Last updated on 2020-05-24 23:47:55 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.9.14 | 14.45 | 443.62 | 458.07 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.9.14 | 12.45 | 247.78 | 260.23 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.9.14 | 431.23 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 0.9.14 | 367.11 | NOTE | |||
| r-devel-windows-ix86+x86_64 | 0.9.14 | 39.00 | 346.00 | 385.00 | OK | |
| r-patched-linux-x86_64 | 0.9.14 | 13.99 | 342.09 | 356.08 | OK | |
| r-patched-solaris-x86 | 0.9.14 | 316.80 | ERROR | |||
| r-release-linux-x86_64 | 0.9.14 | 13.80 | 336.83 | 350.63 | OK | |
| r-release-osx-x86_64 | 0.9.14 | NOTE | ||||
| r-release-windows-ix86+x86_64 | 0.9.14 | 36.00 | 354.00 | 390.00 | OK | |
| r-oldrel-osx-x86_64 | 0.9.14 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 0.9.14 | 24.00 | 392.00 | 416.00 | OK |
Version: 0.9.14
Check: dependencies in R code
Result: NOTE
Namespaces in Imports field not imported from:
‘expm’ ‘matrixStats’ ‘matrixcalc’
All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64
Version: 0.9.14
Check: tests
Result: ERROR
Running ‘invariance.R’ [8s/13s]
Running ‘lavaan.R’ [57s/68s]
Running ‘tree.R’ [12s/41s]
Running ‘vim.R’ [12s/40s]
Running the tests in ‘tests/invariance.R’ failed.
Complete output:
> #
> # testing invariance
> #
>
> # simulation:
> # factor structure holds over SES but not over age
> # both SES and age predict a mean difference in cognitive outcome
>
> set.seed(123)
>
> require("semtree")
Loading required package: semtree
Loading required package: OpenMx
To take full advantage of multiple cores, use:
mxOption(key='Number of Threads', value=parallel::detectCores()) #now
Sys.setenv(OMP_NUM_THREADS=parallel::detectCores()) #before library(OpenMx)
> require("lavaan")
Loading required package: lavaan
This is lavaan 0.6-6
lavaan is BETA software! Please report any bugs.
Attaching package: 'lavaan'
The following object is masked from 'package:OpenMx':
vech
> #
> lambda <- list()
> age <- c(0,0,1,1) # 0=young, 1 =old
> ses <- c(0,1,0,1) # 0=low, 1=high
> lambda[[1]] <- c(1,0.9,0.8,0.8)
> lambda[[2]] <- c(1,0.9,0.8,0.8)
> lambda[[3]] <- c(1,0.4,0.2,0.9)
> lambda[[4]] <- c(1,0.4,0.2,0.9)
> cogmean <- 80-age*30 + ses*20
> cogsd <- 1
> errsd <- 1
>
> Nsub <- 200 # persons per agexSES group
>
> cbind(age,ses,cogmean)
age ses cogmean
[1,] 0 0 80
[2,] 0 1 100
[3,] 1 0 50
[4,] 1 1 70
>
> # simulate data from 4 groups
> data <- c()
> for (i in 1:4) {
+ cogsim <- rnorm(n = Nsub,mean = cogmean[i],cogsd)
+ scores <- as.matrix(t(outer(lambda[[i]],cogsim))) + rnorm(Nsub*4,0,errsd)
+ data <- rbind(data,scores)
+ }
> data <- data.frame(data)
> names(data) <- paste0("x",1:4)
>
> fulldata <- cbind(data, age=rep(age,each=Nsub),ses=rep(ses,each=Nsub))
> #
> model<-"
+ ! regressions
+ F=~1.0*x1
+ F=~l2*x2
+ F=~l3*x3
+ F=~l4*x4
+ ! residuals, variances and covariances
+ x1 ~~ VAR_x1*x1
+ x2 ~~ VAR_x2*x2
+ x3 ~~ VAR_x3*x3
+ x4 ~~ VAR_x4*x4
+ F ~~ 1.0*F
+ ! means
+ F~1
+ x1~0*1;
+ x2~0*1;
+ x3~0*1;
+ x4~0*1;
+ ";
> result<-lavaan(model, data=data, fixed.x=FALSE, missing="FIML");
Warning message:
In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
>
> manifests<-c("x1","x2","x3","x4")
> latents<-c("F")
> model <- mxModel("Unnamed_Model",
+ type="RAM",
+ manifestVars = manifests,
+ latentVars = latents,
+ mxPath(from="F",to=c("x1","x2","x3","x4"), free=c(FALSE,TRUE,TRUE,TRUE),
+ value=c(1.0,1.0,1.0,1.0) , arrows=1, label=c("F__x1","l2","l3","l4") ),
+ mxPath(from="one",to=c("F"), free=c(TRUE), value=c(1.0) , arrows=1, label=c("const__F") ),
+ mxPath(from="one",to=c("x2","x3","x4"), free=c(TRUE,TRUE,TRUE), value=c(1.0,1.0,1.0) , arrows=1, label=c("const__x2","const__x3","const__x4") ),
+ mxPath(from="x1",to=c("x1"), free=c(TRUE), value=c(1.0) , arrows=2, label=c("VAR_x1") ),
+ mxPath(from="x2",to=c("x2"), free=c(TRUE), value=c(1.0) , arrows=2, label=c("VAR_x2") ),
+ mxPath(from="x3",to=c("x3"), free=c(TRUE), value=c(1.0) , arrows=2, label=c("VAR_x3") ),
