Last updated on 2026-07-23 20:51:40 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.9.10 | 18.24 | 403.66 | 421.90 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.9.10 | 13.73 | 278.24 | 291.97 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.9.10 | 34.00 | 638.14 | 672.14 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.9.10 | 13.00 | 268.73 | 281.73 | OK | |
| r-devel-windows-x86_64 | 1.9.10 | 22.00 | 418.00 | 440.00 | OK | |
| r-patched-linux-x86_64 | 1.9.10 | 20.13 | 362.16 | 382.29 | OK | |
| r-release-linux-x86_64 | 1.9.10 | 18.25 | 366.76 | 385.01 | OK | |
| r-release-macos-arm64 | 1.9.10 | 5.00 | 89.00 | 94.00 | OK | |
| r-release-macos-x86_64 | 1.9.10 | 13.00 | 429.00 | 442.00 | OK | |
| r-release-windows-x86_64 | 1.9.10 | 21.00 | 405.00 | 426.00 | OK | |
| r-oldrel-macos-arm64 | 1.9.10 | OK | ||||
| r-oldrel-macos-x86_64 | 1.9.10 | 12.00 | 393.00 | 405.00 | OK | |
| r-oldrel-windows-x86_64 | 1.9.10 | 28.00 | 528.00 | 556.00 | OK |
Version: 1.9.10
Check: tests
Result: ERROR
Running ‘test_CV_response.R’ [0s/0s]
Running ‘test_SSdlogis.R’ [0s/0s]
Running ‘test_SSquadpq.R’ [2s/3s]
Running ‘test_boot_nlme.R’ [2s/2s]
Running ‘test_confidence_intervals.R’ [3s/3s]
Running ‘test_environments.R’ [0s/0s]
Running ‘test_nlsLMList.R’ [4s/4s]
Running ‘test_predict_gam.R’ [2s/4s]
Running ‘test_predict_nlme.R’ [2s/3s]
Running ‘test_predict_nls.R’ [3s/4s]
Running ‘test_predict_summary_simulate.R’ [2s/3s]
Running ‘test_predict_varFunc.R’ [2s/3s]
Running ‘test_sim_boot_lfmc.R’ [3s/3s]
Running ‘test_simulate_gam.R’ [2s/3s]
Running ‘test_simulate_gls.R’ [2s/3s]
Running ‘test_simulate_gnls.R’ [3s/4s]
Running ‘test_simulate_lm.R’ [3s/3s]
Running ‘test_simulate_lme.R’ [3s/4s]
Running ‘test_simulate_nlme.R’ [3s/4s]
Running ‘test_var_cov.R’ [2s/3s]
Running ‘tests_SSall.R’ [4s/5s]
Running ‘tests_SSother.R’ [0s/0s]
Running the tests in ‘tests/tests_SSall.R’ failed.
Complete output:
> ## Testing all SS functions in nlraa package
> require(nlraa)
Loading required package: nlraa
>
> ## 1. SSbgf
> ## This function won't be tested here as it is used extensively in the
> ## vignette nlraa::nlraa-AgronJ-paper
> ## However, in the future I will create a version reparameterized in
> ## terms of unconstrained parameters, because the condition t.m < t.e
> ## is not guranteed
>
> ## 2. SSbgf4
> ## It would also be beneficial to reparameterize this function
> ## for routine work in terms of unconstrained parameters
> data(sm)
> #' ## Let's just pick one crop
> sm2 <- subset(sm, Crop == "M")
> ## For this particular problem it is easier to 'fix' t.b and w.b
> fit <- nls(Yield ~ bgf2(DOY, w.max, w.b = 0, t.e, t.m, t.b = 141),
+ data = sm2, start = list(w.max = 16, t.e= 240, t.m = 200))
>
> ## 3. SSbgrp
> x <- 1:30
> y <- bgrp(x, 20, log(25), log(5)) + rnorm(30, 0, 1)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSbgrp(x, w.max, lt.e, ldt), data = dat)
> exp(confint(fit)[2:3,])
Waiting for profiling to be done...
2.5% 97.5%
lt.e 24.712138 25.171632
ldt 4.650622 5.163314
>
> ## 4. SSbg4rp
> set.seed(1234)
> x <- 1:100
> y <- bg4rp(x, 20, log(70), log(30), log(20)) + rnorm(100, 0, 1)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSbg4rp(x, w.max, lt.e, ldtm, ldtb), data = dat)
> exp(coef(fit))[-1]
lt.e ldtm ldtb
70.32360 30.02220 17.75343
>
> ## 5. SSdlf
> ## Extended example in vignette 'nlraa-Oddi-LFMC'
>
> ## 6. SSricker
> set.seed(123)
> x <- 1:30
> y <- 30 * x * exp(-0.3 * x) + rnorm(30, 0, 0.25)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSricker(x, a, b), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
a 29.7588789 30.4225878
b 0.2982059 0.3020252
>
> ## 7. SSprofd
> ## I'm not a huge fan of this function as I think the dlf is more stable
> set.seed(1234)
> x <- 1:10
> y <- profd(x, 0.3, 0.05, 0.5, 4) + rnorm(10, 0, 0.01)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSprofd(x, a, b, c, d), data = dat)
> confint(fit, level = 0.9)
Waiting for profiling to be done...
