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Type 'q()' to quit R. > #### doRUnit.R --- Run RUnit tests > ####------------------------------------------------------------------------ > > ### Origianlly follows Gregor Gojanc's example in CRAN package 'gdata' > ### and the corresponding section in the R Wiki: > ### http://wiki.r-project.org/rwiki/doku.php?id=developers:runit > > ### MM: Vastly changed: This should also be "runnable" for *installed* > ## package which has no ./tests/ > ## ----> put the bulk of the code e.g. in ../inst/unitTests/runTests.R : > > if(require("RUnit", quietly=TRUE)) { + + ## --- Setup --- + + wd <- getwd() + pkg <- sub("\\.Rcheck$", '', basename(dirname(wd))) + + library(package=pkg, character.only=TRUE) + + path <- system.file("unitTests", package = pkg) + + stopifnot(file.exists(path), file.info(path.expand(path))$isdir) + + source(file.path(path, "runTests.R"), echo = TRUE) + } > pkg <- "fBasics" > if (require("RUnit", quietly = TRUE)) { + library(package = pkg, character.only = TRUE) + if (!(exists("path") && file.exists(path))) + .... [TRUNCATED] Executing test function test.ghFit ... Title: Generalized Hyperbolic Parameter Estimation Call: ghFit(x = s, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = 1, trace = FALSE) Model: Generalized Hyperbolic Distribution Estimated Parameter(s): alpha beta delta mu lambda 0.7193496 0.2285134 3.2558776 -0.4722910 -0.3088275 alpha beta delta mu lambda TRUE FALSE FALSE FALSE TRUE done successfully. Executing test function test.hypFit ... Title: Hyperbolic Parameter Estimation Call: hypFit(x = s, alpha = 1, beta = 0, delta = 1, mu = mean(s), trace = FALSE) Model: Hyperbolic Distribution Estimated Parameter(s): alpha beta delta mu 1.4908462 0.7817350 0.4393939 -0.9939305 alpha beta delta mu TRUE TRUE TRUE TRUE done successfully. Executing test function test.nFit ... nFit(): Title: Normal Parameter Estimation Call: nFit(x = s) Model: Normal Distribution Estimated Parameter(s): mean sd 0.9993372 0.4942227 mean sd TRUE TRUE done successfully. Executing test function test.nigFit ... Title: Normal Inverse Gaussian Parameter Estimation Call: .nigFit.mle(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu, scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description) Model: Normal Inverse Gaussian Distribution Estimated Parameter(s): alpha beta delta mu 1.5295155 -0.7336941 0.5101010 -1.0065896 alpha beta delta mu 0.12584725 NaN 0.01027594 NaN th.0 est SE alpha 1.5 1.5295155 0.12584725 beta -0.7 -0.7336941 NaN delta 0.5 0.5101010 0.01027594 mu -1.0 -1.0065896 NaN t-ratios: alpha beta delta mu 0.23453440 -0.33694076 0.98297848 -0.06589626 done successfully. Executing test function test.stableFit ... stableFit(): Title: Stable Parameter Estimation Call: .qStableFit(x = x, doplot = doplot, title = title, description = description) Model: Stable Distribution Estimated Parameter(s): alpha beta gamma delta 1.86800000 0.35400000 0.98881057 0.06219485 th.0 est relErr alpha 1.8 1.86800000 0.03777778 beta 0.3 0.35400000 0.18000000 gamma 1.0 0.98881057 -0.01118943 delta 0.1 0.06219485 -0.37805154 done successfully. Executing test function test.tFit ... Title: Student-t Parameter Estimation Call: tFit(x = s, df = 2 * var(s)/(var(s) - 1), trace = FALSE) Model: Student-t Distribution Estimated Parameter(s): df 4.223582 df TRUE done successfully. Executing test function test.Heaviside ... done successfully. Executing test function test.gh ... Distribution Check for: gh Call: fBasics::distCheck(fun = "gh", n = 2000, robust = FALSE, alpha = 1.3, beta = 