Running examples in 'simTool-Ex.R' failed
The error most likely occurred in:

> ### Name: evalGrids
> ### Title: Workhorse for simulation studies
> ### Aliases: evalGrids
> 
> ### ** Examples
> 
> 
> rng = function(data, ...) {
+ ret = range(data)
+ names(ret) = c("min", "max")
+ ret
+ }
> 
> # call runif(n=1), runif(n=2), runif(n=3)
> # and range on the three "datasets"
> # generated by runif(n=1), runif(n=2), runif(n=3)
> eg = evalGrids(
+  expandGrid(fun="runif", n=1:3),
+  expandGrid(proc="rng"),
+  rep=10
+ )
Warning in evalGrids(expandGrid(fun = "runif", n = 1:3), expandGrid(proc = "rng"),  :
  The function evalGrids() is deprecated. Please use eval_tibbles() instead.
Warning in expandGrid(fun = "runif", n = 1:3) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
Warning in expandGrid(proc = "rng") :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
Warning in evalGrids(expandGrid(fun = "runif", n = 1:3), expandGrid(proc = "rng"),  :
  NAs introduced by coercion to integer range
[1] "Estimated replications per hour:  NA"
> eg
$call
evalGrids(dataGrid = expandGrid(fun = "runif", n = 1:3), procGrid = expandGrid(proc = "rng"), 
    replications = 10)

$dataGrid
    fun n
1 runif 1
2 runif 2
3 runif 3

$procGrid
  proc
1  rng

$simulation
$simulation[[1]]
$simulation[[1]][[1]]
$simulation[[1]][[1]]$data
[1] 0.2655087

$simulation[[1]][[1]]$results
$simulation[[1]][[1]]$results[[1]]
      min       max 
0.2655087 0.2655087 



$simulation[[1]][[2]]
$simulation[[1]][[2]]$data
[1] 0.3721239

$simulation[[1]][[2]]$results
$simulation[[1]][[2]]$results[[1]]
      min       max 
0.3721239 0.3721239 



$simulation[[1]][[3]]
$simulation[[1]][[3]]$data
[1] 0.5728534

$simulation[[1]][[3]]$results
$simulation[[1]][[3]]$results[[1]]
      min       max 
0.5728534 0.5728534 



$simulation[[1]][[4]]
$simulation[[1]][[4]]$data
[1] 0.9082078

$simulation[[1]][[4]]$results
$simulation[[1]][[4]]$results[[1]]
      min       max 
0.9082078 0.9082078 



$simulation[[1]][[5]]
$simulation[[1]][[5]]$data
[1] 0.2016819

$simulation[[1]][[5]]$results
$simulation[[1]][[5]]$results[[1]]
      min       max 
0.2016819 0.2016819 



$simulation[[1]][[6]]
$simulation[[1]][[6]]$data
[1] 0.8983897

$simulation[[1]][[6]]$results
$simulation[[1]][[6]]$results[[1]]
      min       max 
0.8983897 0.8983897 



$simulation[[1]][[7]]
$simulation[[1]][[7]]$data
[1] 0.9446753

$simulation[[1]][[7]]$results
$simulation[[1]][[7]]$results[[1]]
      min       max 
0.9446753 0.9446753 



$simulation[[1]][[8]]
$simulation[[1]][[8]]$data
[1] 0.6607978

$simulation[[1]][[8]]$results
$simulation[[1]][[8]]$results[[1]]
      min       max 
0.6607978 0.6607978 



$simulation[[1]][[9]]
$simulation[[1]][[9]]$data
[1] 0.629114

$simulation[[1]][[9]]$results
$simulation[[1]][[9]]$results[[1]]
     min      max 
0.629114 0.629114 



$simulation[[1]][[10]]
$simulation[[1]][[10]]$data
[1] 0.06178627

$simulation[[1]][[10]]$results
$simulation[[1]][[10]]$results[[1]]
       min        max 
0.06178627 0.06178627 




$simulation[[2]]
$simulation[[2]][[1]]
$simulation[[2]][[1]]$data
[1] 0.2059746 0.1765568

