* using log directory 'd:/Rcompile/CRANpkg/local/3.3/uwIntroStats.Rcheck' * using R version 3.3.3 (2017-03-06) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * checking for file 'uwIntroStats/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'uwIntroStats' version '0.0.5' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'uwIntroStats' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... 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ERROR Running examples in 'uwIntroStats-Ex.R' failed The error most likely occurred in: > ### Name: regress > ### Title: General Regression for an Arbitrary Functional > ### Aliases: regress fitted.uRegress print.augCoefficients print.uRegress > ### uLRtest uWaldtest termTraverse explode indentNames getLevels testList > ### pasteTwo processTerm addArgs pasteOn pasteOnSpline pastePair > ### movingSum myNext reFormatReg createCols checkNesting splitOnParen > ### reFormat equal > ### Keywords: ~kwd1 ~kwd2 > > ### ** Examples > > # Loading required libraries > library(survival) > library(sandwich) > > # Reading in a dataset > mri <- read.table("http://www.emersonstatistics.com/datasets/mri.txt",header=TRUE) > > # Creating a Surv object to reflect time to death > mri$ttodth <- Surv(mri$obstime,mri$death) > > # Attaching the mri dataset > attach(mri) > > # Linear regression of atrophy on age > regress("mean", atrophy~age, data=mri) Call: regress(fnctl = "mean", formula = atrophy ~ age, data = mri) Residuals: Min 1Q Median 3Q Max -36.870 -8.589 -0.870 7.666 51.203 Coefficients: Estimate Naive SE Robust SE 95%L 95%H [1] Intercept -16.06 6.256 6.701 -29.22 -2.907 [2] age 0.6980 0.08368 0.09002 0.5213 0.8747 F stat df Pr(>F) [1] Intercept 5.75 1 0.0168 [2] age 60.12 1 < 0.00005 Residual standard error: 12.36 on 733 degrees of freedom Multiple R-squared: 0.08669, Adjusted R-squared: 0.08545 F-statistic: 60.12 on 1 and 733 DF, p-value: 2.988e-14 > > ## Linear regression of atrophy on male and race and their interaction, > ## with a multiple-partial F-test on the race-age interaction > regress("mean", atrophy~ male + U(ra=~race*age), data=mri) Call: regress(fnctl = "mean", formula = atrophy ~ male + U(ra = ~race * age), data = mri) Residuals: Min 1Q Median 3Q Max -33.438 -8.368 -0.462 7.372 53.253 Coefficients: Estimate Naive SE Robust SE 95%L 95%H [1] Intercept 2.199 13.24 14.53 -26.33 30.73 [2] male 6.003 0.8850 0.8815 4.273 7.734 ra [3] race -15.30 8.902 9.660 -34.26 3.664 [4] age 0.4255 0.1770 0.1943 0.04399 0.8070 [5] race:age 0.1954 0.1186 0.1287 -0.05726 0.4481 F stat df Pr(>F) [1] Intercept 0.02 1 0.8798 [2] male 46.37 1 < 0.00005 ra 21.51 3 < 0.00005 [3] race 2.51 1 0.1137 [4] age 4.79 1 0.0289 [5] race:age 2.31 1 0.1293 Residual standard error: 11.99 on 730 degrees of freedom Multiple R-squared: 0.1443, Adjusted R-squared: 0.1397 F-statistic: 27.23 on 4 and 730 DF, p-value: < 2.2e-16 > > ## Linear regression of atrophy on age, male, race (as a dummy variable), chf, > ## and diabetes. There are two multiple partial F-tests and both are named > regress("mean", atrophy~age+male+U(rc=~dummy(race)+chf)+U(md=~male+diabetes), data=mri) Call: regress(fnctl = "mean", formula = atrophy ~ age + male + U(rc = ~dummy(race) + chf) + U(md = ~male + diabetes), data = mri) Residuals: Min 1Q Median 3Q Max -34.003 -8.611 -0.572 7.279 54.064 Coefficients: Estimate Naive SE Robust SE 95%L 95%H [1] Intercept -17.95 6.088 6.593 -30.90 -5.009 [2] age 0.6862 0.08142 0.08840 0.5127 0.8598 [3] male 5.879 0.8952 0.8942 4.123 7.635 rc dummy(race) [4] race.2 -2.147 1.285 1.255 -4.611 0.3176 [5] race.3 0.1829 1.826 1.796 -3.343 3.709 [6] race.4 -2.734 3.508 3.798 -10.19 4.722 [7] chf 1.919 1.943 2.179 -2.358 6.197 md male 5.879 0.8952 0.8942 4.123 7.635 [8] diabetes 0.6524 1.450 1.267 -1.836 3.141 F stat df Pr(>F) [1] Intercept 7.41 1 0.0066 [2] age 60.25 1 < 0.00005 [3] male 43.23 1 < 0.00005 rc 1.09 4 0.3598 dummy(race) 1.14 3 0.3336 [4] race.2 2.92 1 0.0877 [5] race.3 0.01 1 0.9189 [6] race.4 0.52 1 0.4718 [7] chf 0.78 1 0.3787 md 22.84 2 < 0.00005 male 43.23 1 < 0.00005 [8] diabetes 0.26 1 0.6069 Dummy terms calculated from race, reference = 1 Residual standard error: 12 on 727 degrees of freedom Multiple R-squared: 0.1453, Adjusted R-squared: 0.1371 F-statistic: 15.31 on 7 and 727 DF, p-value: < 2.2e-16 > > ## Proportional hazards regression clustered on id > ## (here it makes no difference because the ids are unique) > regress("hazard", ttodth~ldl, id=ptid, data=mri) Warning in coxph(formula, data = data, ...) : a variable appears on both the left and right sides of the formula Error in eval(predvars, data, env) : invalid 'envir' argument of type 'closure' Calls: regress ... eval -> eval -> -> model.frame.default -> eval Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... OK * checking re-building of vignette outputs ... [5s] WARNING Error in re-building vignettes: ... * checking PDF version of manual ... OK * DONE Status: 1 ERROR, 1 WARNING