0.3, delta = 1.7, mu = 0.2, lambda = 0.8) 1. Normalization Check: NORM 1 with absolute error < 3.4e-07 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 1.011492e-06 3. r(2000) Check: MEAN VAR SAMPLE 0.896 2.36 X 0.8589451 with absolute error < 1.3e-05 X^2 3.107412 with absolute error < 0.00015 MEAN VAR EXACT 0.859 2.37 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE done successfully. Executing test function test.hyp ... Distribution Check for: hyp Call: fBasics::distCheck(fun = "hyp", n = 1000, robust = FALSE, alpha = 1.2, beta = 0.2, delta = 1.9, mu = 0.1, pm = "1") 1. Normalization Check: NORM 1 with absolute error < 6e-07 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 8.013692e-07 3. r(1000) Check: MEAN VAR SAMPLE 0.633 2.78 X 0.652347 with absolute error < 1.8e-05 X^2 3.300974 with absolute error < 0.00015 MEAN VAR EXACT 0.652 2.88 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE Distribution Check for: hyp Call: fBasics::distCheck(fun = "hyp", n = 1000, robust = FALSE, alpha = 0.9, beta = -0.3, delta = 1.4, mu = -0.1, pm = "2") 1. Normalization Check: NORM 0.9999999 with absolute error < 1e-04 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 1.458313e-06 3. r(1000) Check: MEAN VAR SAMPLE -1.41 6.83 X -1.377474 with absolute error < 1.2e-05 X^2 9.277938 with absolute error < 6.8e-05 MEAN VAR EXACT -1.38 7.38 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE Distribution Check for: hyp Call: fBasics::distCheck(fun = "hyp", n = 1000, robust = FALSE, alpha = 0.9, beta = -0.3, delta = 1.4, mu = -0.1, pm = "3") 1. Normalization Check: NORM 1 with absolute error < 4.2e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 1.667319e-07 3. r(1000) Check: MEAN VAR SAMPLE -4.77 89.3 X -4.517556 with absolute error < 2.3e-05 X^2 112.9993 with absolute error < 8e-04 MEAN VAR EXACT -4.52 92.6 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE done successfully. Executing test function test.hypSlider ... done successfully. Executing test function test.nig ... Distribution Check for: nig Call: fBasics::distCheck(fun = "nig", n = 1000, robust = FALSE, alpha = 2.1, beta = 0.1, delta = 1.5, mu = -0.1) 1. Normalization Check: NORM 1 with absolute error < 3.5e-07 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 1.163456e-06 3. r(1000) Check: MEAN VAR SAMPLE 0.000105 0.74 X -0.02849031 with absolute error < 5.1e-06 X^2 0.7175339 with absolute error < 2.9e-05 MEAN VAR EXACT -0.0285 0.717 normCheck rmseCheck meanvarCheck TRUE TRUE TRUE done successfully. Executing test function test.nigSlider ... done successfully. Executing test function test.jbData ... done successfully. Executing test function test.jbTable ... done successfully. Executing test function test.NormalityTests ... Title: Exact one-sample Kolmogorov-Smirnov test Test Results: STATISTIC: D: 0.0673 P VALUE: Alternative Two-Sided: 0.9659 Alternative Less: 0.6626 Alternative Greater: 0.6087 Title: Shapiro - Wilk Normality Test Test Results: STATISTIC: W: 0.9858 P VALUE: 0.8045 Title: Jarque-Bera Normality Test Test Results: STATISTIC: X-squared: 0.9827 P VALUE: Asymptotic p Value: 0.6118 Loading required package: interp Title: Jarque - Bera Normality Test Test Results: PARAMETER: Sample Size: 50 STATISTIC: LM: 0.983 ALM: 0.913 P VALUE: LM p-value: 0.524 ALM p-value: 0.574 Asymptotic: 0.612 Title: D'Agostino Normality Test Test Results: STATISTIC: Chi2 | Omnibus: 1.036 Z3 | Skewness: -0.5189 Z4 | Kurtosis: -0.8757 P VALUE: Omnibus Test: 0.5957 Skewness Test: 0.6038 Kurtosis Test: 0.3812 Title: Anderson - Darling Normality Test Test Results: STATISTIC: A: 0.1428 P VALUE: 0.9689 Title: Cramer - von Mises Normality Test Test Results: STATISTIC: W: 0.0165 P VALUE: 0.989 Title: Lilliefors (KS) Normality Test Test Results: STATISTIC: D: 0.0509 P VALUE: 0.9869 Title: Pearson Chi-Square Normality Test Test Results: PARAMETER: Number of Classes: 10 STATISTIC: P: 2.8 P VALUE: Adhusted: 0.9029 Not adjusted: 0.9717 Title: Shapiro - Francia Normality Test Test Results: STATISTIC: W: 0.9911 P VALUE: 0.9283 done successfully. Executing test function test.NormalityTests.MSFT ... Title: Asymptotic one-sample Kolmogorov-Smirnov test Test Results: STATISTIC: D: 0.4541 P VALUE: Alternative Two-Sided: < 2.2e-16 Alternative Less: < 2.2e-16 Alternative Greater: < 2.2e-16 Title: Shapiro - Wilk Normality Test Test Results: STATISTIC: W: 0.9595 P VALUE: 1.911e-06 Title: Jarque-Bera Normality Test Test Results: STATISTIC: X-squared: 97.8732 P VALUE: Asymptotic p Value: < 2.2e-16 Title: Jarque - Bera Normality Test Test Results: PARAMETER: Sample Size: 248 STATISTIC: LM: 97.873 ALM: 105.288 P VALUE: ALM p-value: < 2.2e-16 Asymptotic: < 2.2e-16 Title: D'Agostino Normality Test Test Results: STATISTIC: Chi2 | Omnibus: 28.8972 Z3 | Skewness: 2.5469 Z4 | Kurtosis: 4.734 P VALUE: Omnibus Test: 5.309e-07 Skewness Test: 0.01087 Kurtosis Test: 2.202e-06 Title: Anderson - Darling Normality Test Test Results: STATISTIC: A: 1.5917 P VALUE: 0.0004206 Title: Cramer - von Mises Normality Test Test Results: STATISTIC: W: 0.195 P VALUE: 0.006156 Title: Lilliefors (KS) Normality Test Test Results: STATISTIC: D: 0.0559 P VALUE: 0.05848 Title: Pearson Chi-Square Normality Test Test Results: PARAMETER: Number of Classes: 19 STATISTIC: P: 16.4677 P VALUE: Adhusted: 0.4208 Not adjusted: 0.5599 Title: Shapiro - Francia Normality Test Test Results: STATISTIC: W: 0.9544 P VALUE: 2.225e-06 done successfully. Executing test function test.basicStats ... done successfully. Executing test function test.densityPlot ... done successfully. Executing test function test.distCheck ... Distribution Check for: norm Call: fBasics::distCheck() 1. Normalization Check: NORM 1 with absolute error < 9.4e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 2.205081e-17 3. r(1000) Check: MEAN VAR SAMPLE 0.0293 0.823 X 0 with absolute error < 0 X^2 1 with absolute error < 1.2e-07 MEAN VAR EXACT 0 1 done successfully. Executing test function test.histPlot ... done successfully. Executing test function test.qqnormPlot ... done successfully. Executing test function test.seriesPlot ... done successfully. Executing test function test.stdev ... done successfully. Executing test function test.acfPlot ... done successfully. Executing test function test.lacfPlot ... done successfully. Executing test function test.lmacfPlot ... done successfully. Executing test function test.logpdfPlot ... done successfully. Executing test function test.pacfPlot ... done successfully. Executing test function test.qqgausPlot ... done successfully. Executing test function test.scalinglawPlot ... Scaling Law: Open Plot Intercept 1.83971 Plot Slope 0.505934 Plot Inverse Slope 1.976542 Scaling Law: Open Plot Intercept 1.83971 Plot Slope 0.505934 Plot Inverse Slope 1.976542 done successfully. Executing test function test.teffectPlot ... done successfully. Executing test function test.Sys.putenv ... done successfully. Executing test function test.correlationTests ... Title: Pearson's Correlation Test Test Results: PARAMETER: Degrees of Freedom: 98 SAMPLE ESTIMATES: Correlation: -0.0661 STATISTIC: t: -0.656 P VALUE: Alternative Two-Sided: 0.5134 Alternative Less: 