Running ‘test-all.R’ [12s/13s]
Running the tests in ‘tests/test-all.R’ failed.
Complete output:
  > library(testthat)
  > test_check("KFAS")
  Loading required package: KFAS
  Please cite KFAS in publications by using: 
  
    Jouni Helske (2017). KFAS: Exponential Family State Space Models in R. Journal of Statistical Software, 78(10), 1-39. doi:10.18637/jss.v078.i10.
  Call:
  SSModel(formula = t12 ~ SSMcycle(period = 10, type = "common", 
      Q = 2) + SSMcycle(period = 10, type = "distinct", P1 = diag(c(1, 
      1, 2, 2)), Q = diag(1:2)) + SSMtrend(2, type = "common", 
      Q = diag(c(1, 0.5))) + SSMtrend(2, type = "distinct", Q = list(diag(0.1, 
      2), diag(0.01, 2)), P1 = diag(c(0.1, 0.01, 0.1, 0.01))) + 
      SSMseasonal(period = 4, type = "common") + SSMseasonal(period = 4, 
      type = "distinct", Q = diag(c(2, 3)), P1 = diag(c(2, 2, 2, 
          3, 3, 3))) + SSMseasonal(period = 5, type = "common", 
      sea.type = "trig") + SSMseasonal(period = 5, type = "distinct", 
      sea.type = "trig", Q = diag(c(0.1, 0.2)), P1 = diag(rep(c(0.1, 
          0.2), each = 4))) + SSMarima(ar = 0.9, ma = 0.2) + SSMregression(~-1 + 
      x, index = 1, Q = 1, data = d))
  
  State space model object of class SSModel
  
  Dimensions:
  [1] Number of time points: 100
  [1] Number of time series: 2
  [1] Number of disturbances: 25
  [1] Number of states: 38
  Names of the states:
   [1]  x.t1           level          slope          level.t1       slope.t1     
   [6]  level.t2       slope.t2       sea_dummy1     sea_dummy2     sea_dummy3   
  [11]  sea_dummy1.t1  sea_dummy2.t1  sea_dummy3.t1  sea_dummy1.t2  sea_dummy2.t2
  [16]  sea_dummy3.t2  sea_trig1      sea_trig*1     sea_trig2      sea_trig*2   
  [21]  sea_trig1.t1   sea_trig*1.t1  sea_trig2.t1   sea_trig*2.t1  sea_trig1.t2 
  [26]  sea_trig*1.t2  sea_trig2.t2   sea_trig*2.t2  cycle          cycle*       
  [31]  cycle.t1       cycle*.t1      cycle.t2       cycle*.t2      arima1.t1    
  [36]  arima2.t1      arima1.t2      arima2.t2    
  Distributions of the time series:
  [1]  gaussian
  
  Object is a valid object of class SSModel.Saving _problems/testGLM-203.R
  Saving _problems/testGLM-214.R
  Saving _problems/testGLM-225.R
  Saving _problems/testGLM-236.R
  Saving _problems/testGLM-247.R
  [ FAIL 5 | WARN 0 | SKIP 0 | PASS 570 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('testGLM.R:202:3'): Residuals for Gaussian GLM works properly ──────
  Expected `as.numeric(rstandard(kfas.gaussian, type = "pearson"))` to equal `rstandard(glm.gaussian, type = "pearson")`.
  Differences:
  20/20 mismatches (average diff: 0.0434)
  [1] -1.305 - -1.375 ==  0.0706
  [2]  0.829 -  0.874 == -0.0449
  [3]  0.224 -  0.236 == -0.0121
  [4]  1.632 -  1.720 == -0.0883
  [5] -0.805 - -0.849 ==  0.0436
  [6] -0.639 - -0.673 ==  0.0346
  [7]  0.209 -  0.220 == -0.0113
  [8] -0.760 - -0.801 ==  0.0411
  [9]  0.451 -  0.475 == -0.0244
  ...
  ── Failure ('testGLM.R:213:3'): Residuals for Poisson GLM works properly ───────
  Expected `as.numeric(rstandard(kfas.poisson, type = "pearson"))` to equal `rstandard(glm.poisson, type = "pearson")`.
  Differences:
  9/9 mismatches (average diff: 0.499)
  [1] -1.053 - -1.693 ==  0.6403
  [2]  1.436 -  2.054 == -0.6178
  [3] -0.249 - -0.368 ==  0.1190
  [4] -0.351 - -0.564 ==  0.2134
  [5] -1.306 - -1.867 ==  0.5617
  [6]  1.618 -  2.392 == -0.7734
  [7]  1.404 -  2.257 == -0.8537
  [8] -0.131 - -0.187 ==  0.0562
  [9] -1.369 - -2.024 ==  0.6545
  ── Failure ('testGLM.R:224:3'): Residuals for Binomial GLM works properly ──────
  Expected `as.numeric(rstandard(kfas.binomial, type = "pearson"))` to equal `rstandard(glm.binomial, type = "pearson")`.
  Differences:
  12/12 mismatches (average diff: 0.116)
  [1] -0.1488 - -0.1804 ==  0.03155
  [2]  0.4013 -  0.5188 == -0.11747
  [3]  0.2739 -  0.3370 == -0.06314
  [4] -0.9065 - -1.1300 ==  0.22357
  [5] -0.0334 - -0.0414 ==  0.00806
  [6]  0.8971 -  1.0234 == -0.12624
  [7] -1.1779 - -1.3858 ==  0.20798
  [8] -0.1792 - -0.2182 ==  0.03891
  [9]  0.8180 -  0.9725 == -0.15451
  ...
  ── Failure ('testGLM.R:235:3'): Residuals for Gamma GLM works properly ─────────
  Expected `as.numeric(rstandard(kfas.gamma2, type = "pearson"))` to equal `rstandard(glm.gamma, type = "pearson")`.
  Differences:
  9/9 mismatches (average diff: 0.175)
  [1]  2.355 -  3.2471 == -0.89260
  [2] -0.406 - -0.4648 ==  0.05920
  [3] -0.884 - -0.9630 ==  0.07860
  [4] -0.922 - -0.9849 ==  0.06282
  [5] -1.006 - -1.0681 ==  0.06168
  [6] -0.425 - -0.4559 ==  0.03046
  [7]  0.041 -  0.0455 == -0.00451
  [8]  0.609 -  0.7059 == -0.09684
  [9]  1.342 -  1.6293 == -0.28763
  ── Failure ('testGLM.R:246:3'): Residuals for negative binomial GLM works properly ──
  Expected `as.numeric(rstandard(kfas.NB, type = "pearson"))` to equal `rstandard(glm.NB, type = "pearson")`.
  Differences:
  146/146 mismatches (average diff: 0.0411)
  [1] -1.1437 - -1.2378 ==  0.0941
  [2] -0.4169 - -0.4512 ==  0.0343
  [3] -0.1746 - -0.1890 ==  0.0144
  [4] -0.7612 - -0.8010 ==  0.0398
  [5] -0.7612 - -0.8010 ==  0.0398
  [6]  0.0268 -  0.0282 == -0.0014
  [7]  0.7164 -  0.7538 == -0.0374
  [8]  0.9134 -  0.9611 == -0.0477
  [9] -0.2934 - -0.3186 ==  0.0252
  ...
  
  [ FAIL 5 | WARN 0 | SKIP 0 | PASS 570 ]
  Error:
  ! Test failures.
  Execution halted