Running ‘test-pmodel-test.R’ [19s/28s]
  Running ‘test-pmodel.R’ [15s/24s]
  Running ‘test-pmtree.R’ [18s/27s]
  Comparing ‘test-pmtree.Rout’ to ‘test-pmtree.Rout.save’ ...95c95,102
< |   [2] age <= 50: n = 502
---
> |   [2] age <= 50
> |   |   [3] age <= 42: n = 153
> |   |       (Intercept)        trt2 
> |   |        -0.1300531   0.5027284 
> |   |   [4] age > 42: n = 371
> |   |       (Intercept)        trt2 
> |   |        -0.9932518   0.7773634 
> |   [5] age > 50: n = 476
97,100c104
< |         -1.162126    1.162126 
< |   [3] age > 50: n = 498
< |       (Intercept)        trt2 
< |         -1.034074    1.980880 
---
> |        -0.7958013   1.9056497 
102,103c106,107
< Number of inner nodes:    1
< Number of terminal nodes: 2
---
> Number of inner nodes:    2
> Number of terminal nodes: 3
105c109
< Objective function: 601.9864
---
> Objective function: 621.198
108c112,119
< |   [2] age <= 50: n = 502
---
> |   [2] age <= 50
> |   |   [3] age <= 42: n = 153
> |   |       (Intercept)        trt2 
> |   |        -0.1300531   0.5027284 
> |   |   [4] age > 42: n = 371
> |   |       (Intercept)        trt2 
> |   |        -0.9932518   0.7773634 
> |   [5] age > 50: n = 476
110,113c121
< |         -1.162126    1.162126 
< |   [3] age > 50: n = 498
< |       (Intercept)        trt2 
< |         -1.034074    1.980880 
---
> |        -0.7958013   1.9056497 
115,116c123,124
< Number of inner nodes:    1
< Number of terminal nodes: 2
---
> Number of inner nodes:    2
> Number of terminal nodes: 3
118c126
< Objective function: 601.9864
---
> Objective function: 621.198
122c130,137
< |   [2] age <= 50: n = 502
---
> |   [2] age <= 50
> |   |   [3] age <= 42: n = 153
> |   |       (Intercept)        trt2 
> |   |        -0.1300531   0.5027284 
> |   |   [4] age > 42: n = 371
> |   |       (Intercept)        trt2 
> |   |        -0.9932518   0.7773634 
> |   [5] age > 50: n = 476
124,127c139
< |         -1.162126    1.162126 
< |   [3] age > 50: n = 498
< |       (Intercept)        trt2 
< |         -1.034074    1.980880 
---
> |        -0.7958013   1.9056497 
129,130c141,142
< Number of inner nodes:    1
< Number of terminal nodes: 2
---
> Number of inner nodes:    2
> Number of terminal nodes: 3
132c144
< Objective function: 601.9864
---
> Objective function: 621.198
136c148,155
< |   [2] age <= 50: n = 22
---
> |   [2] age <= 50
> |   |   [3] age <= 43: n = 8
> |   |       (Intercept)        trt2 
> |   |        -0.4367177   0.7420993 
> |   |   [4] age > 43: n = 14
> |   |       (Intercept)        trt2 
> |   |        -0.9253406   0.6662319 
> |   [5] age > 50: n = 20
138,141c157
< |         -1.162126    1.162126 
< |   [3] age > 50: n = 20
< |       (Intercept)        trt2 
< |         -1.034074    1.980880 
---
> |        -0.7958013   1.9056497 
143,144c159,160
< Number of inner nodes:    1
< Number of terminal nodes: 2
---
> Number of inner nodes:    2
> Number of terminal nodes: 3
146c162
< Objective function: 91.09863
---
> Objective function: 94.40128
156,159c172,179
< |   [2] age <= 50: n = 502
< |       (Intercept)        trt2 
< |         -1.162126    1.162126 
< |   [3] age > 50: n = 498
---
> |   [2] age <= 50
> |   |   [3] age <= 42: n = 153
> |   |       (Intercept)        trt2 
> |   |        -0.1300531   0.5027284 
> |   |   [4] age > 42: n = 371
> |   |       (Intercept)        trt2 
> |   |        -0.9932518   0.7773634 
> |   [5] age > 50: n = 476
161c181
< |         -1.034074    1.980880 
---
> |        -0.7958013   1.9056497 
163,164c183,184
< Number of inner nodes:    1
< Number of terminal nodes: 2
---
> Number of inner nodes:    2
> Number of terminal nodes: 3
166c186
< Objective function (negative log-likelihood): 601.9864
---
> Objective function (negative log-likelihood): 621.198
180,181c200,201
< statistic 1.446675e+01
< p.value   7.220786e-04
---
> statistic 1.754685e+01
> p.value   1.547928e-04
185,186c205,206
< statistic 2.0885768
< p.value   0.3519422
---
> statistic 9.609115153
> p.value   0.008192325
190,191c210,221
< statistic 4.1541909
< p.value   0.1252936
---
> statistic 0.7184773
> p.value   0.6982077
> 
> $`4`
>                 age
> statistic 2.0024795
> p.value   0.3674237
> 
> $`5`
>                 age
> statistic 1.1781017
> p.value   0.5548537
196,197c226,227
< statistic 7.19313420
< p.value   0.02741768
---
> statistic 7.40146997
> p.value   0.02470536
201,202c231,232
< statistic 2.7056665
< p.value   0.2585068
---
> statistic 8.03869435
> p.value   0.01796469
204a235,240
> NULL
> 
> $`4`
> NULL
> 
> $`5`
206,207c242,243
< statistic 3.6772322
< p.value   0.1590374
---
> statistic 1.1198024
> p.value   0.5712655
212c248
< 'log Lik.' -601.9864 (df=5)