+ mxPath(from="x4",to=c("x4"), free=c(TRUE), value=c(1.0) , arrows=2, label=c("VAR_x4") ),
+ mxPath(from="F",to=c("F"), free=c(FALSE), value=c(1.0) , arrows=2, label=c("VAR_F") ),
+ mxPath(from="one",to=c("x1"), free=F, value=0, arrows=1),
+ mxData(data[1:50,], type = "raw")
+ );
>
> result <- mxTryHard(model)
Running Unnamed_Model with 11 parameters
Beginning initial fit attempt
Running Unnamed_Model with 11 parameters
Lowest minimum so far: 3260.1298832466
OpenMx status code 6 not in list of acceptable status codes, (0,0)
Not all eigenvalues of the Hessian are positive: 5375522957.59402, 87.6853234559546, 70.3845137330389, 65.0207403866546, 26.6999821190647, 1.43677988336926, 2.60947714481234e-05, 1.21790590566928e-06, 4.24664110530932e-15, -1.1128646543663e-10, -5396883230.77566
Beginning fit attempt 1 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Fit attempt worse than current best: 3260.12989648096 vs 3260.1298832466
Beginning fit attempt 2 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Fit attempt generated errors
Beginning fit attempt 3 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Fit attempt worse than current best: 3272.28969776554 vs 3260.1298832466
Beginning fit attempt 4 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Fit attempt generated errors
Beginning fit attempt 5 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Lowest minimum so far: 3235.57170981942
OpenMx status code 6 not in list of acceptable status codes, (0,0)
Not all eigenvalues of the Hessian are positive: 5143045997.65345, 87.6855773812779, 70.5666123989302, 65.0217647312684, 26.7518236915981, 1.91640472921186, 7.19965100405217e-05, 1.21909674823136e-06, -9.91125761573336e-15, -3.86545953927982e-14, -5162203435.58507
Beginning fit attempt 6 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Fit attempt generated errors
Beginning fit attempt 7 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Fit attempt worse than current best: 3256.75663903538 vs 3235.57170981942
Beginning fit attempt 8 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Lowest minimum so far: 637.851594148809
OpenMx status code 6 not in list of acceptable status codes, (0,0)
Not all eigenvalues of the Hessian are positive: 12594202250.6772, 410539.521258017, 227128.072431512, 82.6140297057458, 61.0385038544654, 26.9758896363023, 20.4742189116867, 19.1277690968383, 0.0047410411227176, 0.00346008055005324, -5056276116.50107
Beginning fit attempt 9 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Lowest minimum so far: 611.4240575611
OpenMx status code 6 not in list of acceptable status codes, (0,0)
Not all eigenvalues of the Hessian are positive: 348951058232.898, 574198.517215359, 288792.80891657, 94.7098346667671, 63.8350382176358, 43.685421396699, 32.3128690348284, 30.6811146991877, 0.203191491438579, 0.00850292348041861, -798233875153.431
Beginning fit attempt 10 of at maximum 10 extra tries
Running Unnamed_Model with 11 parameters
Lowest minimum so far: 611.424025797553
OpenMx status code 6 not in list of acceptable status codes, (0,0)
Not all eigenvalues of the Hessian are positive: 349051031747.525, 574411.600988537, 288738.578147543, 94.7299895681835, 63.7908935946122, 43.689181551094, 32.3087812992549, 30.6778908657851, 0.20616838244696, 0.00847927780174462, -798355396954.693
Retry limit reached
Retry limit reached; Best fit=611.42403 (started at 196710.97) (11 attempt(s): 8 valid, 3 errors)
Uncertain solution found - consider parameter validity, try again, increase extraTries, change inits, change model, or check data!
Start values from best fit:
0.294246721531443,0.692752601893522,0.643854780081641,0.517436610239405,1.06093735167851,1.07544852770618,1.15665917267355,48.4179957925071,8.42656924445125,12.4961904874703,80.0258911747755
> summary(result)
Summary of Unnamed_Model
The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED)
free parameters:
name matrix row col Estimate Std.Error A
1 l2 A x2 F 0.2942467 NA !
