Running ‘test_CV_response.R’ [0s/0s]
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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