> ### for models
> ### Aliases: resi resi.default resi.glm resi.lm resi.lmrob resi.glmrob
> ### resi.nls resi.survreg resi.coxph resi.hurdle resi.zeroinfl
> ### resi.geeglm resi.glmgee resi.gee resi.lme resi.lmerMod resi.glmmTMB
>
> ### ** Examples
>
> ## for timing purposes, a small number of bootstrap replicates is used in the
> ## examples. Run them with a higher or default `nboot` argument for better performance
>
> ## RESI on a linear model
> # fit linear model
> mod = lm(charges ~ region * age + bmi + sex, data = RESI::insurance)
>
> # run resi on fitted model with desired number of bootstrap replicates
> # store bootstrap results for calculating different CIs later
> resi_obj = resi(mod, nboot = 50, store.boot = TRUE)
> # print output
> resi_obj
Analysis of effect sizes based on RESI:
Confidence level = 0.05
Call: lm(formula = charges ~ region * age + bmi + sex, data = RESI::insurance)
Coefficient Table
Estimate Std. Error t value Pr(>|t|) RESI 2.5%
(Intercept) -5359.4352 2175.9439 -2.4630 0.0139 -0.0673 -0.1203
regionnorthwest -2339.4433 2395.1507 -0.9767 0.3289 -0.0267 -0.0804
regionsoutheast -3230.8512 2643.1099 -1.2224 0.2218 -0.0334 -0.0870
regionsouthwest -232.4839 2574.2823 -0.0903 0.9281 -0.0025 -0.0560
age 220.3325 40.2091 5.4797 0.0000 0.1497 0.0924
bmi 323.7725 58.0849 5.5741 0.0000 0.1523 0.1067
sexmale 1328.0215 621.7421 2.1360 0.0329 0.0584 0.0047
regionnorthwest:age 34.9040 57.2364 0.6098 0.5421 0.0167 -0.0369
regionsoutheast:age 83.6359 63.3258 1.3207 0.1868 0.0361 -0.0175
regionsouthwest:age -33.6290 61.4065 -0.5476 0.5840 -0.0150 -0.0688
97.5%
(Intercept) -0.0144
regionnorthwest 0.0270
regionsoutheast 0.0201
regionsouthwest 0.0511
age 0.2072
bmi 0.1980
sexmale 0.1121
regionnorthwest:age 0.0703
regionsoutheast:age 0.0898
regionsouthwest:age 0.0389
Analysis of Deviance Table (Type II tests)
Response: charges
Df F Pr(>F) RESI 2.5% 97.5%
region 3 1.5959 0.1886 0.0365 0.0000 0.1048
age 1 117.7046 0.0000 0.2951 0.2384 0.3548
bmi 1 31.0708 0.0000 0.1498 0.1067 0.1980
sex 1 4.5624 0.0329 0.0515 0.0000 0.1121
region:age 3 1.1167 0.3412 0.0161 0.0000 0.0928
Overall RESI comparing model to intercept-only model:
Res.Df Df F Pr(>F) RESI
1 1328 9 20.2486 0 0.3595
Notes:
1. The RESI was calculated using a robust covariance estimator.
2. Confidence intervals (CIs) constructed using 50 non-parametric bootstraps.
>
> # fit a reduced model for comparison
> mod_red = lm(charges ~ bmi, data = RESI::insurance)
>
> # running resi and including the reduced model will provide almost the exact same
> # output as not including a reduced model. The difference is that the "overall"
> # portion of the output will compare the full model to the reduced model.
> # The "summary" and "anova" RESI estimates will be the same. (The bootstrapped
> # confidence intervals may differ.)
