CRAN Package Check Results for Package semtree

Last updated on 2021-10-24 15:51:17 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.9.17 16.66 938.40 955.06 OK
r-devel-linux-x86_64-debian-gcc 0.9.17 14.37 409.14 423.51 OK
r-devel-linux-x86_64-fedora-clang 0.9.17 837.85 OK
r-devel-linux-x86_64-fedora-gcc 0.9.17 670.56 OK
r-devel-windows-x86_64 0.9.17 26.00 418.00 444.00 OK
r-devel-windows-x86_64-gcc10-UCRT 0.9.17 OK
r-patched-linux-x86_64 0.9.17 21.59 540.13 561.72 OK
r-patched-solaris-x86 0.9.17 281.80 ERROR
r-release-linux-x86_64 0.9.17 15.23 547.00 562.23 OK
r-release-macos-arm64 0.9.17 OK
r-release-macos-x86_64 0.9.17 OK
r-release-windows-ix86+x86_64 0.9.17 24.00 588.00 612.00 OK
r-oldrel-macos-x86_64 0.9.17 OK
r-oldrel-windows-ix86+x86_64 0.9.17 37.00 550.00 587.00 OK

Check Details

Version: 0.9.17
Check: tests
Result: ERROR
     Running ‘invariance.R’
     Running ‘lavaan.R’ [22s/27s]
     Running ‘scores.R’ [8s/12s]
     Running ‘tree.R’
     Running ‘vim.R’ [9s/33s]
    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 0.6-9
     lavaan is FREE software! Please report any bugs.
     > #
     > 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)
     + }
     >
     > # rescale data
     > data <- scale(data)
     >
     > # put data into dataframe and label observed variables
     > data <- data.frame(data)
     > names(data) <- paste0("x",1:4)
     >
     > # add predictors to create full data set
     > fulldata <- cbind(data, age=factor(rep(age,each=Nsub)),ses=factor(rep(ses,each=Nsub)))
     >
     > # specify model
     > 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 <- mxRun(model)
     Running Unnamed_Model with 11 parameters
     Error: C stack usage 272864404 is too close to the limit
     Error: The job for model 'Unnamed_Model' exited abnormally with the error message: User interrupt
     Execution halted
    Running the tests in ‘tests/scores.R’ failed.
    Complete output:
     > 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")
     + )
     >
     > #lgcModel=mxRun(lgcModel)
     >
     > # TREE CONTROL OPTIONS.
     > # TO OBTAIN BASIC/DEFAULT SMETREE OPTIONS, SIMPLY TPYE THE FOLLOWING:
     >
     > ctrl <- semtree.control(method = "score")
     >
     > # RUN TREE.
     >
     > tree <- semtree(model=lgcModel, data=lgcm, control = ctrl)
     ❯ Model was not run. Estimating parameters now.
     Running Linear Growth Curve Model Path Specification with 10 parameters
    
     Beginning initial fit attempt
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 1 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 2 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 3 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 4 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 5 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 6 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 7 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 8 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 9 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 10 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Retry limit reached
    
    
    
     All fit attempts resulted in errors - check starting values or model specification
    
     ✖ Error in OpenMx_scores_input() function. There are no free parameters in the model estimates. Model not run or converged?
     Error:
     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$noise <- as.factor(lgcm$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$alpha <- 0.05
     >
     > # RUN TREE.
     >
     > tree <- semtree(model=lgcModel, data=lgcm, control = controlOptions)
     Error: C stack usage 272929940 is too close to the limit
     Error : The job for model 'INITIALIZED MODEL' exited abnormally with the error message: User interrupt
     ✖ Model had a run error.
     ✔ Tree construction finished [took less than a second].
     >
     > # RERUN TREE WITH MODEL CONSTRAINTS.
     > # MODEL CONSTRAINTS CAN BE ADDED BY IDENTIFYING THE PARAMETERS TO BE
     > # CONSTRAINED IN EVERY NODE. ONLY UNCONSTRAINED PARAMETERS ARE THEN
     > # TESTED AT EACH NODE FOR GROUP DIFFERENCES. IN THIS EXAMPLE THE MODEL
     > # RESIDUALS ARE CONSTRAINED OVER THE NODES.
     >
     > constraints <- semtree.constraints(global.invariance = names(omxGetParameters(lgcModel))[1:5])
     >
     > treeConstrained <- semtree(model=lgcModel, data=lgcm, control = controlOptions,
     + constraints=constraints)
     Error: C stack usage 272929940 is too close to the limit
     Error: no more error handlers available (recursive errors?); invoking 'abort' restart
     Execution halted
    Running the tests in ‘tests/vim.R’ failed.
    Complete output:
     > set.seed(789)
     > require("semtree")
     Loading required package: semtree
     Loading required package: OpenMx
     To take full advantage of multiple cores, use:
     mxOption(key='Number of Threads', value=parallel::detectCores()) #now
     Sys.setenv(OMP_NUM_THREADS=parallel::detectCores()) #before library(OpenMx)
     > data(lgcm)
     >
     > lgcm$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 = 3))
     ❯ Model was not run. Estimating parameters now before running the forest.
     Running Linear Growth Curve Model Path Specification with 10 parameters
    
     Beginning initial fit attempt
     Running Linear Growth Curve Model Path Specification with 10 parameters
     Error: C stack usage 273061012 is too close to the limit
    
     Fit attempt generated errors
    
     Beginning fit attempt 1 of at maximum 10 extra tries
     Running Linear Growth Curve Model Path Specification with 10 parameters
    
     *** 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 = model, suppressWarnings = T, unsafe = T, silent = T, intervals = intervals, beginMessage = T)
     3: runWithCounter(model, numdone, silent, intervals = F)
     4: doTryCatch(return(expr), name, parentenv, handler)
     5: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     6: tryCatchList(expr, classes, parentenv, handlers)
     7: 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, nlines = 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))})
     8: try(runWithCounter(model, numdone, silent, intervals = F))
     9: withCallingHandlers(expr, warning = function(w) if (inherits(w, classes)) tryInvokeRestart("muffleWarning"))
     10: suppressWarnings(try(runWithCounter(model, numdone, silent, intervals = F)))
     11: OpenMx::mxTryHard(model)
     12: semforest(lgcModel, lgcm, control = semforest.control(num.trees = 3))
     An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86