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Type 'q()' to quit R. > # Run all unit tests, i.e. all checks of all test-functions > # > # Author: Andre Schuetzenmeister > ############################################################################### > > library(VCA) > library(RUnit) > > # enable/disable time-consuming model 1 and model 2 testcases with real-world data > # see file "runit.VCAinference.R" > > realWorldModel2 <- FALSE > realWorldModel1 <- FALSE > > options(warn=1) > > # current working directory > wd <- getwd() > > # set path from wd, where RunAllTests.R resided to RUnit test-scripts (scripts directory) > sd <- "./runit/UnitTests" > tc.path <- sub("\\./", "/", paste0(wd, sd)) > > # test function regexpr fits to string "TFxyz" which are used as identifiers for easier referencing > > testSuite <- defineTestSuite(name="VCA", dirs=tc.path, + testFileRegexp="runit.*\\.R$", + testFuncRegexp = "^TF[[:digit:]]{3}.+", # use custom regexpr for test functions + rngKind="default", + rngNormalKind="default") > > > testData <- runTestSuite(testSuite, verbose=0L) *************************************************************************** Variance Component Analysis (VCA) - test cases for function 'VCAinference'. *************************************************************************** Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! SAS PROC MIXED uses the inverse of the Fisher-Information Matrix as approximation of Covariance-Matrix of Variance Components. This is equal to the one obtained via ANOVA-approach stated in "Variance Components" (Searle et al. 1991) in case of balanced designs, otherwise Var(VC) of both results may differ: SAS_Est R_Est SAS_LCL R_LCL SAS_UCL R_UCL lot 0.01377 0.01377 -0.013750 -0.016870 0.041280 0.044400 device 0.06190 0.06190 -0.062340 -0.063040 0.186100 0.186800 lot:device:day 0.01235 0.01235 -0.004180 -0.004480 0.028870 0.029180 lot:device:day:run 0.05125 0.05125 0.033110 0.032950 0.069400 0.069560 error 0.00118 0.00118 0.000932 0.000932 0.001543 0.001543 Est_Diff LCL_Diff UCL_Diff lot 0 0.00312 -0.00312 device 0 0.00070 -0.00070 lot:device:day 0 0.00030 -0.00031 lot:device:day:run 0 0.00016 -0.00016 error 0 0.00000 0.00000 Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! SAS PROC MIXED uses the inverse of the Fisher-Information Matrix as approximation of Covariance-Matrix of Variance Components. This is equal to the one obtained via ANOVA-approach stated in "Variance Components" (Searle et al. 1991) in case of balanced designs, otherwise Var(VC) of both results may differ: SAS_Est R_Est SAS_LCL R_LCL SAS_UCL R_UCL Est_Diff lot 0.18920 0.18920 -0.19090 -0.21540 0.56920 0.59380 0 device 0.43590 0.43590 -0.46080 -0.45200 1.33260 1.32380 0 lot:device:day 0.31780 0.31780 0.18600 0.18680 0.44960 0.44870 0 lot:device:day:run 0.05724 0.05724 0.02931 0.02931 0.08517 0.08517 0 error 0.04108 0.04108 0.03241 0.03241 0.05376 0.05376 0 LCL_Diff UCL_Diff lot 0.0245 -0.0246 device -0.0088 0.0088 lot:device:day -0.0008 0.0009 lot:device:day:run 0.0000 0.0000 error 0.0000 0.0000 Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! SAS PROC MIXED uses the inverse of the Fisher-Information Matrix as approximation of Covariance-Matrix of Variance Components. This is equal to the one obtained via ANOVA-approach stated in "Variance Components" (Searle et al. 1991) in case of balanced designs, otherwise Var(VC) of both results may differ: SAS_Est R_Est SAS_LCL R_LCL SAS_UCL lot -0.000850 -0.000850 -0.001540 -0.001690 -1.700e-04 device 0.058040 0.058040 -0.057620 -0.058080 1.737e-01 lot:device:day -0.017410 -0.017410 -0.034820 -0.035110 -3.250e-06 lot:device:day:run 0.084000 0.084000 0.053940 0.054130 1.141e-01 error 0.000337 0.000337 0.000266 0.000266 4.400e-04 R_UCL Est_Diff LCL_Diff UCL_Diff lot -0.00002000 0 0.00015 -0.00015000 device 