5% 95%
a 0.26478673 0.32478558
b 0.01251864 0.06535237
c 0.30950669 0.81949619
d 1.92550894 15.01686244
>
> ## 8. SSnrh
> set.seed(1234)
> x <- seq(0, 2000, 100)
> y <- nrh(x, 35, 0.04, 0.83, 2) + rnorm(length(x), 0, 0.5)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSnrh(x, asym, phi, theta, rd), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
asym 34.29435363 40.14274335
phi 0.03732059 0.04944883
theta 0.57992430 0.86057599
rd 1.50629012 3.27600106
>
> ## 9. SSlinp
> set.seed(123)
> x <- 1:30
> y <- linp(x, 0, 1, 20) + rnorm(30, 0, 0.5)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSlinp(x, a, b, xs), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
a -0.3479829 0.6014031
b 0.9547236 1.0376608
xs 19.1980347 20.3174589
>
> ## 10. SSplin
> set.seed(123)
> x <- 1:30
> y <- plin(x, 10, 20, 1) + rnorm(30, 0, 0.5)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSplin(x, a, xs, b), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
a 9.8543980 10.287226
xs 19.9897839 21.260686
b 0.9660968 1.187966
>
> ## 11. SSquadp
> set.seed(123)
> x <- 1:30
> y <- quadp(x, 5, 1.7, -0.04, 20) + rnorm(30, 0, 0.6)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSquadp(x, a, b, c, xs), data = dat, algorithm = "port")
> ## Using port because default does not work
> summary(fit)
Formula: y ~ SSquadp(x, a, b, c, xs)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 5.217705 0.504927 10.334 1.07e-10 ***
b 1.654013 0.136705 12.099 3.49e-12 ***
c -0.036514 0.007817 -4.671 8.01e-05 ***
xs 16.759684 1.166384 14.369 7.02e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5908 on 26 degrees of freedom
Algorithm "port", convergence message: both X-convergence and relative convergence (5)
> ## It's strange but confint will return NAs unless level is 0.5
> confint(fit, level = 0.5)
Waiting for profiling to be done...
25% 75%
a 4.86984193 5.56309409
b 1.56049856 1.74703911
c -0.04170704 -0.03116638
xs 16.10215516 18.10527059
>
> ## 12. SSpquad
> set.seed(12345)
> x <- 1:40
> y <- pquad(x, 5, 20, 1.7, -0.04) + rnorm(40, 0, 0.6)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSpquad(x, a, xs, b, c), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
a 4.75353507 5.33189468
xs 19.59114287 20.65057362
b 1.62375057 1.95410208
c -0.05397683 -0.03487333
>
> ## 13. SSblin
> set.seed(1234)
> x <- 1:30
> y <- blin(x, 0, 0.75, 15, 1.75) + rnorm(30, 0, 0.5)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSblin(x, a, b, xs, c), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
a -0.6619779 0.4050879
b 0.6758029 0.8041661
xs 13.7666329 15.4484429
c 1.6706631 1.7726210
>
> ## 14. SSexpf
> set.seed(1234)
> x <- 1:15
> y <- expf(x, 10, -0.3) + rnorm(15, 0, 0.2)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSexpf(x, a, c), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
a 9.330309 10.5981097
c -0.329983 -0.2832748
>
> ## 15. SSexpfp
> set.seed(12345)
> x <- 1:30
> y <- expfp(x, 10, 0.1, 15) + rnorm(30, 0, 1.5)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSexpfp(x, a, c, xs), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
a 8.74904806 10.8792954
c 0.09302385 0.1130315
xs 14.30133599 15.4435476
>
> ## 16. SSpexpf
> set.seed(1234)
> x <- 1:30
> y <- pexpf(x, 20, 15, -0.2) + rnorm(30, 0, 1)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSpexpf(x, a, xs, c), data = dat)
>
> ## 17. SSbell
> set.seed(1234)
> x <- 1:20
> y <- bell(x, 8, -0.0314, 0.000317, 13) + rnorm(length(x), 0, 0.5)
> dat <- data.frame(x = x, y = y)
> fit <- nls(y ~ SSbell(x, asym, a, b, xc), data = dat)
> confint(fit)
Waiting for profiling to be done...
2.5% 97.5%
asym 7.2302693788 8.301058072
a -0.0369890550 -0.026000855
b -0.0007388103 0.001929091
xc 12.5419350314 13.606979346
>
> ## 18. SSratio
> require(minpack.lm)
Loading required package: minpack.lm
> set.seed(1234)
> x <- 1:100
> y <- ratio(x, 1, 0.5, 1, 1.5) + rnorm(length(x), 0, 0.025)
> dat <- data.frame(x = x, y = y)
> fit <- nlsLM(y ~ SSratio(x, a, b, c, d), data = dat)
>
> ## Testing nlsLMList
> require(nlme)
Loading required package: nlme
> data(Orange)
> fit.nlis.o <- nlsLMList(circumference ~ SSlogis(age, asym, xmid, scal), data = Orange)
> data(Soybean)
> fit.nlis1.s <- nlsLMList(weight ~ SSbgf(Time, w.max, t.e, t.m), data = Soybean)
Warning message:
In nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, :*** buffer overflow detected ***: terminated
Aborted
Flavor: r-devel-linux-x86_64-debian-gcc