Running 'bugfixes.R' [5s]
  Running 'regtest-families.R' [14s]
  Running 'regtest-gamboostLSS.R' [12s]
  Running 'regtest-glmboostLSS.R' [10s]
  Running 'regtest-mstop.R' [6s]
  Running 'regtest-noncyclic_fitting.R' [17s]
  Running 'regtest-stabilization.R' [45s]
  Running 'regtest-stabsel.R' [9s]
Running the tests in 'tests/regtest-noncyclic_fitting.R' failed.
Complete output:
  > require("gamboostLSS")
  Loading required package: gamboostLSS
  Loading required package: mboost
  Loading required package: parallel
  Loading required package: stabs
  
  Attaching package: 'gamboostLSS'
  
  The following object is masked from 'package:stats':
  
      model.weights
  
  > 
  > ###negbin dist, linear###
  > 
  > set.seed(2611)
  > x1 <- rnorm(1000)
  > x2 <- rnorm(1000)
  > x3 <- rnorm(1000)
  > x4 <- rnorm(1000)
  > x5 <- rnorm(1000)
  > x6 <- rnorm(1000)
  > mu    <- exp(1.5 + x1^2 +0.5 * x2 - 3 * sin(x3) -1 * x4)
  > sigma <- exp(-0.2 * x4 +0.2 * x5 +0.4 * x6)
  > y <- numeric(1000)
  > for (i in 1:1000)
  +   y[i] <- rnbinom(1, size = sigma[i], mu = mu[i])
  > dat <- data.frame(x1, x2, x3, x4, x5, x6, y)
  > 
  > #fit models at number of params + 1
  > 
  > #glmboost
  > model <- glmboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
  +                      control = boost_control(mstop = 3), method = "noncyclic")
  > 
  > #linear baselearner with bols
  > model <- gamboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
  +                      control = boost_control(mstop = 3), method = "noncyclic",
  +                      baselearner = "bols")
  > 
  > #nonlinear bbs baselearner
  > 
  > model <- gamboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
  +                      control = boost_control(mstop = 3), method = "noncyclic",
  +                      baselearner = "bbs")
  > 
  > #reducing model and increasing it afterwards should yield the same fit
  > 
  > model <- glmboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
  +                      control = boost_control(mstop = 50), method = "noncyclic")
  > 
  > m_co <- coef(model)
  > 
  > mstop(model) <- 5
  > mstop(model) <- 50
  > 
  > stopifnot(all.equal(m_co, coef(model)))
  > 
  > 
  > model <- gamboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
  +                      control = boost_control(mstop = 50), method = "noncyclic",
  +                      baselearner = "bols")
  > 
  > m_co <- coef(model)
  > 
  > mstop(model) <- 5
  > mstop(model) <- 50
  > 
  > stopifnot(all.equal(m_co, coef(model)))
  > 
  > 
  > model <- gamboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
  +                      control = boost_control(mstop = 50), method = "noncyclic",
  +                      baselearner = "bbs")
  > 
  > m_co <- coef(model)
  > 
  > mstop(model) <- 5
  > mstop(model) <- 50
  > 
  > stopifnot(all.equal(m_co, coef(model)))
  > 
  > 
  > model <- gamboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
  +                      control = boost_control(mstop = 50), method = "noncyclic",
  +                      baselearner = "bbs")
  > 
  > m_co <- coef(model)
  > 
  > mstop(model) <- 5
  > mstop(model) <- 50
  > 
  > stopifnot(all.equal(m_co, coef(model)))
  > 
  > ## check cvrisk for noncyclic models
  > model <- glmboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
  +                      control = boost_control(mstop = 3), method = "noncyclic")
  > cvr1 <- cvrisk(model, grid = 1:50, cv(model.weights(model), B = 5))
  Starting cross-validation...
  [Fold: 1]
  [  1] ........................................ -- risk: 1805.447 
  [ 41] .........
  Final risk: 1799.837 
  
  [Fold: 2]
  [  1] ........................................ -- risk: 1734.54 
  [ 41] .........
  Final risk: 1732.471 
  
  [Fold: 3]
  [  1] ........................................ -- risk: 1836.44 
  [ 41] .........
  Final risk: 1830.995 
  
  [Fold: 4]
  [  1] ........................................ -- risk: 1644.972 
  [ 41] .........
  Final risk: 1643.654 
  