0.2567 Alternative Greater: 0.7433 CONFIDENCE INTERVAL: Two-Sided: -0.2592, 0.132 Less: -1, 0.1005 Greater: -0.2291, 1 Title: Kendall's tau Correlation Test Test Results: SAMPLE ESTIMATES: tau: 0.0085 STATISTIC: z: 0.1251 T | Exact: 2496 P VALUE: Alternative Two-Sided: 0.9005 Alternative Two-Sided | Exact: 0.9031 Alternative Less: 0.5498 Alternative Less | Exact: 0.5508 Alternative Greater: 0.4502 Alternative Greater | Exact: 0.4515 Title: Spearman's rho Correlation Test Test Results: SAMPLE ESTIMATES: rho: 0.0155 STATISTIC: S: 164068 P VALUE: Alternative Two-Sided: 0.8782 Alternative Less: 0.5609 Alternative Greater: 0.4391 done successfully. Executing test function test.distributionTest ... Title: Kolmogorov-Smirnov Two Sample Test Test Results: STATISTIC: D | Two Sided: 0.11 D^- | Less: 0.07 D^+ | Greater: 0.11 P VALUE: Alternative Two-Sided: 0.8042 Alternative Exact Two-Sided: 0.8042 Alternative Less: 0.7116 Alternative Greater: 0.4374 done successfully. Executing test function test.locationTests ... Title: t Test Test Results: PARAMETER: x Observations: 100 y Observations: 50 mu: 0 SAMPLE ESTIMATES: Mean of x: -1e-04 Mean of y: 0.0634 Var of x: 0.9851 Var of y: 2.2333 STATISTIC: T: -0.2721 T | Equal Var: -0.3102 P VALUE: Alternative Two-Sided: 0.7863 Alternative Less: 0.3932 Alternative Greater: 0.6068 Alternative Two-Sided | Equal Var: 0.7568 Alternative Less | Equal Var: 0.3784 Alternative Greater | Equal Var: 0.6216 CONFIDENCE INTERVAL: Two-Sided: -0.5291, 0.402 Less: -Inf, 0.3256 Greater: -0.4527, Inf Two-Sided | Equal Var: -0.4683, 0.3412 Less | Equal Var: -Inf, 0.2755 Greater | Equal Var: -0.4026, Inf Title: Kruskal-Wallis Two Sample Test Test Results: PARAMETER: x Observations: 100 y Observations: 50 SAMPLE ESTIMATES: Mean of x: -1e-04 Mean of y: 0.0634 Var of x: 0.9851 Var of y: 2.2333 STATISTIC: KW chi-squared: 0 P VALUE: 0.9968 done successfully. Executing test function test.scaleTests ... Title: Ansari-Bradley Test for Scale Test Results: STATISTIC: AB: 3864 P VALUE: Alternative Two-Sided : 0.6098 Alternative Two-Sided | Exact: 0.6138 Alternative Less : 0.3049 Alternative Less | Exact: 0.3069 Alternative Greater : 0.6951 Alternative Greater | Exact: 0.6959 CONFIDENCE INTERVAL: Two-Sided | Asymptotic : 0.6281, 1.3073 Two-Sided | Exact : 0.6266, 1.3085 Less | Asymptotic : 0, 1.2288 Less | Exact : 0, 1.2288 Greater | Asymptotic : 0.6746, Inf Greater | Exact : 0.6746, Inf Title: Mood Two-Sample Test of Scale Test Results: STATISTIC: Z: -0.9229 P VALUE: Alternative Two-Sided: 0.3561 Alternative Less: 0.178 Alternative Greater: 0.822 done successfully. Executing test function test.varianceTests ... Title: F Test of Variances Test Results: PARAMETER: Hypothesized Ratio: 1 Numerator df: 99 Denumerator df: 49 SAMPLE ESTIMATES: Ratio of Variances: 0.4411 STATISTIC: F: 0.4411 P VALUE: Alternative Two-Sided: 0.0005773 Alternative Less: 0.0002886 Alternative Greater: 0.9997 CONFIDENCE INTERVAL: Two-Sided: 0.2651, 0.7046 Less: 0, 0.6535 Greater: 0.2881, Inf Title: Bartlett Test for Homogeneity of Variances Test Results: STATISTIC: Bartlett's Chi-squared: 11.6484 P VALUE: 0.0006426 Title: Fligner-Killeen Test for Homogeneity of Variances Test Results: STATISTIC: FK:med chi-squared: 1.822 P VALUE: 0.1771 done successfully. Executing test function test.akimaInterp ... done successfully. Executing test function test.akimaInterpp ... done successfully. Executing test function test.as.matrix.ts ... done successfully. Executing test function test.as.align.default ... done successfully. Executing test function test.colnames.default ... done successfully. Executing test function test.rownames.default ... done successfully. Executing test function test.colStats ... done successfully. Executing test function test.colorLocator ... done successfully. Executing test function test.greyPalette ... done successfully. Executing test function test.colorTable ... done successfully. Executing test function test.decor ... done successfully. Executing test function test.description ... done successfully. Executing test function test.distCheck ... done successfully. Executing test function test.fHTEST ... done successfully. Executing test function test.isS4 ... done successfully. Executing test function test.gridVector ... done successfully. Executing test function test.interactivePlot ... done successfully. Executing test function test.fjulian ... done successfully. Executing test function test.krigeInterp ... done successfully. Executing test function test.kurtosis.POSIXct ... done successfully. Executing test function test.kurtosis.POSIXlt ... done successfully. Executing test function test.kurtosis.data.frame ... done successfully. Executing test function test.kurtosis.default ... done successfully. Executing test function test.portableInnovations ... done successfully. Executing test function test.randomNumbers ... done successfully. Executing test function test.linearInterp ... done successfully. Executing test function test.countFunctions ... fUtilities 0 done successfully. Executing test function test.listFunctions ... character(0) done successfully. Executing test function test.listIndex ... fBasics Index: DistributionFits Fit normal, Student-t and stable distributions Heaviside Heaviside and related functions HistogramPlot Histogram and density plots Ids Set and retrieve column/row names NormalityTests Tests for normality acfPlot Autocorrelation function plots akimaInterp Bivariate Spline Interpolation baseMethods Generic functions extensions basicStats Basic time series statistics boxPlot Time series box plots characterTable Table of characters colVec Column and row vectors colorLocator Named colors in R colorPalette Color palettes colorTable Table of colors correlationTest Correlation tests decor Functions for decorating plots dght Generalized Hyperbolic Student-t distribution distCheck Distribution check fBasics-deprecated Deprecated functions in package fBasics fBasics-package Portfolio modelling, optimization and backtesting fBasicsData fBasics data sets fDISTFIT-class Class '"fDISTFIT"' fHTEST-class Class '"fHTEST"' getS4 General S4 Class Extractor Functions gh Generalized Hyperbolic Distribution ghFit GH Distribution Fit ghMode Generalized Hyperbolic Mode ghMoments Generalized Hyperbolic Distribution Moments ghRobMoments Robust Moments for the GH ghSlider Generalized Hyperbolic Distribution Slider ghtFit GHT distribution fit ghtMode Generalized Hyperbolic Student-t Mode ghtMoments Generalized Hyperbolic Student-t Moments ghtRobMoments Robust Moments for the GHT gld Generalized Lambda Distribution gldFit GH Distribution Fit gldMode Generalized Lambda Distribution Mode gldRobMoments Robust Moments for the GLD gridVector Grid vector coordinates hilbert Hilbert matrix hyp Hyperbolic distribution hypFit Fit a hyperbolic distribution hypMode Hyperbolic mode hypMoments Hyperbolic distribution moments hypRobMoments Robust moments for the HYP hypSlider Hyperbolic distribution slider interactivePlot Interactive Plot Utility inv The inverse of a matrix krigeInterp Bivariate Krige Interpolation kron Kronecker product ks2Test Two sample Kolmogorov-Smirnov