$simulation[[2]][[1]]$results
$simulation[[2]][[1]]$results[[1]]
      min       max 
0.1765568 0.2059746 



$simulation[[2]][[2]]
$simulation[[2]][[2]]$data
[1] 0.6870228 0.3841037

$simulation[[2]][[2]]$results
$simulation[[2]][[2]]$results[[1]]
      min       max 
0.3841037 0.6870228 



$simulation[[2]][[3]]
$simulation[[2]][[3]]$data
[1] 0.7698414 0.4976992

$simulation[[2]][[3]]$results
$simulation[[2]][[3]]$results[[1]]
      min       max 
0.4976992 0.7698414 



$simulation[[2]][[4]]
$simulation[[2]][[4]]$data
[1] 0.7176185 0.9919061

$simulation[[2]][[4]]$results
$simulation[[2]][[4]]$results[[1]]
      min       max 
0.7176185 0.9919061 



$simulation[[2]][[5]]
$simulation[[2]][[5]]$data
[1] 0.3800352 0.7774452

$simulation[[2]][[5]]$results
$simulation[[2]][[5]]$results[[1]]
      min       max 
0.3800352 0.7774452 



$simulation[[2]][[6]]
$simulation[[2]][[6]]$data
[1] 0.9347052 0.2121425

$simulation[[2]][[6]]$results
$simulation[[2]][[6]]$results[[1]]
      min       max 
0.2121425 0.9347052 



$simulation[[2]][[7]]
$simulation[[2]][[7]]$data
[1] 0.6516738 0.1255551

$simulation[[2]][[7]]$results
$simulation[[2]][[7]]$results[[1]]
      min       max 
0.1255551 0.6516738 



$simulation[[2]][[8]]
$simulation[[2]][[8]]$data
[1] 0.2672207 0.3861141

$simulation[[2]][[8]]$results
$simulation[[2]][[8]]$results[[1]]
      min       max 
0.2672207 0.3861141 



$simulation[[2]][[9]]
$simulation[[2]][[9]]$data
[1] 0.01339033 0.38238796

$simulation[[2]][[9]]$results
$simulation[[2]][[9]]$results[[1]]
       min        max 
0.01339033 0.38238796 



$simulation[[2]][[10]]
$simulation[[2]][[10]]$data
[1] 0.8696908 0.3403490

$simulation[[2]][[10]]$results
$simulation[[2]][[10]]$results[[1]]
      min       max 
0.3403490 0.8696908 




$simulation[[3]]
$simulation[[3]][[1]]
$simulation[[3]][[1]]$data
[1] 0.4820801 0.5995658 0.4935413

$simulation[[3]][[1]]$results
$simulation[[3]][[1]]$results[[1]]
      min       max 
0.4820801 0.5995658 



$simulation[[3]][[2]]
$simulation[[3]][[2]]$data
[1] 0.1862176 0.8273733 0.6684667

$simulation[[3]][[2]]$results
$simulation[[3]][[2]]$results[[1]]
      min       max 
0.1862176 0.8273733 



$simulation[[3]][[3]]
$simulation[[3]][[3]]$data
[1] 0.7942399 0.1079436 0.7237109

$simulation[[3]][[3]]$results
$simulation[[3]][[3]]$results[[1]]
      min       max 
0.1079436 0.7942399 



$simulation[[3]][[4]]
$simulation[[3]][[4]]$data
[1] 0.4112744 0.8209463 0.6470602

$simulation[[3]][[4]]$results
$simulation[[3]][[4]]$results[[1]]
      min       max 
0.4112744 0.8209463 



$simulation[[3]][[5]]
$simulation[[3]][[5]]$data
[1] 0.7829328 0.5530363 0.5297196

$simulation[[3]][[5]]$results
$simulation[[3]][[5]]$results[[1]]
      min       max 
0.5297196 0.7829328 



$simulation[[3]][[6]]
$simulation[[3]][[6]]$data
[1] 0.7893562 0.0233312 0.4772301

