CRAN Package Check Results for Package semtree

Last updated on 2019-12-06 08:55:05 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.9.13 14.40 430.71 445.11 ERROR
r-devel-linux-x86_64-debian-gcc 0.9.13 10.44 301.40 311.84 OK
r-devel-linux-x86_64-fedora-clang 0.9.13 517.29 OK
r-devel-linux-x86_64-fedora-gcc 0.9.13 461.16 OK
r-devel-windows-ix86+x86_64 0.9.13 24.00 459.00 483.00 OK
r-devel-windows-ix86+x86_64-gcc8 0.9.13 25.00 448.00 473.00 OK
r-patched-linux-x86_64 0.9.13 13.40 371.52 384.92 OK
r-patched-solaris-x86 0.9.13 263.30 ERROR
r-release-linux-x86_64 0.9.13 14.89 372.31 387.20 OK
r-release-windows-ix86+x86_64 0.9.13 20.00 324.00 344.00 OK
r-release-osx-x86_64 0.9.13 OK
r-oldrel-windows-ix86+x86_64 0.9.13 16.00 378.00 394.00 OK
r-oldrel-osx-x86_64 0.9.13 OK

Check Details

Version: 0.9.13
Check: tests
Result: ERROR
     Running 'invariance.R' [34s/19s]
     Running 'lavaan.R' [42s/47s]
     Running 'tree.R' [65s/30s]
     Running 'vim.R' [200s/91s]
    Running the tests in 'tests/vim.R' failed.
    Complete output:
     > set.seed(789)
     > require("semtree")
     Loading required package: semtree
     Loading required package: 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 = 25))
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     [x] Tree construction finished!
     >
     >
     > vimp <- varimp(fr)
     >
     > print(vimp)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     semtree
     --- call from context ---
     print.semforest.varimp(vimp)
     --- call from argument ---
     if (class(vimp$importance) == "matrix") {
     x <- aggregateVarimp(vimp, aggregate, scale, na.omit)
     } else {
     x <- vimp$importance
     if (!na.omit) {
     x[is.na(x)] <- 0
     }
     }
     --- R stacktrace ---
     where 1: print.semforest.varimp(vimp)
     where 2: print(vimp)
     where 3: print(vimp)
    
     --- value of length: 2 type: logical ---
     [1] TRUE FALSE
     --- function from context ---
     function (x, aggregate = "mean", scale = "absolute", sort.values = F,
     na.omit = FALSE, ...)
     {
     vimp <- x
     if (class(vimp$importance) == "matrix") {
     x <- aggregateVarimp(vimp, aggregate, scale, na.omit)
     }
     else {
     x <- vimp$importance
     if (!na.omit) {
     x[is.na(x)] <- 0
     }
     }
     if (sort.values) {
     low <- min(x, na.rm = T) - 1
     filt <- is.na(x)
     x[filt] <- low
     srt <- sort(x, index.return = T)
     x <- x[srt$ix]
     x[x <= (low + 0.5)] <- NA
     }
     cat("Variable Importance\n")
     print(x, ...)
     }
     <bytecode: 0xb2e0fc0>
     <environment: namespace:semtree>
     --- function search by body ---
     Function print.semforest.varimp in namespace semtree has this body.
     ----------- END OF FAILURE REPORT --------------
     Error in if (class(vimp$importance) == "matrix") { :
     the condition has length > 1
     Calls: print -> print -> print.semforest.varimp
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.9.13
Check: tests
Result: ERROR
     Running ‘invariance.R’ [18s/54s]
     Running ‘lavaan.R’ [50s/60s]
     Running ‘tree.R’ [11s/18s]
     Running ‘vim.R’ [16s/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 0.6-5
     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
     OpenMx status code 6 not in list of acceptable status codes, (0,0)
     Not all eigenvalues of the Hessian are positive: 167443773578084, 314435.929561526, 190625.776515999, 43109.5080672531, 8950.1070918141, 44.8311406169129, 27.3413923374245, 0.283453187061305, 0.167802110751759, -112.228472688807, -312002161961779
    
     Beginning fit attempt 1 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Lowest minimum so far: 3252.15512650476
     OpenMx status code 6 not in list of acceptable status codes, (0,0)
     Not all eigenvalues of the Hessian are positive: 13201896138.4943, 87.6849648257364, 70.4671743018242, 65.0211803903042, 26.7201786404394, 1.65407060920141, 3.07470721242059e-05, 1.21927025277993e-06, 1.52959399893484e-14, -9.34986754882785e-12, -13331111937.3142
    