---
> 'log Lik.' -621.198 (df=8)
214c250
< 'log Lik.' -601.9864 (df=4)
---
> 'log Lik.' -621.198 (df=6)
217c253
< [1] -601.9864
---
> [1] -621.198
219c255
< [1] -601.9864
---
> [1] -621.198
221c257
< [1] -601.9864
---
> [1] -621.198
223c259
< [1] -601.9864
---
> [1] -621.198
228c264
< 'log Lik.' -601.9864 (df=4)
---
> 'log Lik.' -621.198 (df=6)
230c266
< 'log Lik.' -623.9381 (df=NA)
---
> 'log Lik.' -674.0438 (df=NA)
236c272
< 'log Lik.' 24.60555 (df=4)
---
> 'log Lik.' 55.4331 (df=6)
343c379
< |   |   [3] tests <= 57.69231: n = 124
---
> |   |   [3] tests <= 57.69231: n = 123
345c381
< |   |         42.603550   -6.488818 
---
> |   |         41.586538   -5.471806 
348c384
< |   |         54.967949   -1.729082 
---
> |   |         54.807692   -1.568826 
353c389
< |   |   [7] tests > 92.30769: n = 88
---
> |   |   [7] tests > 92.30769: n = 89
355c391
< |   |          87.73389   -10.65998 
---
> |   |          89.06883   -11.99492 
360c396
< Objective function: -282919.6
---
> Objective function: -276926.2
401c437
< 184270 
---
> 167022.9 
413c449
< 247262.1814  67543.0073  18595.5202   3297.5797    959.4839 
---
> 269734.5165  72661.7239  15878.6172   2973.8659    932.7745 
507,516c543,550
< 1: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 2: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 3: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 4: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 5: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   one or more coefficients may be infinite
---
> 1: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1 ; beta may be infinite. 
> 2: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 3: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 4: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269 ; beta may be infinite. 
520,525c554,557
< 1: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 2: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 3: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   one or more coefficients may be infinite
---
> 1: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 2: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269 ; beta may be infinite. 
529,538c561,568
< 1: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 2: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 3: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 4: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 5: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   one or more coefficients may be infinite
---
> 1: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 2: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,3 ; beta may be infinite. 
> 3: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 4: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269 ; beta may be infinite. 
542,549c572,577
< 1: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 2: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 3: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 4: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   one or more coefficients may be infinite
---
> 1: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 2: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 3: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269 ; beta may be infinite. 
553,562c581,588
< 1: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 2: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 3: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 4: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 5: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   one or more coefficients may be infinite
---
> 1: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 2: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,3 ; beta may be infinite. 
> 3: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 4: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269 ; beta may be infinite. 
566,573c592,597
< 1: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 2: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 3: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   Ran out of iterations and did not converge
< 4: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
<   one or more coefficients may be infinite
---
> 1: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 2: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2 ; beta may be infinite. 
> 3: In fitter(X, Y, strats, offset, init, control, weights = weights,  :
>   Loglik converged before variable  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269 ; beta may be infinite. 
Running the tests in ‘tests/test-pmodel.R’ failed.