$simulation[[3]][[6]]$results
$simulation[[3]][[6]]$results[[1]]
      min       max 
0.0233312 0.7893562 



$simulation[[3]][[7]]
$simulation[[3]][[7]]$data
[1] 0.7323137 0.6927316 0.4776196

$simulation[[3]][[7]]$results
$simulation[[3]][[7]]$results[[1]]
      min       max 
0.4776196 0.7323137 



$simulation[[3]][[8]]
$simulation[[3]][[8]]$data
[1] 0.8612095 0.4380971 0.2447973

$simulation[[3]][[8]]$results
$simulation[[3]][[8]]$results[[1]]
      min       max 
0.2447973 0.8612095 



$simulation[[3]][[9]]
$simulation[[3]][[9]]$data
[1] 0.07067905 0.09946616 0.31627171

$simulation[[3]][[9]]$results
$simulation[[3]][[9]]$results[[1]]
       min        max 
0.07067905 0.31627171 



$simulation[[3]][[10]]
$simulation[[3]][[10]]$data
[1] 0.5186343 0.6620051 0.4068302

$simulation[[3]][[10]]$results
$simulation[[3]][[10]]$results[[1]]
      min       max 
0.4068302 0.6620051 





$summary.fun
NULL

$est.reps.per.hour
[1] NA

$sessionInfo
R version 3.4.4 (2018-03-15)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows Server 2008 x64 (build 6002) Service Pack 2

Matrix products: default

locale:
[1] LC_COLLATE=C                 LC_CTYPE=German_Germany.1252
[3] LC_MONETARY=C                LC_NUMERIC=C                
[5] LC_TIME=C                   

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] simTool_1.1.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     tidyr_0.8.2      reshape_0.8.8    crayon_1.3.4    
 [5] dplyr_0.7.7      assertthat_0.2.0 plyr_1.8.4       R6_2.3.0        
 [9] magrittr_1.5     pillar_1.3.0     rlang_0.3.0.1    bindrcpp_0.2.2  
[13] glue_1.3.0       purrr_0.2.5      compiler_3.4.4   pkgconfig_2.0.2 
[17] bindr_0.1.1      tidyselect_0.2.5 tibble_1.4.2    