test lcg Generator for Portable random innovations linearInterp Bivariate Linear Interpolation listFunctions List exported functions in a package locationTest Two sample location tests maxdd Drawdown statistics nig Normal Inverse Gaussian Distribution nigFit Fit of a Normal Inverse Gaussian Distribution nigMode Normal Inverse Gaussian Mode nigMoments Moments for the Normal Inverse Gaussian nigRobMoments Robust Moments for the NIG nigShapeTriangle NIG Shape Triangle nigSlider nigerbolic Distribution Slider norm2 Matrix norm normRobMoments Robust moments for the Normal distribution pascal Pascal matrix pdl Polynomial distributed lags positiveDefinite Positive definite matrices print.control Print control qqnormPlot Quantile-Quantile plots returnSeriesGUI Return series plots rk The rank of a matrix rowStats Row statistics sampleLMoments Sample L-moments sampleRobMoments Robust moments for the GLD scaleTest Two sample scale tests scalinglawPlot Scaling law behaviour seriesPlot Financial time series plots sgh Standardized Generalized Hyperbolic Distribution sghFit Standardized GH distribution fit sght Standardized generalized hyperbolic Student-t Distribution snig Standardized Normal Inverse Gaussian Distribution snigFit Fit of a Standardized NIG Distribution ssd Spline Smoothed Distribution ssdFit Fit density using smoothing splines stableSlider Slider GUI for Stable Distribution symbolTable Table of symbols tr Trace of a matrix triang Upper and lower triangular matrices tsHessian Two sided approximated Hessian tslag Lagged or leading vector/matrix varianceTest Two sample variance tests vec Stacking vectors and matrices volatility Compute volatility NULL done successfully. Executing test function test.apply ... done successfully. Executing test function test.creation ... done successfully. Executing test function test.linearAlgebra ... done successfully. Executing test function test.mathOps ... done successfully. Executing test function test.moreOperations ... done successfully. Executing test function test.subsets ... done successfully. Executing test function test.plot ... done successfully. Executing test function test.print ... done successfully. Executing test function test.hexCode ... done successfully. Executing test function test.colStats ... done successfully. Executing test function test.skewness.POSIXct ... done successfully. Executing test function test.skewness.POSIXlt ... done successfully. Executing test function test.skewness.data.frame ... done successfully. Executing test function test.skewness.default ... done successfully. Executing test function test.sliderMenu ... done successfully. Executing test function test.subplot ... done successfully. Executing test function test.subplot ... done successfully. Executing test function test.unitrootNA ... done successfully. RUNIT TEST PROTOCOL -- Mon Aug 19 10:29:42 2024 *********************************************** Number of test functions: 84 Number of errors: 0 Number of failures: 0 1 Test Suite : fBasics unit testing - 84 test functions, 0 errors, 0 failures Warning messages: 1: In sqrt(diag(fit$cvar)) : NaNs produced 2: In ks.test.default(x, "pnorm", alternative = "two.sided") : ties should not be present for the one-sample Kolmogorov-Smirnov test 3: In ks.test.default(x, "pnorm", alternative = "less") : ties should not be present for the one-sample Kolmogorov-Smirnov test 4: In ks.test.default(x, "pnorm", alternative = "greater") : ties should not be present for the one-sample Kolmogorov-Smirnov test > > proc.time() user system elapsed 6.48 0.89 7.37