0.17420000 0 0.00046 -0.00050000 lot:device:day 0.00028594 0 0.00029 -0.00028919 lot:device:day:run 0.11390000 0 -0.00019 0.00020000 error 0.00044000 0 0.00000 0.00000000 Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Mixed model equations solved locally. Results could not be assigned to object! Warning in VCAinference(fit, ci.method = "satterthwaite") : Point estimate(s) of VC(s) 'Site:Day:Run' found outside of confidence interval, CI was set to 'NA'! Inference from (V)ariance (C)omponent (A)nalysis ------------------------------------------------ > VC: ----- Estimate DF CI LCL CI UCL One-Sided LCL One-Sided UCL total 0.3405 15.5385 0.1875 0.8005 0.2059 0.6927 Site 0.1381 2.6726 0.0423 2.4767 0.0510 1.4414 Site:Day 0.0084 0.4506 0.0012 36811.9725 0.0017 1697.8716 Site:Day:Run 0.0004 0.0005 Inf error 0.1936 80.0000 0.1452 0.2710 0.1520 0.2564 95% Confidence Level Satterthwaite methodology used for computing CIs boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') ************************************************************************** Variance Component Analysis (VCA) - test cases defined in runit.anovaMM.R. ************************************************************************** Error in anovaMM() : argument "form" is missing, with no default Error in anovaMM(Data = 1) : argument "form" is missing, with no default Error in anovaMM(Data = data.frame()) : argument "form" is missing, with no default Error in anovaMM(Data = data.frame(y = 1:10)) : argument "form" is missing, with no default Error in orderData(Data, tobj, quiet = quiet, order.data = order.data) : all(vars %in% colnames(Data)) is not TRUE Error in orderData(Data, tobj, quiet = quiet, order.data = order.data) : all(vars %in% colnames(Data)) is not TRUE There are 3 missing values for the response variable (obs: 43, 57, 66)! Variable 'run' has 3 missing values (obs: 19, 38, 50)! Variable 'day' has 3 missing values (obs: 78, 79, 80)! Convert variable Site from "character" to "factor"! Convert variable ID from "character" to "factor"! Warning in anovaMM(y ~ day + cov + day:(run), dat1) : All values of response variable 'y' are equal! Warning in anovaMM(y ~ day/(run), dat1) : All values of response variable 'y' are equal! Warning in anovaMM(y ~ (day)/(run), dat1) : All values of response variable 'y' are equal! *********************************************************************** Variance Component Analysis (VCA) - test cases for function 'anovaVCA'. *********************************************************************** Error in anovaVCA() : argument "form" is missing, with no default Error in anovaVCA(Data = 1) : argument "form" is missing, with no default Error in anovaVCA(Data = data.frame()) : argument "form" is missing, with no default Error in anovaVCA(Data = data.frame(y = 1:10)) : argument "form" is missing, with no default Error in orderData(Data, tobj, quiet = quiet, exclude.numeric = FALSE, : all(vars %in% colnames(Data)) is not TRUE Error in orderData(Data, tobj, quiet = quiet, exclude.numeric = FALSE, : all(vars %in% colnames(Data)) is not TRUE There are 3 missing values for the response variable (obs: 43, 57, 66)! Variable 'day' has 3 missing values (obs: 78, 79, 80)! Variable 'run' has 3 missing values (obs: 19, 38, 50)! Warning in anovaVCA(y ~ day, dat1) : All values of response variable 'y' are equal! Warning in anovaVCA(y ~ day/run, dat1) : All values of response variable 'y' are equal! Error in anovaVCA(y ~ day, dataEP05A2_3[seq(1, 77, 4), ]) : Variable 'day': number of levels of each grouping factor must be < number of observations! *********************************************************************** Variance Component Analysis (VCA) - test cases defined in runit.misc.R. *********************************************************************** boundary (singular) fit: see