attr(,"class")
[1] "evalGrid"
> 
> # summarizing the results in a data.frame
> as.data.frame(eg)
Warning in as.data.frame.evalGrid(eg) :
  The function as.data.frame.evalGrid() is deprecated. Please use eval_tibbles, since it always returns a tibble-object.
Warning in expandGrid(i = 1:nrow(dataGrid), j = 1:nrow(procGrid)) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
   i j   fun n proc replication        min        max
1  1 1 runif 1  rng           1 0.26550866 0.26550866
2  1 1 runif 1  rng           2 0.37212390 0.37212390
3  1 1 runif 1  rng           3 0.57285336 0.57285336
4  1 1 runif 1  rng           4 0.90820779 0.90820779
5  1 1 runif 1  rng           5 0.20168193 0.20168193
6  1 1 runif 1  rng           6 0.89838968 0.89838968
7  1 1 runif 1  rng           7 0.94467527 0.94467527
8  1 1 runif 1  rng           8 0.66079779 0.66079779
9  1 1 runif 1  rng           9 0.62911404 0.62911404
10 1 1 runif 1  rng          10 0.06178627 0.06178627
11 2 1 runif 2  rng           1 0.17655675 0.20597457
12 2 1 runif 2  rng           2 0.38410372 0.68702285
13 2 1 runif 2  rng           3 0.49769924 0.76984142
14 2 1 runif 2  rng           4 0.71761851 0.99190609
15 2 1 runif 2  rng           5 0.38003518 0.77744522
16 2 1 runif 2  rng           6 0.21214252 0.93470523
17 2 1 runif 2  rng           7 0.12555510 0.65167377
18 2 1 runif 2  rng           8 0.26722067 0.38611409
19 2 1 runif 2  rng           9 0.01339033 0.38238796
20 2 1 runif 2  rng          10 0.34034900 0.86969085
21 3 1 runif 3  rng           1 0.48208012 0.59956583
22 3 1 runif 3  rng           2 0.18621760 0.82737332
23 3 1 runif 3  rng           3 0.10794363 0.79423986
24 3 1 runif 3  rng           4 0.41127443 0.82094629
25 3 1 runif 3  rng           5 0.52971958 0.78293276
26 3 1 runif 3  rng           6 0.02333120 0.78935623
27 3 1 runif 3  rng           7 0.47761962 0.73231374
28 3 1 runif 3  rng           8 0.24479728 0.86120948
29 3 1 runif 3  rng           9 0.07067905 0.31627171
30 3 1 runif 3  rng          10 0.40683019 0.66200508
> 
> # we now generate data for a regression
> # and fit different regression models
> 
> # not that we use SD and not sd (the
> # reason for this is the cast() call below)
> regData = function(n, SD){
+  data.frame(
+    x=seq(0,1,length=n),
+    y=rnorm(n, sd=SD))
+ }
> 
> eg = evalGrids(
+  expandGrid(fun="regData", n=20, SD=1:2),
+  expandGrid(proc="lm", formula=c("y~x", "y~I(x^2)")),
+  replications=2)
Warning in evalGrids(expandGrid(fun = "regData", n = 20, SD = 1:2), expandGrid(proc = "lm",  :
  The function evalGrids() is deprecated. Please use eval_tibbles() instead.
Warning in expandGrid(fun = "regData", n = 20, SD = 1:2) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
Warning in expandGrid(proc = "lm", formula = c("y~x", "y~I(x^2)")) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
[1] "Estimated replications per hour:  461539"
> 
> # can not be converted to data.frame, because
> # an object of class "lm" can not converted to
> # a data.frame
> try(as.data.frame(eg))
Warning in as.data.frame.evalGrid(eg) :
  The function as.data.frame.evalGrid() is deprecated. Please use eval_tibbles, since it always returns a tibble-object.
Warning in expandGrid(i = 1:nrow(dataGrid), j = 1:nrow(procGrid)) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
Error in list_to_dataframe(res, attr(.data, "split_labels"), .id, id_as_factor) : 
  Results must be all atomic, or all data frames
> 
> # for the data.frame we just extract the r.squared
> # from the fitted model
> as.data.frame(eg, convert.result.fun=function(fit) c(rsq=summary(fit)$r.squared))
Warning in as.data.frame.evalGrid(eg, convert.result.fun = function(fit) c(rsq = summary(fit)$r.squared)) :
  The function as.data.frame.evalGrid() is deprecated. Please use eval_tibbles, since it always returns a tibble-object.
Warning in expandGrid(i = 1:nrow(dataGrid), j = 1:nrow(procGrid)) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
  i j     fun  n SD proc  formula replication         rsq
1 1 1 regData 20  1   lm      y~x           1 0.006230915
2 1 1 regData 20  1   lm      y~x           2 0.012593478
3 1 2 regData 20  1   lm y~I(x^2)           1 0.020538264
4 1 2 regData 20  1   lm y~I(x^2)           2 0.016250553
5 2 1 regData 20  2   lm      y~x           1 0.074139331