  [Fold: 5]
  [  1] ........................................ -- risk: 1748.201 
  [ 41] .........
  Final risk: 1745.054 
  > cvr1
  
  	 Cross-validated 
  	 glmboostLSS(formula = y ~ ., data = dat, families = NBinomialLSS(),      control = boost_control(mstop = 3), method = "noncyclic") 
  
         1        2        3        4        5        6        7        8 
  4.857889 4.857889 4.854336 4.851760 4.848367 4.845487 4.843456 4.840692 
         9       10       11       12       13       14       15       16 
  4.837335 4.835769 4.833281 4.831312 4.829041 4.827176 4.824955 4.822376 
        17       18       19       20       21       22       23       24 
  4.820667 4.819740 4.817552 4.816011 4.813472 4.812703 4.810620 4.809356 
        25       26       27       28       29       30       31       32 
  4.807388 4.806713 4.804566 4.803307 4.801508 4.800541 4.799011 4.798048 
        33       34       35       36       37       38       39       40 
  4.796181 4.795305 4.793928 4.793380 4.791924 4.790579 4.789696 4.788866 
        41       42       43       44       45       46       47       48 
  4.787808 4.786352 4.785354 4.784043 4.783679 4.782704 4.781571 4.780256 
        49       50 
  4.779861 4.778943 
  
  	 Optimal number of boosting iterations: 50 
  > plot(cvr1)
  > 
  > risk(model, merge = TRUE)
        mu    sigma    sigma       mu       mu    sigma       mu       mu 
  1770.175 1770.175 1768.441 1768.050 1767.677 1766.076 1765.724 1765.388 
     sigma       mu       mu    sigma       mu       mu    sigma    sigma 
  1763.912 1763.594 1763.292 1761.933 1761.646 1761.373 1760.615 1759.371 
        mu       mu       mu    sigma    sigma       mu       mu    sigma 
  1759.107 1758.855 1758.614 1757.906 1756.754 1756.521 1756.299 1755.650 
        mu       mu    sigma    sigma       mu       mu       mu    sigma 
  1755.432 1755.224 1754.151 1753.555 1753.354 1753.162 1752.977 1751.993 
     sigma       mu       mu       mu    sigma    sigma       mu       mu 
  1751.437 1751.259 1751.089 1750.925 1750.025 1749.507 1749.349 1749.182 
        mu    sigma    sigma       mu       mu       mu    sigma    sigma 
  1749.036 1748.201 1747.724 1747.584 1747.450 1747.321 1746.559 1746.116 
        mu       mu       mu    sigma 
  1745.992 1745.873 1745.759 1745.054 
  > risk(model, merge = FALSE)
  $mu
  [1] 4755.327 4746.600
  
  $sigma
  [1] 4755.327 4752.028 4749.214
  
  attr(,"class")
  [1] "inbag"
  > 
  > 
  > ## test that mstop = 0 is possible
  > compare_models <- function (m1, m2) {
  +   stopifnot(all.equal(coef(m1), coef(m2)))
  +   stopifnot(all.equal(predict(m1), predict(m2)))
  +   stopifnot(all.equal(fitted(m1), fitted(m2)))
  +   stopifnot(all.equal(selected(m1), selected(m2)))
  +   stopifnot(all.equal(risk(m1), risk(m2)))
  +   ## remove obvious differences from objects
  +   m1$control <- m2$control <- NULL
  +   m1$call <- m2$call <- NULL
  +   if (!all.equal(m1, m2))
  +     stop("Objects of offset model + 1 step and model with 1 step not identical")
  +   invisible(NULL)
  + }
  > 
  > # set up models
  > mod <- glmboostLSS(y ~ ., data = dat, method = "noncyclic", control = boost_control(mstop = 0))
  > mod2 <- glmboostLSS(y ~ ., data = dat, method = "noncyclic",  control = boost_control(mstop = 1))
  > mod3 <- glmboostLSS(y ~ ., data = dat, method = "noncyclic", control = boost_control(mstop = 1))
  > 
  > lapply(coef(mod), function(x) stopifnot(is.null(x)))
  $mu
  NULL
  
  $sigma
  NULL
  
  > 
  > mstop(mod3) <- 0
  > mapply(compare_models, m1 = mod, m2 = mod3)
  Error in !all.equal(m1, m2) : invalid argument type
  Calls: mapply -> <Anonymous>
  Execution halted