> resi(model.full = mod, model.reduced = mod_red, nboot = 10)
Analysis of effect sizes based on RESI:
Confidence level = 0.05
Full Model:lm(formula = charges ~ region * age + bmi + sex, data = RESI::insurance)
Reduced Model:lm(formula = charges ~ bmi, data = RESI::insurance)
Coefficient Table
Estimate Std. Error t value Pr(>|t|) RESI 2.5%
(Intercept) -5359.4352 2175.9439 -2.4630 0.0139 -0.0673 -0.1203
regionnorthwest -2339.4433 2395.1507 -0.9767 0.3289 -0.0267 -0.0804
regionsoutheast -3230.8512 2643.1099 -1.2224 0.2218 -0.0334 -0.0870
regionsouthwest -232.4839 2574.2823 -0.0903 0.9281 -0.0025 -0.0560
age 220.3325 40.2091 5.4797 0.0000 0.1497 0.0924
bmi 323.7725 58.0849 5.5741 0.0000 0.1523 0.1067
sexmale 1328.0215 621.7421 2.1360 0.0329 0.0584 0.0047
regionnorthwest:age 34.9040 57.2364 0.6098 0.5421 0.0167 -0.0369
regionsoutheast:age 83.6359 63.3258 1.3207 0.1868 0.0361 -0.0175
regionsouthwest:age -33.6290 61.4065 -0.5476 0.5840 -0.0150 -0.0688
97.5%
(Intercept) -0.0144
regionnorthwest 0.0270
regionsoutheast 0.0201
regionsouthwest 0.0511
age 0.2072
bmi 0.1980
sexmale 0.1121
regionnorthwest:age 0.0703
regionsoutheast:age 0.0898
regionsouthwest:age 0.0389
Analysis of Deviance Table (Type II tests)
Response: charges
Df F Pr(>F) RESI 2.5% 97.5%
region 3 1.5959 0.1886 0.0365 0.0000 0.1048
age 1 117.7046 0.0000 0.2951 0.2384 0.3548
bmi 1 31.0708 0.0000 0.1498 0.1067 0.1980
sex 1 4.5624 0.0329 0.0515 0.0000 0.1121
region:age 3 1.1167 0.3412 0.0161 0.0000 0.0928
Overall RESI comparing full model to reduced model:
Res.Df Df F Pr(>F) RESI
1 1328 8 15.9113 0 0.2983
Notes:
1. The RESI was calculated using a robust covariance estimator.
2. Confidence intervals (CIs) constructed using 10 non-parametric bootstraps.
>
> # used stored bootstrap results to get a different alpha-level confidence interval
> summary(resi_obj, alpha = c(0.01, 0.1))
Analysis of effect sizes based on RESI:
Confidence level = 0.01 0.1
Call: lm(formula = charges ~ region * age + bmi + sex, data = RESI::insurance)
Coefficient Table
Estimate Std. Error t value Pr(>|t|) RESI 0.5%
(Intercept) -5359.4352 2175.9439 -2.4630 0.0139 -0.0673 -0.1369
regionnorthwest -2339.4433 2395.1507 -0.9767 0.3289 -0.0267 -0.0973
regionsoutheast -3230.8512 2643.1099 -1.2224 0.2218 -0.0334 -0.1038
regionsouthwest -232.4839 2574.2823 -0.0903 0.9281 -0.0025 -0.0729
age 220.3325 40.2091 5.4797 0.0000 0.1497 0.0743
bmi 323.7725 58.0849 5.5741 0.0000 0.1523 0.0924
sexmale 1328.0215 621.7421 2.1360 0.0329 0.0584 -0.0122
regionnorthwest:age 34.9040 57.2364 0.6098 0.5421 0.0167 -0.0538
regionsoutheast:age 83.6359 63.3258 1.3207 0.1868 0.0361 -0.0344
regionsouthwest:age -33.6290 61.4065 -0.5476 0.5840 -0.0150 -0.0858
99.5% 5% 95%
(Intercept) 0.0022 -0.1118 -0.0229
regionnorthwest 0.0439 -0.0718 0.0184
regionsoutheast 0.0370 -0.0784 0.0115
regionsouthwest 0.0679 -0.0474 0.0425
age 0.2253 0.1016 0.1980
bmi 0.2124 0.1141 0.1907
sexmale 0.1290 0.0133 0.1035
regionnorthwest:age 0.0871 -0.0283 0.0617
regionsoutheast:age 0.1066 -0.0089 0.0811
regionsouthwest:age 0.0558 -0.0602 0.0302
> car::Anova(resi_obj, alpha = c(0.01, 0.1))
Df F Pr(>F) RESI 0.5% 99.5% 5% 95%
region 3 1.5959 0.18856 0.036480 0.000000 0.12289 0.000000 0.09552
age 1 117.7046 0.00000 0.295111 0.220069 0.37313 0.247731 0.34547
bmi 1 31.0708 0.00000 0.149798 0.092383 0.21239 0.114071 0.19071
sex 1 4.5624 0.03286 0.051549 0.000000 0.12902 0.011851 0.10349
region:age 3 1.1167 0.34115 0.016056 0.000000 0.11090 0.000000 0.08339
>