6 2 1 regData 20  2   lm      y~x           2 0.016003732
7 2 2 regData 20  2   lm y~I(x^2)           1 0.096954084
8 2 2 regData 20  2   lm y~I(x^2)           2 0.071679039
> 
> # for the data.frame we just extract the coefficients
> # from the fitted model
> df = as.data.frame(eg, convert.result.fun=coef)
Warning in as.data.frame.evalGrid(eg, convert.result.fun = coef) :
  The function as.data.frame.evalGrid() is deprecated. Please use eval_tibbles, since it always returns a tibble-object.
Warning in expandGrid(i = 1:nrow(dataGrid), j = 1:nrow(procGrid)) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
> 
> # since we have done 2 replication we can calculate
> # sum summary statistics
> library("reshape")
> df$replication=NULL
> mdf = melt(df, id=1:7, na.rm=TRUE)
> cast(mdf, ... ~ ., c(mean, length, sd))
  i j     fun  n SD proc  formula    variable        mean length         sd
1 1 1 regData 20  1   lm      y~x (Intercept)  0.06587478      2 0.03685978
2 1 1 regData 20  1   lm      y~x           x  0.29301730      2 0.16655528
3 1 2 regData 20  1   lm y~I(x^2) (Intercept)  0.08279561      2 0.08546611
4 1 2 regData 20  1   lm y~I(x^2)      I(x^2)  0.37879517      2 0.10134689
5 2 1 regData 20  2   lm      y~x (Intercept) -0.34693411      2 0.57923435
6 2 1 regData 20  2   lm      y~x           x  1.03331387      2 0.33794571
7 2 2 regData 20  2   lm y~I(x^2) (Intercept) -0.34810472      2 0.35744226
8 2 2 regData 20  2   lm y~I(x^2)      I(x^2)  1.51364975      2 0.15439470
> 
> # note if the data.frame would contain the column
> # named "sd" instead of "SD" the cast will generate
> # an error
> names(df)[5] = "sd"
> mdf = melt(df, id=1:7, na.rm=TRUE)
> try(cast(mdf, ... ~ ., c(mean, length, sd)))
Error in `[.data.frame`(data, , vars, drop = FALSE) : 
  undefined columns selected
> 
> 
> # extracting the summary of the fitted.model
> as.data.frame(eg, convert.result.fun=function(x) {
+  ret = coef(summary(x))
+  data.frame(valueName = rownames(ret), ret, check.names=FALSE)
+ })
Warning in as.data.frame.evalGrid(eg, convert.result.fun = function(x) { :
  The function as.data.frame.evalGrid() is deprecated. Please use eval_tibbles, since it always returns a tibble-object.
Warning in expandGrid(i = 1:nrow(dataGrid), j = 1:nrow(procGrid)) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
   i j     fun  n SD proc  formula replication   valueName    Estimate
1  1 1 regData 20  1   lm      y~x           1 (Intercept)  0.03981098
2  1 1 regData 20  1   lm      y~x           1           x  0.17524494
3  1 1 regData 20  1   lm      y~x           2 (Intercept)  0.09193858
4  1 1 regData 20  1   lm      y~x           2           x  0.41078966
5  1 2 regData 20  1   lm y~I(x^2)           1 (Intercept)  0.02236194
6  1 2 regData 20  1   lm y~I(x^2)           1      I(x^2)  0.30713210
7  1 2 regData 20  1   lm y~I(x^2)           2 (Intercept)  0.14322927
8  1 2 regData 20  1   lm y~I(x^2)           2      I(x^2)  0.45045824
9  2 1 regData 20  2   lm      y~x           1 (Intercept) -0.75651465
10 2 1 regData 20  2   lm      y~x           1           x  1.27227758
11 2 1 regData 20  2   lm      y~x           2 (Intercept)  0.06264643
12 2 1 regData 20  2   lm      y~x           2           x  0.79435017
13 2 2 regData 20  2   lm y~I(x^2)           1 (Intercept) -0.60085456
14 2 2 regData 20  2   lm y~I(x^2)           1      I(x^2)  1.40447621
15 2 2 regData 20  2   lm y~I(x^2)           2 (Intercept) -0.09535488
16 2 2 regData 20  2   lm y~I(x^2)           2      I(x^2)  1.62282329
   Std. Error     t value  Pr(>|t|)
1   0.3051096  0.13048089 0.8976331
2   0.5216462  0.33594597 0.7407959
3   0.5014623  0.18334096 0.8565799
4   0.8573505  0.47913853 0.6376094
5   0.2322758  0.09627321 0.9243674
6   0.4999204  0.61436205 0.5466639
7   0.3838218  0.37316611 0.7133831
8   0.8260883  0.54529069 0.5922471
9   0.6198320 -1.22051566 0.2380181
10  1.0597273  1.20057077 0.2454797
11  0.8587005  0.07295493 0.9426466
12  1.4681209  0.54106590 0.5950957
13  0.4694109 -1.28001845 0.2167902
14  1.0102992  1.39015873 0.1814335
15  0.6395745 -0.14909113 0.8831394
16  1.3765373  1.17891708 0.2537806
> 
> 
> 
> # we now compare to methods for
> # calculating quantiles
> 
> # the functions and parameters
> # that generate the data
> N = c(10, 50, 100)
> library("plyr")