Complete output:
  > library("model4you")
  Loading required package: partykit
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  > library("mvtnorm")
  > library("survival")
  > set.seed(123)
  > 
  > ## function to simulate the data
  > sim_data <- function(n = 500, p = 10, beta = 3, sd = 1){
  + 
  +   ## treatment
  +   lev <- c("C", "A")
  +   a <- rep(factor(lev, labels = lev, levels = lev), length = n)
  + 
  +   ## correlated z variables
  +   sigma <- diag(p)
  +   sigma[sigma == 0] <- 0.2
  +   ztemp <- rmvnorm(n, sigma = sigma)
  +   z <- (pnorm(ztemp) * 2 * pi) - pi
  +   colnames(z) <- paste0("z", 1:ncol(z))
  +   z1 <- z[,1]
  + 
  +   ## outcome
  +   y <- 7 + 0.2 * (a %in% "A") + beta * cos(z1) * (a %in% "A") + rnorm(n, 0, sd)
  + 
  +   data.frame(y = y, a = a, z)
  + }
  > 
  > ## simulate data
  > beta <- 3
  > ntrain <- 500
  > ntest <- 100
  > simdata <- simdata_s <- sim_data(p = 5, beta = beta, n = ntrain)
  > tsimdata <- tsimdata_s <- sim_data(p = 5, beta = beta, n = ntest)
  > simdata_s$cens <- rep(1, ntrain)
  > tsimdata_s$cens <- rep(1, ntest)
  > 
  > ## base model
  > basemodel_lm <- lm(y ~ a, data = simdata)
  > basemodel_wb <- survreg(Surv(y, cens) ~ a, data = simdata_s)
  > 
  > ## forest
  > frst_lm <- pmforest(basemodel_lm, ntree = 10,
  +                     perturb = list(replace = FALSE, fraction = 0.632),
  +                     control = ctree_control(mincriterion = 0, lookahead = TRUE))
  No data given. I'm using data set simdata from the current environment parent.frame(). Please check if that is what you want.
  > frst_wb <- pmforest(basemodel_wb, ntree = 10,
  +                     perturb = list(replace = FALSE, fraction = 0.632),
  +                     control = ctree_control(mincriterion = 0, lookahead = TRUE))
  No data given. I'm using data set simdata_s from the current environment parent.frame(). Please check if that is what you want.
  > 
  > ## personalised models
  > coefs_lm <- pmodel(x = frst_lm, newdata = tsimdata)
   ----------- FAILURE REPORT -------------- 
   --- failure: the condition has length > 1 ---
   --- srcref --- 
  : 
   --- package (from environment) --- 
  model4you
   --- call from context --- 
  pmodel(x = frst_lm, newdata = tsimdata)
   --- call from argument --- 
  if (class(ret) == "matrix") ret <- t(ret)
   --- R stacktrace ---
  where 1: pmodel(x = frst_lm, newdata = tsimdata)
  
   --- value of length: 2 type: logical ---
  [1]  TRUE FALSE
   --- function from context --- 
  function (x = NULL, model = NULL, newdata = NULL, OOB = TRUE, 
      fun = coef, return_attr = c("modelcall", "data", "similarity")) 
  {
      if (is.matrix(x)) {
          if (is.null(model)) 
              stop("When x is a matrix, model must not be NULL. Please enter a model object.")
          pweights <- x
      }
      else {
          if (is.null(model)) 
              model <- x$info$model
          pweights <- predict(x, type = "weights", newdata = newdata, 
              OOB = OOB)
      }
      get_pmod <- function(w) {
          if (sum(w) == 0) 
              stop("The weights for one observation are all 0. A solution may be increasing ntree.")
          dat <- x$data
          dat$w <- w
          pmod <- update(model, weights = w, subset = w > 0, data = dat)
          fun(pmod)
      }
      ret <- apply(pweights, 2, get_pmod)
      if (class(ret) == "matrix") 
          ret <- t(ret)
      if (all.equal(fun, identity) == TRUE) 
          class(ret) <- c("pmodel_identity", class(ret))
      class(ret) <- c("pmodel", class(ret))
      if ("modelcall" %in% return_attr) 
          attr(ret, "modelcall") <- getCall(model)
      if ("data" %in% return_attr) 
          if (is.null(newdata)) 
              attr(ret, "data") <- x$data
          else attr(ret, "data") <- newdata
      if ("similarity" %in% return_attr) 
          attr(ret, "similarity") <- pweights
      return(ret)
  }
  <bytecode: 0x564c12800f48>
  <environment: namespace:model4you>
   --- function search by body ---
  Function pmodel in namespace model4you has this body.
   ----------- END OF FAILURE REPORT -------------- 
  Fatal error: the condition has length > 1