2 l3 A x3 F 0.6927526 0.1076914
3 l4 A x4 F 0.6438548 0.1635136
4 VAR_x1 S x1 x1 0.5174366 0.2244681
5 VAR_x2 S x2 x2 1.0609374 0.2160097 !
6 VAR_x3 S x3 x3 1.0754485 0.2426530 !
7 VAR_x4 S x4 x4 1.1566592 0.2591538 !
8 const__x2 M 1 x2 48.4179958 0.1487100
9 const__x3 M 1 x3 8.4265692 8.6171815
10 const__x4 M 1 x4 12.4961905 13.0854039
11 const__F M 1 F 80.0258912 0.1742084
Model Statistics:
| Parameters | Degrees of Freedom | Fit (-2lnL units)
Model: 11 189 611.424
Saturated: 14 186 NA
Independence: 8 192 NA
Number of observations/statistics: 50/200
Information Criteria:
| df Penalty | Parameters Penalty | Sample-Size Adjusted
AIC: 233.4240 633.4240 640.3714
BIC: -127.9483 654.4563 619.9291
To get additional fit indices, see help(mxRefModels)
timestamp: 2020-05-18 11:40:37
Wall clock time: 0.4078512 secs
optimizer: CSOLNP
OpenMx version number: 2.17.3
Need help? See help(mxSummary)
>
> subset <- data[1:50, ]
>
>
> ctr <- semtree.control(verbose=TRUE)
> ctr$exclude.heywood <- FALSE
> # naive tree should find both effects, age & ses, with age having the stronger effect
> tree <- semtree(model = result, data=fulldata, control=ctr )
Detected OpenMx model.
MODEL IDS 1234
COV IDS 56
OpenMx model estimation selected!
Growing level 0 n=800
Error : The job for model 'INITIALIZED MODEL' exited abnormally with the error message: fit is not finite (0: The continuous part of the model implied covariance (loc2) is not positive definite in data 'INITIALIZED MODEL.data' row 469. Detail:
covariance = matrix(c( # 4x4
3.63757867343763e+19, 3.63757867343763e+19, 3.63757867343763e+19, 3.63757867343763e+19
, 3.63757867343763e+19, 1.32319786054483e+39, 1.32319786054483e+39, 1.32319786054483e+39
, 3.63757867343763e+19, 1.32319786054483e+39, 1.32319786054483e+39, 1.32319786054483e+39
, 3.63757867343763e+19, 1.32319786054483e+39, 1.32319786054483e+39, 1.32319786054483e+39), byrow=TRUE, nrow=4, ncol=4)
)
Model had a run error.
[x] Tree construction finished!
> plot(tree)
Error in round(x$params, 3) :
non-numeric argument to mathematical function
Calls: plot -> plot.semtree -> to.rpart.rec -> paste -> paste
Execution halted
Running the tests in ‘tests/tree.R’ failed.
Complete output:
> set.seed(789)
> require("semtree")
Loading required package: semtree
Loading required package: OpenMx
To take full advantage of multiple cores, use:
mxOption(key='Number of Threads', value=parallel::detectCores()) #now
Sys.setenv(OMP_NUM_THREADS=parallel::detectCores()) #before library(OpenMx)
> data(lgcm)
>
> lgcm$agegroup <- as.ordered(lgcm$agegroup)
> lgcm$training <- as.factor(lgcm$training)
> lgcm$noise <- as.numeric(lgcm$noise)
>
> # LOAD IN OPENMX MODEL.
> # A SIMPLE LINEAR GROWTH MODEL WITH 5 TIME POINTS FROM SIMULATED DATA
>
> manifests <- names(lgcm)[1:5]
> lgcModel <- mxModel("Linear Growth Curve Model Path Specification",
+ type="RAM",
+ manifestVars=manifests,
+ latentVars=c("intercept","slope"),
+ # residual variances
+ mxPath(
+ from=manifests,
+ arrows=2,
+ free=TRUE,
+ values = c(1, 1, 1, 1, 1),
+ labels=c("residual1","residual2","residual3","residual4","residual5")
+ ),
+ # latent variances and covariance
+ mxPath(
+ from=c("intercept","slope"),
+ connect="unique.pairs",
+ arrows=2,
+ free=TRUE,
+ values=c(1, 1, 1),
+ labels=c("vari", "cov", "vars")
+ ),
+ # intercept loadings
+ mxPath(
+ from="intercept",
+ to=manifests,
+ arrows=1,
+ free=FALSE,
+ values=c(1, 1, 1, 1, 1)
+ ),
+ # slope loadings
+ mxPath(
+ from="slope",
+ to=manifests,
+ arrows=1,
+ free=FALSE,
+ values=c(0, 1, 2, 3, 4)
+ ),
+ # manifest means
+ mxPath(
+ from="one",
+ to=manifests,
+ arrows=1,
+ free=FALSE,
+ values=c(0, 0, 0, 0, 0)
+ ),
+ # latent means
+ mxPath(
+ from="one",
+ to=c("intercept", "slope"),
+ arrows=1,
+ free=TRUE,
+ values=c(1, 1),
+ labels=c("meani", "means")
+ ),
+ mxData(lgcm,type="raw")
+ )
>
>
> # TREE CONTROL OPTIONS.