Attaching package: 'plyr'

The following objects are masked from 'package:reshape':

    rename, round_any

> dg = rbind.fill(
+  expandGrid(fun="rbeta", n=N, shape1=4, shape2=4),
+  expandGrid(fun="rnorm", n=N))
Warning in expandGrid(fun = "rbeta", n = N, shape1 = 4, shape2 = 4) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
Warning in expandGrid(fun = "rnorm", n = N) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
> 
> # definition of the two quantile methods
> emp.q = function(data, probs) c(quantile(data, probs=probs))
> nor.q = function(data, probs) {
+  ret = qnorm(probs, mean=mean(data), sd=sd(data))
+  names(ret) = names(quantile(1, probs=probs))
+  ret
+ }
> 
> # the functions and parameters that are
> # applied to the generate data
> pg = rbind.fill(expandGrid(proc=c("emp.q", "nor.q"), probs=c(0.01, 0.025, 0.05)))
Warning in expandGrid(proc = c("emp.q", "nor.q"), probs = c(0.01, 0.025,  :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
> 
> # generate data and apply quantile methods
> set.seed(1234)
> eg = evalGrids(dg, pg, replication=50, progress=TRUE)
Warning in evalGrids(dg, pg, replication = 50, progress = TRUE) :
  The function evalGrids() is deprecated. Please use eval_tibbles() instead.