     Beginning fit attempt 2 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Lowest minimum so far: 3252.15512650304
     OpenMx status code 6 not in list of acceptable status codes, (0,0)
     Not all eigenvalues of the Hessian are positive: 13201913234.4296, 87.6853368760002, 70.4659129058165, 65.0211122021372, 26.7192882776609, 1.65404590616691, 8.65702311626256e-06, 1.21907351115013e-06, 1.36668508379563e-14, 4.73957191668828e-15, -13331129040.635
    
     Beginning fit attempt 3 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Fit attempt worse than current best: 3252.15513965677 vs 3252.15512650304
    
     Beginning fit attempt 4 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Lowest minimum so far: 623.525004134806
     OpenMx status code 6 not in list of acceptable status codes, (0,0)
     Not all eigenvalues of the Hessian are positive: 320729867357.432, 459346.672618543, 263838.604941071, 85.6293119505738, 82.4422846658064, 35.8070092236597, 30.8432311977374, 24.9847345607651, 0.0139041523232348, 0.00936689998757276, -778926645718.519
    
     Beginning fit attempt 5 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Lowest minimum so far: 611.424060118282
     OpenMx status code 6 not in list of acceptable status codes, (0,0)
     Not all eigenvalues of the Hessian are positive: 348980478396.755, 574155.994304914, 288797.054173093, 94.7183602836916, 63.8362968450051, 43.6896881166887, 32.3103525580449, 30.6764226700183, 0.202743827703863, 0.0084946673696697, -798306436375.394
    
     Beginning fit attempt 6 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Fit attempt worse than current best: 611.424113500245 vs 611.424060118282
    
     Beginning fit attempt 7 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Fit attempt generated errors
    
     Beginning fit attempt 8 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Fit attempt worse than current best: 653.145004329023 vs 611.424060118282
    
     Beginning fit attempt 9 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Lowest minimum so far: 611.424053513777
     OpenMx status code 6 not in list of acceptable status codes, (0,0)
     Not all eigenvalues of the Hessian are positive: 354613130563.974, 574246.170234739, 288766.160830331, 94.7162889276178, 63.8219518190794, 43.6822317771762, 32.3175079405234, 30.6644442669731, 0.203236270071256, 0.00849491503414099, -811164036747.583
    
     Beginning fit attempt 10 of at maximum 10 extra tries
     Running Unnamed_Model with 11 parameters
    
     Fit attempt worse than current best: 611.424082348691 vs 611.424053513777
    
     Retry limit reached
    
    
    
     Retry limit reached; Best fit=611.42405 (started at 196710.97) (11 attempt(s): 10 valid, 1 errors)
    
    
     Uncertain solution found - consider parameter validity, try again, increase extraTries, change inits, change model, or check data!
    
     Start values from best fit:
     0.293727306720679,0.691843175430711,0.643576888979643,0.516882969551305,1.06108868634299,1.07577633120739,1.15701990022017,48.4596313881482,8.49943887088932,12.5185950105452,80.0256347469588
     > 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 2 5 0.2937273 NA !
     2 l3 A 3 5 0.6918432 0.1081702
     3 l4 A 4 5 0.6435769 0.1630589
     4 VAR_x1 S 1 1 0.5168830 0.2245393 !
     5 VAR_x2 S 2 2 1.0610887 0.2160261 !
     6 VAR_x3 S 3 3 1.0757763 0.2425629 !
     7 VAR_x4 S 4 4 1.1570199 0.2591143 !
     8 const__x2 M 1 2 48.4596314 0.1487063
     9 const__x3 M 1 3 8.4994389 8.6554957
     10 const__x4 M 1 4 12.5185950 13.0489831 !
     11 const__F M 1 5 80.0256347 0.1741767
    
     Model Statistics:
     | Parameters | Degrees of Freedom | Fit (-2lnL units)
     Model: 11 189 611.4241
     Saturated: 14 186 NA
     Independence: 8 192 NA
     Number of observations/statistics: 50/200
    