> # TO OBTAIN BASIC/DEFAULT SMETREE OPTIONS, SIMPLY TPYE THE FOLLOWING:
>
> controlOptions <- semtree.control(method = "naive")
> controlOptions$alpha <- 0.05
>
> # RUN TREE.
>
> tree <- semtree(model=lgcModel, data=lgcm, control = controlOptions)
>
> # RERUN TREE WITH MODEL CONSTRAINTS.
> # MODEL CONSTRAINTS CAN BE ADDED BY IDENTIFYING THE PARAMETERS TO BE
> # CONSTRAINED IN EVERY NODE. ONLY UNCONSTRAINED PARAMETERS ARE THEN
> # TESTED AT EACH NODE FOR GROUP DIFFERENCES. IN THIS EXAMPLE THE MODEL
> # RESIDUALS ARE CONSTRAINED OVER THE NODES.
>
> constraints <- semtree.constraints(global.invariance = names(omxGetParameters(lgcModel))[1:5])
>
> treeConstrained <- semtree(model=lgcModel, data=lgcm, control = controlOptions,
+ constraints=constraints)
Global Constraints:
residual1 residual2 residual3 residual4 residual5
Freely Estimated Parameters:
vari cov vars meani means
*** caught segfault ***
address 8, cause 'memory not mapped'
Traceback:
1: runHelper(model, frontendStart, intervals, silent, suppressWarnings, unsafe, checkpoint, useSocket, onlyFrontend, useOptimizer, beginMessage)
2: mxRun(model, silent = T, suppressWarnings = T)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
7: try(mxRun(model, silent = T, suppressWarnings = T), silent = T)
8: safeRunAndEvaluate(model2, return.model = T)
9: fitSubmodels(model, subset1, subset2, control, invariance = NULL)
10: naiveSplit(model, mydata, control, invariance, meta, constraints = constraints, ...)
11: doTryCatch(return(expr), name, parentenv, handler)
12: tryCatchOne(expr, names, parentenv, handlers[[1L]])
13: tryCatchList(expr, classes, parentenv, handlers)
14: tryCatch(naiveSplit(model, mydata, control, invariance, meta, constraints = constraints, ...), error = function(e) { cat(paste("Error occured!", e, sep = "\n")) return(NULL)})
15: growTree(model = model, mydata = dataset, control = control, invariance = invariance, meta = meta, constraints = constraints, ...)
16: semtree(model = lgcModel, data = lgcm, control = controlOptions, constraints = constraints)
An irrecoverable exception occurred. R is aborting now ...
Running the tests in ‘tests/vim.R’ failed.
Complete output:
> set.seed(789)
> require("semtree")
Loading required package: semtree
Loading required package: OpenMx
To take full advantage of multiple cores, use:
mxOption(key='Number of Threads', value=parallel::detectCores()) #now
Sys.setenv(OMP_NUM_THREADS=parallel::detectCores()) #before library(OpenMx)
> data(lgcm)
>
> lgcm$agegroup <- as.ordered(lgcm$agegroup)
> lgcm$training <- as.factor(lgcm$training)
> lgcm$noise <- as.numeric(lgcm$noise)
>
> # LOAD IN OPENMX MODEL.