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[1] "Estimated replications per hour:  274725"
> 
> # convert the results to a data.frame
> df = as.data.frame(eg)
Warning in as.data.frame.evalGrid(eg) :
  The function as.data.frame.evalGrid() is deprecated. Please use eval_tibbles, since it always returns a tibble-object.
Warning in expandGrid(i = 1:nrow(dataGrid), j = 1:nrow(procGrid)) :
  The function expandGrid() is deprecated. Please use expand_tibble() instead.
> df$replication=NULL
> mdf = melt(df, id=1:8, na.rm=TRUE)
> 
> # calculate, print and plot summary statistics
> require("ggplot2")
Loading required package: ggplot2
> print(a <- arrange(cast(mdf, ... ~ ., c(mean, sd)), n))
   i j   fun   n shape1 shape2  proc probs variable       mean         sd
1  1 1 rbeta  10      4      4 emp.q 0.010       1%  0.2539288 0.07537514
2  1 2 rbeta  10      4      4 nor.q 0.010       1%  0.1209108 0.08786648
3  1 3 rbeta  10      4      4 emp.q 0.025     2.5%  0.2652816 0.07069832
4  1 4 rbeta  10      4      4 nor.q 0.025     2.5%  0.1804699 0.07845840
5  1 5 rbeta  10      4      4 emp.q 0.050       5%  0.2842030 0.06624867
6  1 6 rbeta  10      4      4 nor.q 0.050       5%  0.2316941 0.07118579
7  4 1 rnorm  10     NA     NA emp.q 0.010       1% -1.4093751 0.60676788
8  4 2 rnorm  10     NA     NA nor.q 0.010       1% -2.1193599 0.68058063
9  4 3 rnorm  10     NA     NA emp.q 0.025     2.5% -1.3463994 0.57190655
10 4 4 rnorm  10     NA     NA nor.q 0.025     2.5% -1.7862919 0.60236540
11 4 5 rnorm  10     NA     NA emp.q 0.050       5% -1.2414398 0.52309655
12 4 6 rnorm  10     NA     NA nor.q 0.050       5% -1.4998351 0.53718549
13 2 1 rbeta  50      4      4 emp.q 0.010       1%  0.1860748 0.03339001
14 2 2 rbeta  50      4      4 nor.q 0.010       1%  0.1244904 0.03882476
15 2 3 rbeta  50      4      4 emp.q 0.025     2.5%  0.2129376 0.03178980
16 2 4 rbeta  50      4      4 nor.q 0.025     2.5%  0.1844740 0.03418612
17 2 5 rbeta  50      4      4 emp.q 0.050       5%  0.2450181 0.03301217
18 2 6 rbeta  50      4      4 nor.q 0.050       5%  0.2360632 0.03045628
19 5 1 rnorm  50     NA     NA emp.q 0.010       1% -2.0607116 0.31693024
20 5 2 rnorm  50     NA     NA nor.q 0.010       1% -2.2798042 0.24275004
21 5 3 rnorm  50     NA     NA emp.q 0.025     2.5% -1.8207912 0.26991716
22 5 4 rnorm  50     NA     NA nor.q 0.025     2.5% -1.9196401 0.21697542
23 5 5 rnorm  50     NA     NA emp.q 0.050       5% -1.5699650 0.25649225
24 5 6 rnorm  50     NA     NA nor.q 0.050       5% -1.6098791 0.19656107
25 3 1 rbeta 100      4      4 emp.q 0.010       1%  0.1654696 0.03700622
26 3 2 rbeta 100      4      4 nor.q 0.010       1%  0.1135081 0.02406594
27 3 3 rbeta 100      4      4 emp.q 0.025     2.5%  0.1938219 0.03311811
28 3 4 rbeta 100      4      4 nor.q 0.025     2.5%  0.1745692 0.02187205
29 3 5 rbeta 100      4      4 emp.q 0.050       5%  0.2323030 0.02627427
30 3 6 rbeta 100      4      4 nor.q 0.050       5%  0.2270852 0.02019063
31 6 1 rnorm 100     NA     NA emp.q 0.010       1% -2.1039795 0.27732120
32 6 2 rnorm 100     NA     NA nor.q 0.010       1% -2.3407961 0.18644684
33 6 3 rnorm 100     NA     NA emp.q 0.025     2.5% -1.8487720 0.20789087
34 6 4 rnorm 100     NA     NA nor.q 0.025     2.5% -1.9757958 0.16694384
35 6 5 rnorm 100     NA     NA emp.q 0.050       5% -1.6148406 0.18381739
36 6 6 rnorm 100     NA     NA nor.q 0.050       5% -1.6618754 0.15134804
> ggplot(a, aes(x=fun, y=mean, color=proc)) + geom_point(size=I(3)) + facet_grid(probs ~ n)
Error in namespaceExport(ns, exports) : 
  undefined exports: %s!=%, %s!==%, %s+%, %s<%, %s<=%, %s==%, %s===%, %s>%, %s>=%, %stri!=%, %stri!==%, %stri+%, %stri<%, %stri<=%, %stri==%, %stri===%, %stri>%, %stri>=%, stri_datetime_add<-, stri_sub<-, stri_subset<-, stri_subset_charclass<-, stri_subset_coll<-, stri_subset_fixed<-, stri_subset_regex<-, stri_c, stri_c_list, stri_cmp, stri_cmp_eq, stri_cmp_equiv, stri_cmp_ge, stri_cmp_gt, stri_cmp_le, stri_cmp_lt, stri_cmp_neq, stri_cmp_nequiv, stri_compare, stri_conv, stri_count, stri_count_boundaries, stri_count_charclass, stri_count_coll, stri_count_fixed, stri_count_regex, stri_count_words, stri_datetime_add, stri_datetime_create, stri_datetime_fields, stri_datetime_format, stri_datetime_fstr, stri_datetime_now, stri_datetime_parse, stri_datetime_symbols, stri_detect, stri_detect_charclass, stri_detect_coll, stri_detect_fixed, stri_detect_regex, stri_dup, stri_duplicated, stri_duplicated_any, stri_enc_detect, stri_enc_detect2, stri_enc_fromutf32, stri_enc_get, stri_enc_inf
Calls: <Anonymous> ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
Execution halted