     Information Criteria:
     | df Penalty | Parameters Penalty | Sample-Size Adjusted
     AIC: 233.4241 633.4241 640.3714
     BIC: -127.9483 654.4563 619.9291
     To get additional fit indices, see help(mxRefModels)
     timestamp: 2019-12-05 10:07:34
     Wall clock time: 0.08442593 secs
     optimizer: CSOLNP
     OpenMx version number: 2.15.4
     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
     Testing Covariate: 5/6 (age)
     Within Covariates LLs: 9060.91
     Testing Covariate: 6/6 (ses)
     Within Covariates LLs: 14212.87
     Best LR 14212.8672413 : ses at covariate column 6
    
     Growing level 1
     Testing Covariate: 5/6 (age)
     Within Covariates LLs: 730756.61
     Testing Covariate: 6/6 (ses)
     Within LLs NULL
     Best LR 730756.6113717 : age at covariate column 5
    
     Growing level 2
     Testing Covariate: 5/6 (age)
     Within LLs NULL
     Testing Covariate: 6/6 (ses)
     Within LLs NULL
     Best Likelihood Ratio was NULL. Stop splitting
     Growing level 2
     Testing Covariate: 5/6 (age)
     Within LLs NULL
     Testing Covariate: 6/6 (ses)
     Within LLs NULL
     Best Likelihood Ratio was NULL. Stop splitting
     Growing level 1
     Testing Covariate: 5/6 (age)
     Within Covariates LLs: -646813.19
     Testing Covariate: 6/6 (ses)
     Within LLs NULL
     Best LR -646813.1947605 : age at covariate column 5
    
     [x] Tree construction finished!
     > plot(tree)
     >
     > # invariance tree should exclude splits wrt age and only splir wrt to ses
     > ctr$report.level <- 99
     > ctr$alpha.invariance <- 0.01
     >
     > cnst <- semtree.constraints(local.invariance = c("l2","l3","l4"))
     > #cnst <- semtree.constraints(local.invariance=)
     > tree2 <- semtree(model = result, data=fulldata, control=ctr, constraints = cnst )
     Detected OpenMx model.
     MODEL IDS 1234
     COV IDS 56
     OpenMx model estimation selected!
     Growing level 0
     Growing tree level 0
     |--Estimating baseline likelihood: 34068.7654324576
     Testing Covariate: 5/6 (age)
     Within LLs NULL
     |--Estimating baseline likelihood: 34068.7654324576
     Testing Covariate: 6/6 (ses)
     Within Covariates LLs: 14212.87
     Filtering possible splits by invariance
     |--invariance vector ids: 1 corresponding to labels l2 |--invariance vector ids: 2 corresponding to labels l3 |--invariance vector ids: 3 corresponding to labels l4
     Best LR 14212.8672413 : ses at covariate column 6
    
     |--Stopping rule applied based on p value 0
     |--Stop splitting based on stopping rule.
     Growing level 1
     Growing tree level 1
     |--Estimating baseline likelihood: 9722.52657547348
     Testing Covariate: 5/6 (age)
     Within Covariates LLs: -100.81
     |--Estimating baseline likelihood: 9722.52657547348
     Testing Covariate: 6/6 (ses)
     Within LLs NULL
     Filtering possible splits by invariance
     |--invariance vector ids: 1 corresponding to labels l2 |--invariance vector ids: 2 corresponding to labels l3 |--invariance vector ids: 3 corresponding to labels l4
     Error occured!
     Error in if (!is.na(LR) & (pchisq(LRtest[j + 1] - LRtest[j], df = length(invariance[[j]]), : missing value where TRUE/FALSE needed
     Best Likelihood Ratio was NULL. Stop splitting
     Growing level 1
     Growing tree level 1
     |--Estimating baseline likelihood: 10183.0793811741
     Testing Covariate: 5/6 (age)
     Within Covariates LLs: -1526.69
     |--Estimating baseline likelihood: 10183.0793811741
     Testing Covariate: 6/6 (ses)
     Within LLs NULL
     Filtering possible splits by invariance
     |--invariance vector ids: 1 corresponding to labels l2 |--invariance vector ids: 2 corresponding to labels l3 |--invariance vector ids: 3 corresponding to labels l4
    
