Running 'test-palmtree.R' [161s]
Running the tests in 'tests/test-palmtree.R' failed.
Complete output:
  > library("palmtree")
  Loading required package: partykit
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  > 
  > ## load data and scale points achieved to [0, 100] percent
  > data("MathExam14W", package = "psychotools")
  > MathExam14W$tests <- 100 * MathExam14W$tests/26
  > MathExam14W$pcorrect <- 100 * MathExam14W$nsolved/13
  > 
  > MathExam <- MathExam14W[,c("pcorrect", "group", "tests", "study",
  +                            "attempt", "semester", "gender")]
  > 
  > 
  > ## fit PALM tree to detect potential group effects
  > ## while globally adjusting for math ability prior to the exam
  > tr <- palmtree(pcorrect ~ group | tests | tests + study +
  +                  attempt + semester + gender, data = MathExam)
  > 
  > palmmod <- lm(pcorrect ~ 0 + .tree + group:.tree + tests, data = tr$data)
  > 
  > tr
  Partially additive linear model tree
  
  Model formula:
  pcorrect ~ group | tests + study + attempt + semester + gender
  
  Fitted party:
  [1] root
  |   [2] attempt <= 1
  |   |   [3] tests <= 92.30769: n = 352
  |   |       (Intercept)      group2 
  |   |         -7.088499   -2.997321 
  |   |   [4] tests > 92.30769: n = 79
  |   |       (Intercept)      group2 
  |   |          13.98085   -14.49418 
  |   [5] attempt > 1: n = 298
  |       (Intercept)      group2 
  |          2.332298   -1.704129 
  
  Number of inner nodes:    2
  Number of terminal nodes: 3
  Number of parameters per node: 2
  Objective function (residual sum of squares): 253218
  
  Linear fixed effects (from palm model):
      tests 
  0.7868941 
  > palmmod
  
  Call:
  lm(formula = pcorrect ~ 0 + .tree + group:.tree + tests, data = tr$data)
  
  Coefficients:
         .tree3         .tree4         .tree5          tests  .tree3:group2  
        -7.0885        13.9808         2.3323         0.7869        -2.9973  
  .tree4:group2  .tree5:group2  
       -14.4942        -1.7041  
  
  > 
  > predict(tr, newdata = MathExam[1:3,])
         1        2        3 
  47.38878 44.70352 53.44181 
  > 
  > proc.time()
     user  system elapsed 
     1.23    0.23    1.45