> # A SIMPLE LINEAR GROWTH MODEL WITH 5 TIME POINTS FROM SIMULATED DATA
>
> manifests <- names(lgcm)[1:5]
> lgcModel <- mxModel("Linear Growth Curve Model Path Specification",
+ type="RAM",
+ manifestVars=manifests,
+ latentVars=c("intercept","slope"),
+ # residual variances
+ mxPath(
+ from=manifests,
+ arrows=2,
+ free=TRUE,
+ values = c(1, 1, 1, 1, 1),
+ labels=c("residual1","residual2","residual3","residual4","residual5")
+ ),
+ # latent variances and covariance
+ mxPath(
+ from=c("intercept","slope"),
+ connect="unique.pairs",
+ arrows=2,
+ free=TRUE,
+ values=c(1, 1, 1),
+ labels=c("vari", "cov", "vars")
+ ),
+ # intercept loadings
+ mxPath(
+ from="intercept",
+ to=manifests,
+ arrows=1,
+ free=FALSE,
+ values=c(1, 1, 1, 1, 1)
+ ),
+ # slope loadings
+ mxPath(
+ from="slope",
+ to=manifests,
+ arrows=1,
+ free=FALSE,
+ values=c(0, 1, 2, 3, 4)
+ ),
+ # manifest means
+ mxPath(
+ from="one",
+ to=manifests,
+ arrows=1,
+ free=FALSE,
+ values=c(0, 0, 0, 0, 0)
+ ),
+ # latent means
+ mxPath(
+ from="one",
+ to=c("intercept", "slope"),
+ arrows=1,
+ free=TRUE,
+ values=c(1, 1),
+ labels=c("meani", "means")
+ ),
+ mxData(lgcm,type="raw")
+ )
>
>
> fr <- semforest(lgcModel, lgcm,control = semforest.control(num.trees = 15))
*** caught segfault ***
address 8, cause 'memory not mapped'
Traceback:
1: runHelper(model, frontendStart, intervals, silent, suppressWarnings, unsafe, checkpoint, useSocket, onlyFrontend, useOptimizer, beginMessage)
2: mxRun(model, silent = T, suppressWarnings = T)
3: doTryCatch(return(expr), name, parentenv, handler)
4: tryCatchOne(expr, names, parentenv, handlers[[1L]])
5: tryCatchList(expr, classes, parentenv, handlers)
6: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
7: try(mxRun(model, silent = T, suppressWarnings = T), silent = T)
8: safeRunAndEvaluate(model1, return.model = T)
9: fitSubmodels(model, subset1, subset2, control, invariance = NULL)
10: fairSplit(model, mydata, control, invariance, meta, constraints = constraints, ...)
11: doTryCatch(return(expr), name, parentenv, handler)
12: tryCatchOne(expr, names, parentenv, handlers[[1L]])
13: tryCatchList(expr, classes, parentenv, handlers)
14: tryCatch(fairSplit(model, mydata, control, invariance, meta, constraints = constraints, ...), error = function(e) { cat(paste("Error occured!", e, sep = "\n")) return(NULL)})
15: growTree(model, sub1, control, invariance, meta, edgelabel = 1, depth = depth + 1, constraints)
16: growTree(model = model, mydata = dataset, control = control, invariance = invariance, meta = meta, constraints = constraints, ...)
17: semtree(model = model, data = data$bootstrap.data, control = semtree.control, predictors = predictors, constraints = constraints, ...)
18: doTryCatch(return(expr), name, parentenv, handler)
19: tryCatchOne(expr, names, parentenv, handlers[[1L]])
20: tryCatchList(expr, classes, parentenv, handlers)
21: tryCatch({ result <- semtree(model = model, data = data$bootstrap.data, control = semtree.control, predictors = predictors, constraints = constraints, ...)}, error = function(err) { errmsg <- paste(date(), paste(err), paste(traceback()), sep = "\n") write(errmsg, file = "error.log", append = TRUE) return(NULL)})
22: (function (data, seed, skip, model, semtree.control, with.error.handler = TRUE, predictors, constraints, ...) { if (!is.na(seed)) { cat("Set seed ", seed, " for tree in forest\n") set.seed(seed) } if (skip) return(NULL) result <- NULL if (with.error.handler) { tryCatch({ result <- semtree(model = model, data = data$bootstrap.data, control = semtree.control, predictors = predictors, constraints = constraints, ...) }, error = function(err) { errmsg <- paste(date(), paste(err), paste(traceback()), sep = "\n") write(errmsg, file = "error.log", append = TRUE) return(NULL) }) } else { result <- semtree(model = model, data = data$bootstrap.data, control = semtree.control, predictors = predictors, constraints = constraints, ...) } return(result)})(dots[[1L]][[1L]], dots[[2L]][[1L]], dots[[3L]][[1L]], model = new("MxRAMModel", name = "Linear Growth Curve Model Path Specification", matrices = list( A = new("FullMatrix", name = "A", values = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 3, 4, 0, 0), labels = c(NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ ), free = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), lbound = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), ubound = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ ), .squareBrackets = numeric(0), .persist = TRUE, .condenseSlots = FALSE, display = character(0), dependencies = integer(0), joinModel = NA_character_, joinKey = NA_character_), S = new("SymmMatrix", name = "S", values = c(1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1), labels = c("residual1", NA, NA, NA, NA, NA, NA, NA, "residual2", NA, NA, NA, NA, NA, NA, NA, "residual3", NA, NA, NA, NA, NA, NA, NA, "residual4", NA, NA, NA, NA, NA, NA, NA, "residual5", NA, NA, NA, NA, NA, NA, NA, "vari", "cov", NA, NA, NA, NA, NA, "cov", "vars"), free = c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE), lbound = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ ), ubound = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), .squareBrackets = numeric(0), .persist = TRUE, .condenseSlots = FALSE, display = character(0), dependencies = integer(0), joinModel = NA_character_, joinKey = NA_character_), F = new("FullMatrix", name = "F", values = c(1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), labels = NA_character_, free = FALSE, lbound = NA_real_, ubound = NA_real_, .squareBrackets = numeric(0), .persist = TRUE, .condenseSlots = TRUE, display = character(0), dependencies = integer(0), joinModel = NA_character_, joinKey = NA_character_), M = new("FullMatrix", name = "M", values = c(0, 0, 0, 0, 0, 1, 1), labels = c(NA, NA, NA, NA, NA, "meani", "means"), free = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE), lbound = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), ubound = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), .squareBrackets = numeric(0), .persist = TRUE, .condenseSlots = FALSE, display = character(0), dependencies = integer(0), joinModel = NA_character_, joinKey = NA_character_)), algebras = list(), constraints = list(), intervals = list(), latentVars = c("intercept", "slope"), manifestVars = c("o1", "o2", "o3", "o4", "o5"), data = new("MxDataStatic", observed = list(o1 = c(4.49004741292, 4.9089544336, 4.50927240128, 4.91459733173, 4.95560325902, 4.92573789321, 5.11978692193, 5.01665693805, 4.81996678066, 4.45907434364, 5.27742400614, 4.97762633082, 4.68313833084, 4.68166519275, 5.26432965127, 5.49635588349, 4.57906796707, 4.60546623271, 5.1076437985, 4.75501763807, 4.89661359745, 4.93552750592, 5.69171478731, 5.15042205537, 5.77974056909, 4.72028118423, 4.46967965855, 5.4606355931, 5.43597171255, 3.92136609957, 5.37093842659, 5.7858528749, 4.81738406503, 4.8698339746, 4.98810756231, 4.53765515216, 5.30135131828, 4.45308173297, 4.81045874772, 5.03833738943, 5.00072904116, 5.55217705985, 4.97639427266, 4.75722385156, 4.29118117202, 4.79976259901, 4.78706708149, 5.1249810944, 5.44918389226, 4.68652136203, 5.05474581892, 5.09831916785, 4.70223967383, 5.69254077294, 5.16706600755, 5.33972234088, 5.06878328678, 5.20389025135, 5.01642531159, 5.47017138355, 5.16179998491, 5.08371576335, 5.06386295627, 4.60893676589, 5.29391820625, 4.59237412566, 5.48516437893, 4.36343248866, 4.89771155174, 4.98028898141, 4.57468745643, 4.91662898309, 4.69245143938, 5.32721765476, 5.53556008271, 5.29408547201, 5.82139882661, 4.6183952552, 4.59361415398, 5.40910580767, 4.95633866895, 5.91247699253, 5.1773392089, 5.44453115164, 4.76360315045, 4.69076514209, 5.694299236, 4.94566150406, 5.31256815093, 5.08420693281, 4.84888813432, 4.30027353896, 4.60238023197, 5.61809862505, 5.29757154454, 5.30611778525, 5.17138499086, 5.33580455379, 5.34270400019, 5.01576732638, 5.08848554002, 4.8048870154, 5.06120351131, 4.67947825591, 4.97924431324, 4.72751992069, 4.78871265899, 4.34916117223, 5.76316328655, 4.93561033934, 5.42590619983, 4.8333464661, 5.59187976722, 4.84672322437, 5.4169272773, 4.73653366081, 5.08087013416, 5.09279148943, 4.22626795453, 4.92192143626, 5.32076883696, 5.19903209867, 4.73397234226, 5.25811250098, 5.59876273401, 