     *** caught segfault ***
     address 8, cause 'memory not mapped'
    
     Traceback:
     1: runHelper(model, frontendStart, intervals, silent, suppressWarnings, unsafe, checkpoint, useSocket, onlyFrontend, useOptimizer, beginMessage)
     2: mxRun(sharedModel, silent = T)
     3: fitSubmodels(model, subset1, subset2, control, invariance[[j]])
     4: invarianceFilter(model, mydata, btn.matrix, LL.baseline, invariance, control)
     5: naiveSplit(model, mydata, control, invariance, meta, constraints = constraints, ...)
     6: doTryCatch(return(expr), name, parentenv, handler)
     7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     8: tryCatchList(expr, classes, parentenv, handlers)
     9: tryCatch(naiveSplit(model, mydata, control, invariance, meta, constraints = constraints, ...), error = function(e) { cat(paste("Error occured!", e, sep = "\n")) return(NULL)})
     10: growTree(model, sub1, control, invariance, meta, edgelabel = 1, depth = depth + 1, constraints)
     11: growTree(model = model, mydata = dataset, control = control, invariance = invariance, meta = meta, constraints = constraints, ...)
     12: semtree(model = result, data = fulldata, control = ctr, constraints = cnst)
     An irrecoverable exception occurred. R is aborting now ...
    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)
     [x] Tree construction finished!
     >
     > # 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
     [x] Tree construction finished!
     >
     > # SEE PLOT.
     > # THE PLOT FUNCTION WILL SHOW ALL FREE PARAMETERS AT EACH TERMINAL NODE.
     > # THIS CAN CREATE UNREADABLE FIGURES FOR MODELS WITH MANY FREE PARAMETERS.
     >
     > plot(tree)
     >
     > summary(tree)
     SEMtree Summary
     Template model:
     Total Sample Size: 400
     Number of nodes: 3
     Number of leaf nodes: 2
     Free Parameters: 10 ( residual1 residual2 residual3 residual4 residual5 vari cov vars meani means )
     >
     > summary(treeConstrained)
     SEMtree Summary
     Template model:
     Total Sample Size: 400
     Number of nodes: 3
     Number of leaf nodes: 2
     Free Parameters: 5 ( vari cov vars meani means )
     >
     > print(tree)
     SEMtree with numbered nodes
     |-[1] agegroup >= 0.5 [N=400 LR=6458.97, df=10]
     | |-[2] TERMINAL [N=200]
     | |-[3] TERMINAL [N=200]
     >
     > parameters(tree)
     2 3
     residual1 0.068 0.045
     residual2 0.049 0.059
     residual3 0.043 0.053
     residual4 0.055 0.051
     residual5 0.037 0.030
     vari 0.082 0.095
     cov -0.011 -0.017
     vars 0.499 1.239
     meani 5.017 1.973
     means -0.142 -0.789
     >
     > parameters(tree, leafs.only=FALSE)
     1 2 3
     residual1 0.050 0.068 0.045
     residual2 0.059 0.049 0.059
     residual3 0.047 0.043 0.053
     residual4 0.052 0.055 0.051
     residual5 0.037 0.037 0.030
     vari 73.522 0.082 0.095
     cov 31.408 -0.011 -0.017
     vars 14.432 0.499 1.239
     meani 3.924 5.017 1.973
     means -0.279 -0.142 -0.789
     >
     > treeSub <- subtree(tree, startNode=3)
     >
     > if (!is.null(treeSub))
     + plot(treeSub)
     >
     > toTable(tree)
     Error in apply(str.matrix, 2, function(x) { :
     dim(X) must have a positive length
     Calls: toTable -> apply
     Execution halted
    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 = 25))
     [x] Tree construction finished!
     [x] Tree construction finished!
    
     *** 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: 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 = model, mydata = dataset, control = control, invariance = invariance, meta = meta, constraints = constraints, ...)
     16: semtree(model = model, data = data$bootstrap.data, control = semtree.control, predictors = predictors, constraints = constraints, ...)
     17: doTryCatch(return(expr), name, parentenv, handler)
     18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     19: tryCatchList(expr, classes, parentenv, handlers)
     20: tryCatch({ result <- semtree(model = model, data = data$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)})
     21: (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]][[3L]], dots[[2L]][[3L]], dots[[3L]][[3L]], 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, 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-4.25708316871, -1.94248876657, -7.61012482887, -1.48111355983, 4.72023364519, -5.7178700455, 5.90341168681, -4.23403662942), agegroup = c(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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), training = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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, 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, 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 = "2.15.4"), semtree.control = list( verbose = FALSE, num.folds = 5, exclude.heywood = TRUE, min.N = 20, 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)
     22: 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)
     23: semforest(lgcModel, lgcm, control = semforest.control(num.trees = 25))
     An irrecoverable exception occurred. R is aborting now ...
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