help('isSingular') Mixed model equations solved locally. Results could not be assigned to object! Warning in lsmMat(obj, NULL, quiet = quiet) : 'Sex:age2' is "numeric", LS Means cannot be estimated,'Sex:age2' will be skipped! Warning in lsmMat(obj, NULL, quiet = quiet) : 'Sex:age2' is "numeric", LS Means cannot be estimated,'Sex:age2' will be skipped! Warning in lsmMat(obj, NULL, quiet = quiet) : 'Sex:age2' is "numeric", LS Means cannot be estimated,'Sex:age2' will be skipped! [1] TRUE [1] TRUE [1] TRUE [1] TRUE [1] TRUE [1] TRUE Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Mixed model equations were solved but results could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Matrices 'H' and 'Q' were comuted but could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Matrices 'H' and 'Q' were comuted but could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Matrices 'H' and 'Q' were comuted but could not be assigned to 'VCA' object! Matrices 'H' and 'Q' were comuted but could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! [1] TRUE [1] TRUE [1] TRUE [1] TRUE [1] TRUE [1] TRUE Note: 'ddfm' set to "satterthwaite", currently option "contain" does not work for REML-estimation! Warning in lsmeans(fit.vca, var = "snp", at = list(tim = 1:4)) : Argument 'at' was not correctly specified! Neither covariables nor factor variables could be matched! Warning in lsmeans(fit.vca, var = "snp", at = list(sex = c(Male = 0.3, Female = 0.6))) : Sum of all coefficients of factor-variable 'sex' is not equal to 1! It will be skipped! Warning in lsmeans(fit.vca, var = "snp", at = list(sex = c(Male = 0.5, Female = 0.6))) : Sum of all coefficients of factor-variable 'sex' is not equal to 1! It will be skipped! Error in orderData(Data, tobj, quiet = quiet, order.data = order.data) : all(vars %in% colnames(Data)) is not TRUE Error in orderData(Data, trms, quiet = quiet, order.data = order.data) : all(vars %in% colnames(Data)) is not TRUE Mixed model equations solved locally. Results could not be assigned to object! boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! sample 1 : [1] TRUE [1] TRUE sample 2 : [1] TRUE [1] TRUE boundary (singular) fit: see help('isSingular') Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! boundary (singular) fit: see help('isSingular') sample 3 : [1] TRUE [1] TRUE sample 4 : [1] TRUE [1] TRUE Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! sample 5 : [1] TRUE [1] TRUE boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') sample 6 : [1] TRUE [1] TRUE Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! sample 7 : [1] TRUE [1] TRUE sample 8 : [1] TRUE [1] TRUE Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! sample 9 : [1] TRUE [1] TRUE sample 10 : [1] TRUE [1] TRUE boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') Error in ranef.VCA(fit) : (converted from warning) The fitted model has not been re-scaled yet! Results are likely to differ from correct results! Error in residuals.VCA(fit) : (converted from warning) The fitted model has not been re-scaled yet! Results are likely to differ from correct results! Mixed model equations were solved but results could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Matrices 'H' and 'Q' were comuted but could not be assigned to 'VCA' object! Matrices 'H' and 'Q' were comuted but could not be assigned to 'VCA' object! Mixed model equations were solved but results could not be assigned to 'VCA' object! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Some required information missing! Usually solving mixed model equations has to be done as a prerequisite! Matrices 'H' and 'Q' were comuted but could not be assigned to 'VCA' object! Matrices 'H' and 'Q' were comuted but could not be assigned to 'VCA' object! Mixed model equations solved locally. Results could not be assigned to object! Convert variable day from "character" to "factor"! Convert variable day from "character" to "factor"! TF075.protectedCall.anovaVCA smpl: Error in Fsweep(M, asgn = asgn) : Inverting the C Matrix failed! Double check the specified model! Are there replicates? dtld: try-error:Error in Fsweep(M, asgn = asgn) : Inverting the C Matrix failed! Double check the specified model! Are there replicates? Additional error checks: Levels of 'run' are indistinguishable from levels of 'day'! TF076.protectedCall.remlVCA smpl: Error in solve.default(VCvar[nze, nze]) : system is computationally singular: reciprocal condition number = 1.42946e-28 dtld: try-error:Error in solve.default(VCvar[nze, nze]) : system is computationally singular: reciprocal condition number = 1.42946e-28 Additional error checks: REML-estimation error! Zero variation within factor-levels of 'day:run'! TF077.protectedCall.remlMM smpl: Error in solve.default(VCvar[nze, nze]) : system is computationally singular: reciprocal condition number = 1.42268e-26 dtld: try-error:Error in solve.default(VCvar[nze, nze]) : system is computationally singular: reciprocal condition number = 1.42268e-26 Additional error checks: REML-estimation error! Zero variation within factor-levels of 'day:run'! Convert variable day from "character" to "factor"! Convert variable day from "character" to "factor"! TF078.protectedCall.anovaMM smpl: Error in Fsweep(M, asgn = asgn) : Inverting the C Matrix failed! Double check the specified model! Are there replicates? dtld: try-error:Error in Fsweep(M, asgn = asgn) : Inverting the C Matrix failed! Double check the specified model! Are there replicates? Additional error checks: Levels of 'run' are indistinguishable from levels of 'day'! Convert variable day from "character" to "factor"! Convert variable day from "character" to "factor"! TF079.protectedCall.fitVCA.anova smpl: Error in Fsweep(M, asgn = asgn) : Inverting the C Matrix failed! Double check the specified model! Are there replicates? dtld: try-error:Error in Fsweep(M, asgn = asgn) : Inverting the C Matrix failed! Double check the specified model! Are there replicates? Additional error checks: Levels of 'run' are indistinguishable from levels of 'day'! TF080.protectedCall.fitVCA.reml smpl: Error in solve.default(VCvar[nze, nze]) : system is computationally singular: reciprocal condition number = 1.09583e-28 dtld: try-error:Error in solve.default(VCvar[nze, nze]) : system is computationally singular: reciprocal condition number = 1.09583e-28 Additional error checks: REML-estimation error! Zero variation within factor-levels of 'day:run'! Convert variable day from "character" to "factor"! Convert variable day from "character" to "factor"! TF081.protectedCall.fitLMM.anova smpl: Error in Fsweep(M, asgn = asgn) : Inverting the C Matrix failed! Double check the specified model! Are there replicates? dtld: try-error:Error in Fsweep(M, asgn = asgn) : Inverting the C Matrix failed! Double check the specified model! Are there replicates? Additional error checks: Levels of 'run' are indistinguishable from levels of 'day'! TF082.protectedCall.fitLMM.remlss smpl: Error in solve.default(VCvar[nze, nze]) : system is computationally singular: reciprocal condition number = 9.59965e-20 dtld: try-error:Error in solve.default(VCvar[nze, nze]) : system is computationally singular: reciprocal condition number = 9.59965e-20 Additional error checks: REML-estimation error! Zero variation within factor-levels of 'day:run'! ************************************************************************** Variance Component Analysis (VCA) - test cases defined in runit.remlMM.R. ************************************************************************** boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') Error in remlMM() : argument "form" is missing, with no default In addition: There were 18 warnings (use warnings() to see them) Error in remlMM(Data = 1) : argument "form" is missing, with no default Error in remlMM(Data = data.frame()) : argument "form" is missing, with no default Error in remlMM(Data = data.frame(y = 1:10)) : argument "form" is missing, with no default Error in orderData(Data, trms, quiet = quiet, order.data = order.data) : all(vars %in% colnames(Data)) is not TRUE Error in orderData(Data, trms, quiet = quiet, order.data = order.data) : all(vars %in% colnames(Data)) is not TRUE boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') *********************************************************************** Variance Component Analysis (VCA) - test cases for function 'remlVCA'. *********************************************************************** Error in remlVCA() : argument "form" is missing, with no default In addition: Warning messages: 1: In remlMM(y ~ day + cov + day:(run), dat1) : All values of response variable 'y' are equal! 2: In remlMM(y ~ day/(run), dat1) : All values of response variable 'y' are equal! 3: In remlMM(y ~ (day)/(run), dat1) : All values of response variable 'y' are equal! Error in remlVCA(Data = 1) : argument "form" is missing, with no default Error in remlVCA(Data = data.frame()) : argument "form" is missing, with no default Error in remlVCA(Data = data.frame(y = 1:10)) : argument "form" is missing, with no default Error in orderData(Data, trms, quiet = quiet, exclude.numeric = FALSE, : all(vars %in% colnames(Data)) is not TRUE Error in orderData(Data, trms, quiet = quiet, exclude.numeric = FALSE, : all(vars %in% colnames(Data)) is not TRUE boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') Warning messages: 1: In remlVCA(y ~ 1, Data = dataRS0003_1) : No random effects specified! Call function 'anovaVCA' instead! 2: In remlVCA(y ~ 1, Data = dataRS0003_2) : No random effects specified! Call function 'anovaVCA' instead! 3: In remlVCA(y ~ 1, Data = dataRS0003_3) : No random effects specified! Call function 'anovaVCA' instead! 4: In remlVCA(y ~ day, dat1) : All values of response variable 'y' are equal! 5: In remlVCA(y ~ day/run, dat1) : All values of response variable 'y' are equal! > > sInfo <- sessionInfo() > cat("Test Summary Report R-Paket VCA", paste("V", sInfo$otherPkgs[["VCA"]]$Version, sep=""), file="./VCA_UnitTest_Protocol.txt", append=FALSE) > cat("\n-------------------------------------", file="./VCA_UnitTest_Protocol.txt", append=TRUE ) > cat("\n\n\n1) Package Description:", file="./VCA_UnitTest_Protocol.txt", append=TRUE) > cat("\n-----------------------\n\n", file="./VCA_UnitTest_Protocol.txt", append=TRUE) > capture.output(print(sInfo$otherPkgs[["VCA"]]), file="./VCA_UnitTest_Protocol.txt", append=TRUE) > cat("\n\n\n2) Test Environment:", file="./VCA_UnitTest_Protocol.txt", append=TRUE) > cat("\n--------------------\n", file="./VCA_UnitTest_Protocol.txt", append=TRUE) > sinfo <- Sys.info() > snam <- names(sinfo) > for(i in 1:length(sinfo)) + { + cat(paste("\n", snam[i], paste(rep(" ", 20-nchar(snam[i])), collapse=""), sep=""),":\t", sinfo[i], file="./VCA_UnitTest_Protocol.txt", append=TRUE) + } > > cat("\n\n\n\n3) Test Protocol:", file="./VCA_UnitTest_Protocol.txt", append=TRUE) > cat("\n-----------------\n\n", file="./VCA_UnitTest_Protocol.txt", append=TRUE) > > printTextProtocol(testData, showDetails=FALSE) RUNIT TEST PROTOCOL -- Tue Jan 16 16:58:03 2024 *********************************************** Number of test functions: 176 Number of errors: 0 Number of failures: 0 1 Test Suite : VCA - 176 test functions, 0 errors, 0 failures > capture.output(printTextProtocol(testData, showDetails=TRUE), file="./VCA_UnitTest_Protocol.txt", append=TRUE) > printHTMLProtocol(testData, file="./VCA_UnitTest_Protocol.html") > > options(warn=0) > > > proc.time() user system elapsed 83.21 1.43 84.65