> # the result of resi, as well as the summary or Anova of a `resi` object can be plotted
> # if the resi object was created with the store.boot = `TRUE` option, any alpha
> # can be specified
> plot(resi_obj, alpha = 0.01)
> # if the variable names on the y-axis are too long, you can reduce their size with
> # the ycex.axis argument (or use regular common solutions like changing the margins)
> plot(resi_obj, alpha = 0.01, ycex.axis = 0.5)
>
> # for some model types and formula structures, data argument is required
> if(requireNamespace("splines")){
+ # fit logistic regression model with splines
+ mod = glm(smoker ~ splines::ns(age, df = 3) + region, data = RESI::insurance,
+ family = "binomial")
+
+ # specify additional arguments to the variance-covariance function via vcov.args
+ resi_obj = resi(mod, data = RESI::insurance, alpha = 0.01,
+ vcov.args = list(type = "HC0"), nboot = 25)
+ summary(resi_obj)
+ car::Anova(resi_obj)}
Analysis of Deviance Table (Type II tests)
Response: smoker
Df Chisq Pr(>Chisq) RESI 0.5% 99.5%
splines::ns(age, df = 3) 3 1.4735 0.68841 0.000000 0 0.089509
region 3 7.2960 0.06304 0.056663 0 0.137583
>
>
> ## RESI on a survival model with alternate Z2S
> if(requireNamespace("survival")){
+ # fit coxph model on example data from survival package
+ # Note: for survival models, you need to specify robust variance in the model
+ # creation. resi will ignore the vcovfunc argument for this reason.
+ mod.coxph = survival::coxph(survival::Surv(time, status) ~ age + sex + wt.loss,
+ data=survival::lung, robust = TRUE)
+
+ # run resi on the model
+ # to use the alternative Z to RESI formula (which is equal in absolute value to the
+ # chi-square to RESI (S) formula), specify unbiased = FALSE.
+ resi(mod.coxph, data = survival::lung, unbiased = FALSE, nboot = 10)}
Loading required namespace: survival
Error in value[[3L]](cond) :
car::Anova failed. Try rerunning with anova = FALSE
Calls: resi ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.5.1
Check: tests
Result: ERROR
Running ‘testthat.R’ [103s/118s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
>
> library(testthat)
> library(RESI)
>
> test_check("RESI")
Loading required package: carData
Loading required namespace: survival
Loading required namespace: pscl
Loading required namespace: gee
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept) diagnosesevere drugstandard time
-0.08759224 -1.31391092 0.05960381 1.49985707
drugstandard:time
-1.01744498
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept)
0.0902573
Loading required namespace: geepack
Loading required namespace: glmtoolbox
Loading required namespace: lme4
boundary (singular) fit: see help('isSingular')
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept) diagnosesevere drugstandard time
-0.08759224 -1.31391092 0.05960381 1.49985707
drugstandard:time
-1.01744498
Loading required namespace: glmmTMB
Saving _problems/test-resi-174.R
Updating model fit failed. Try running with providing data argument
Updating model fit failed. Try running with providing data argument
Saving _problems/test-resi-227.R
Saving _problems/test-resi-302.R
Saving _problems/test-resi-407.R
Saving _problems/test-resi-470.R
[ FAIL 5 | WARN 102 | SKIP 0 | PASS 159 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-resi.R:173:3'): Specifying non-allowed vcov produces warning ───
Error in `value[[3L]](cond)`: car::Anova failed. Try rerunning with anova = FALSE
Backtrace:
▆
1. ├─testthat::expect_warning(...) at test-resi.R:173:3
2. │ └─testthat:::expect_condition_matching_(...)