4.56297855637, 5.69763677582, 5.44613240557, 4.94422714011, 5.32405044186, 5.14499242101, 4.96133435543, 4.95360301187, 4.83521280599, 5.31931693746, 5.04430092097, 5.24905529074, 5.07038734804, 5.99059177701, 5.62976732436, 4.83240875147, 4.29806963169, 5.06010296006, 5.41886499158, 5.32057699921, 4.62589086542, 4.68370752638, 5.49539243697, 4.86012027784, 4.84793012811, 4.94603974369, 5.27754706748, 5.74442999884, 5.40950088852, 4.59046879861, 4.82641466419, 5.40590569524, 4.48021157988, 4.66298301804, 5.27631870694, 5.59543626803, 4.02854818254, 4.98551002825, 5.4079195916, 5.26838510266, 5.23256746731, 5.18891285058, 5.44207407675, 4.91737170124, 4.22686961699, 5.32745833728, 4.85280964887, 4.60165342244, 5.18233219567, 4.34707548537, 4.68131360165, 5.20032245695, 4.75123418388, 5.05468888915, 4.95968331923, 6.00145358935, 5.4975300799, 4.98536446817, 5.62924651264, 4.83089087409, 5.29485344648, 5.15779680683, 5.23255074671, 4.92156879812, 5.12358658496, 4.73547967964, 4.82694753889, 5.00661265382, 4.78631406316, 4.76795113526, 4.51493971313, 4.6928475905, 5.35075585032, 4.99035582077, 4.94319177236, 2.56249517162, 1.53191835569, 1.6671467703, 1.2072633134, 1.49676521383, 1.81200673707, 1.65396173565, 2.1590686531, 2.03784256168, 2.19892303774, 1.78453835654, 1.81617411502, 1.88091232033, 2.06179529517, 1.57552445681, 2.40219949023, 2.13574993468, 1.70765083159, 2.38016800787, 2.50184186605, 1.90210033936, 1.20057149047, 2.3729399776, 1.72149236425, 1.81469901198, 2.30042621509, 2.14399482079, 2.23090094212, 2.16764364296, 1.6556451974, 2.02883280795, 1.22573343964, 1.69887045422, 1.63400385367, 2.21306636188, 2.57615523868, 2.49537650094, 2.00744860162, 2.07220643873, 2.23328732121, 1.67340314411, 1.99311557178, 2.08536546802, 1.0262576442, 1.01862129028, 2.32077599298, 1.84628023151, 1.87629953173, 2.06573555806, 2.06051590526, 2.51968434189, 2.40952143081, 2.26490427881, 2.1044082542, 1.93824154721, 2.02974402433, 1.9599188605, 1.88675506561, 2.54020501528, 1.88017173008, 2.51293703903, 1.41708429245, 2.19232220921, 1.53822673371, 1.86734528097, 1.20766624685, 1.77346662045, 1.53694024328, 1.68563965337, 2.96426572363, 2.04428980348, 1.85724462521, 1.69696646404, 1.59086930277, 2.53620854093, 1.15375643457, 2.06256462751, 2.40079939785, 1.6719382599, 1.83289677367, 2.02966340765, 1.92423534496, 1.65216305152, 2.47603043698, 2.49198268461, 1.32435951092, 2.20720226188, 1.61502234815, 2.04921361135, 2.52264950668, 2.2510329883, 2.10951831963, 1.33043692067, 2.4321692467, 1.38701016422, 1.80408244071, 2.02523986341, 1.99506751036, 1.68603239722, 1.4936142138, 1.83437616196, 2.29052857603, 2.0480088478, 1.95416144463, 1.41537048574, 2.00403129566, 1.7127133018, 2.08696780358, 1.67750918228, 2.06105014858, 1.94977232576, 1.80953364669, 1.65005943292, 1.65557791743, 1.77688057275, 1.51003289544, 2.50874540888, 1.56385894777, 2.11576249638, 1.99978559598, 1.97608797843, 1.85358180421, 1.72858692296, 2.28569599376, 1.92995172382, 1.98480731885, 2.71576386851, 1.84649601019, 1.96576344733, 2.310486217, 2.11652465252, 2.6961344491, 2.09138268336, 2.12430158147, 1.60891730491, 2.15818265021, 1.83624942778, 2.06355057868, 1.84821389848, 1.90076292758, 1.47002123486, 2.59648132224, 1.80838249588, 1.83478852885, 2.48004331835, 2.09249951997, 2.07130060681, 2.26797544145, 1.09476776451, 1.90046064291, 2.09133566722, 2.24480611094, 2.35326387396, 2.17073228109, 3.14864173926, 2.01771697304, 1.66085510392, 1.44185381811, 1.60296000854, 1.97470880112, 1.51172423999, 1.88039768831, 2.19256266673, 1.71859299728, 2.1689074456, 1.57415801741, 1.182270149, 1.90325708469, 1.9288321469, 1.54860937343, 2.26288456999, 2.4429227514, 1.91156053045, 1.81258332161, 2.85995277125, 2.50882283817, 1.86708287024, 2.58963126999, 1.60504590598, 2.37558589447, 1.91505476728, 1.85712524077, 1.99842003999, 2.57740780891, 2.21944401323, 1.67331052793, 2.46873184965, 2.52770118473, 1.56058099201, 2.02979645304, 2.25084920941, 1.8038841515, 2.07495596615, 2.70393345746, 2.06389469225, 2.10485326416, 1.81110427716, 1.94130315074, 1.56764687199, 2.73314248818), o2 = c(3.31764550076, 5.19028877665, 3.48636456113, 5.19413260137, 5.11833806695, 3.79955327366, 4.64021897717, 5.09435357152, 4.54427370512, 3.55963683732, 5.0926643915, 5.38161236928, 3.86627319903, 5.36676570721, 5.68021782648, 6.12953791681, 2.59573189939, 5.27621757994, 4.11989789795, 4.909893149, 4.91707604391, 3.99710432514, 4.34963520543, 3.96744542105, 5.40252371411, 6.74387649876, 4.8045264223, 4.65990839655, 5.32125420712, 4.32401005444, 5.11119446027, 5.65500466355, 4.07792377985, 6.10881475727, 4.82060941576, 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4.74579498031, 4.75509519768, 4.79790699131, 3.78996353925, 5.04372374045, 4.9366571159, 6.19601044563, 5.03394460849, 5.59015162075, 4.85522866329, 3.93828596265, 3.7916209322, 5.39799452827, 4.67218986069, 5.57760523509, 4.74945131253, 4.43020217471, 5.39325613681, 5.62056502387, 5.12554391069, 4.64124780915, 4.70372565302, 5.32776398651, 5.47470732813, 4.3398458402, 5.15757376088, 4.29621904162, 4.72500703043, 4.55118145617, 5.56578458302, 5.66739717069, 5.66981305645, 4.46569575175, 4.30693290002, 4.02669398169, 4.81205911566, 4.87427991572, 4.62251690415, 4.02484972421, 4.8422426837, 4.79568973288, 5.1825246516, 5.58495645845, 4.47237184748, 4.98131315402, 4.37462006271, 4.53084722301, 4.11964279621, 5.04077022901, 3.9794118708, 4.02428458861, 7.427360266, 4.02562530515, 4.89399079714, 4.85349528088, 3.7297187824, 4.52634541111, 5.08376619674, 4.67347431543, 4.17788855808, 4.62680257481, 4.81180549956, 4.75886172956, 6.05795192521, 3.9211176358, 6.22950273759, 3.97608928684, 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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), noise = c(1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1)), means = NA_real_, type = "raw", numObs = 400, observedStats = list(), .isSorted = FALSE, .needSort = TRUE, .parallel = TRUE, .noExoOptimize = TRUE, primaryKey = NA_character_, weight = NA_character_, frequency = NA_character_, minVariance = 1.49011611938477e-08, algebra = character(0), warnNPDacov = TRUE, exoFree = NULL, naAction = "pass", verbose = 0L, name = "data"), submodels = list(), expectation = new("MxExpectationRAM", A = "A", S = "S", F = "F", M = "M", thresholds = NA_character_, dims = NA_character_, threshnames = NA_character_, usePPML = FALSE, ppmlData = NULL, UnfilteredExpCov = NA, numStats = numeric(0), between = NULL, isProductNode = NULL, verbose = 0L, .rampartCycleLimit = NA_integer_, .rampartUnitLimit = NA_integer_, .useSufficientSets = TRUE, .forceSingleGroup = FALSE, .analyzeDefVars = TRUE, .maxDebugGroups = 0L, .optimizeMean = 2L, .useSparse = NA, expectedCovariance = NULL, expectedMean = NULL, data = NA_integer_, dataColumns = integer(0), dataColumnNames = NULL, .runDims = character(0), output = list(), debug = list(), name = "expectation"), fitfunction = new("MxFitFunctionML", fellner = NA, verbose = 0L, profileOut = character(0), rowwiseParallel = NA, jointConditionOn = "auto", components = character(0), info = list(), dependencies = integer(0), expectation = integer(0), vector = FALSE, rowDiagnostics = FALSE, result = numeric(0), name = "fitfunction"), compute = NULL, independent = FALSE, options = list(), output = list(), runstate = list(), .newobjects = FALSE, .resetdata = FALSE, .wasRun = FALSE, .modifiedSinceRun = TRUE, .version = "2.17.3"), semtree.control = list(verbose = FALSE, test.type = "ml", score.tests = list(nominal = "LM", ordinal = "DM", metric = "DM"), num.folds = 5, exclude.heywood = TRUE, min.N = 20, min.bucket = 10, method = "fair", max.depth = NA, alpha = 1, alpha.invariance = NA, progress.bar = TRUE, bonferroni = FALSE, use.all = FALSE, exclude.code = NA, seed = NA, mtry = 2, custom.stopping.rule = NA, report.level = 0), TRUE, predictors = c("agegroup", "training", "noise"), NULL)
23: mapply(FUN = semtreeApplyWrapper, forest.data, seeds, skip, MoreArgs = list(model = model, semtree.control = semforest.control$semtree.control, with.error.handler, predictors = covariates, constraints), SIMPLIFY = FALSE)
24: semforest(lgcModel, lgcm, control = semforest.control(num.trees = 15))
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86