3. │ └─testthat:::quasi_capture(...)
4. │ ├─testthat (local) .capture(...)
5. │ │ └─base::withCallingHandlers(...)
6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
7. ├─RESI::resi(mod.coxph, data = data.surv, nboot = 1, vcovfunc = sandwich::vcovHC)
8. └─RESI:::resi.coxph(mod.coxph, data = data.surv, nboot = 1, vcovfunc = sandwich::vcovHC)
9. └─RESI:::resi.default(...)
10. ├─RESI::resi_pe(...)
11. └─RESI:::resi_pe.coxph(...)
12. └─RESI:::resi_pe.default(...)
13. └─base::tryCatch(...)
14. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
15. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
16. └─value[[3L]](cond)
── Error ('test-resi.R:225:3'): resi produces the correct estimates ────────────
Error in `value[[3L]](cond)`: car::Anova failed. Try rerunning with anova = FALSE
Backtrace:
▆
1. ├─testthat::expect_equal(...) at test-resi.R:225:3
2. │ └─testthat::quasi_label(enquo(object), label)
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─base::unname(resi(mod.coxph, nboot = 1, data = data.surv)$estimates)
5. ├─RESI::resi(mod.coxph, nboot = 1, data = data.surv)
6. └─RESI:::resi.coxph(mod.coxph, nboot = 1, data = data.surv)
7. └─RESI:::resi.default(...)
8. ├─RESI::resi_pe(...)
9. └─RESI:::resi_pe.coxph(...)
10. └─RESI:::resi_pe.default(...)
11. └─base::tryCatch(...)
12. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
13. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
14. └─value[[3L]](cond)
── Error ('test-resi.R:302:3'): RESI estimates are in between the confidence limits ──
Error in `value[[3L]](cond)`: car::Anova failed. Try rerunning with anova = FALSE
Backtrace:
▆
1. ├─RESI::resi(mod.coxph, data = data.surv, nboot = 500) at test-resi.R:302:3
2. └─RESI:::resi.coxph(mod.coxph, data = data.surv, nboot = 500)
3. └─RESI:::resi.default(...)
4. ├─RESI::resi_pe(...)
5. └─RESI:::resi_pe.coxph(...)
6. └─RESI:::resi_pe.default(...)
7. └─base::tryCatch(...)
8. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
9. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
10. └─value[[3L]](cond)
── Error ('test-resi.R:407:3'): unbiased = FALSE returns same abs. RESI as Chi-sq/F ──
Error in `value[[3L]](cond)`: car::Anova failed. Try rerunning with anova = FALSE
Backtrace:
▆
1. ├─RESI::resi(mod.coxph, data = data.surv, nboot = 1, unbiased = FALSE) at test-resi.R:407:3
2. └─RESI:::resi.coxph(mod.coxph, data = data.surv, nboot = 1, unbiased = FALSE)
3. └─RESI:::resi.default(...)
4. ├─RESI::resi_pe(...)
5. └─RESI:::resi_pe.coxph(...)
6. └─RESI:::resi_pe.default(...)
7. └─base::tryCatch(...)
8. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
9. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
10. └─value[[3L]](cond)
── Error ('test-resi.R:470:3'): tibbles work ───────────────────────────────────
Error in `value[[3L]](cond)`: car::Anova failed. Try rerunning with anova = FALSE
Backtrace:
▆
1. ├─testthat::expect_equal(...) at test-resi.R:470:3
2. │ └─testthat::quasi_label(enquo(object), label)
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─base::unname(resi(mod.coxph.tib, nboot = 1, data = data.surv)$estimates)
5. ├─RESI::resi(mod.coxph.tib, nboot = 1, data = data.surv)
6. └─RESI:::resi.coxph(mod.coxph.tib, nboot = 1, data = data.surv)
7. └─RESI:::resi.default(...)
8. ├─RESI::resi_pe(...)
9. └─RESI:::resi_pe.coxph(...)
10. └─RESI:::resi_pe.default(...)
11. └─base::tryCatch(...)
12. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
13. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
14. └─value[[3L]](cond)
[ FAIL 5 | WARN 102 | SKIP 0 | PASS 159 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc