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> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("NADA-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('NADA') Loading required package: survival Loading required package: splines > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "Cen" > > ### * Cen > > flush(stderr()); flush(stdout()) > > ### Name: Cen > ### Title: Create a Censored Object > ### Aliases: Cen > ### Keywords: survival > > ### ** Examples > > obs = c(0.5, 0.5, 1.0, 1.5, 5.0, 10, 100) > censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE) > > Cen(obs, censored) [1] < 0.5 0.5 1.0 < 1.5 5.0 10.0 100.0 > flip(Cen(obs, censored)) [1] 99.5+ 99.5 99.0 98.5+ 95.0 90.0 0.0 > > > > cleanEx(); ..nameEx <- "cendiff" > > ### * cendiff > > flush(stderr()); flush(stdout()) > > ### Name: cendiff > ### Title: Test Censored ECDF Differences > ### Aliases: cendiff > ### Keywords: survival > > ### ** Examples > > > data(Cadmium) > > obs = Cadmium$Cd > censored = Cadmium$CdCen > groups = Cadmium$Region > > # Cd differences between regions? > cendiff(obs, censored, groups) N Observed Expected (O-E)^2/E (O-E)^2/V groups=COLOPLT 9 2.84 6.13 1.76 7.02 groups=SRKYMT 10 6.84 3.55 3.05 7.02 Chisq= 7 on 1 degrees of freedom, p= 0.00808 > > # Same as above using formula interface > cenfit(Cen(obs, censored)~groups) n n.cen median mean se(mean) groups=COLOPLT 9 3 0.4 0.5622222 0.1133135 groups=SRKYMT 10 1 3.0 10.5000000 7.4795321 > > > > cleanEx(); ..nameEx <- "cenfit-class" > > ### * cenfit-class > > flush(stderr()); flush(stdout()) > > ### Name: cenfit-class > ### Title: Class "cenfit" > ### Aliases: cenfit-class [,cenfit,numeric,missing-method > ### Keywords: classes > > ### ** Examples > > obs = c(0.5, 0.5, 1.0, 1.5, 5.0, 10, 100) > censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE) > > class(cenfit(Cen(obs, censored))) [1] "cenfit" attr(,"package") [1] "NADA" > > > > cleanEx(); ..nameEx <- "cenfit-methods" > > ### * cenfit-methods > > flush(stderr()); flush(stdout()) > > ### Name: cenfit-methods > ### Title: Methods for function cenfit in Package NADA > ### Aliases: cenfit-methods cenfit,formula,missing,missing-method > ### cenfit,Cen,missing,missing-method > ### cenfit,numeric,logical,missing-method > ### cenfit,numeric,logical,factor-method > ### Keywords: methods > > ### ** Examples > > data(Atrazine) > > cenfit(Atrazine$Atra, Atrazine$AtraCen) n n.cen median mean se(mean) 48.000000 14.000000 0.030000 2.007292 1.819023 > cenfit(Atrazine$Atra, Atrazine$AtraCen, Atrazine$Month) n n.cen median mean se(mean) Atrazine$Month=June 24 9 0.02 0.04416667 0.01519453 Atrazine$Month=Sept 24 5 0.06 3.97041669 3.59360355 > > cenfit(Cen(Atrazine$Atra, Atrazine$AtraCen)) n n.cen median mean se(mean) 48.000000 14.000000 0.030000 2.007292 1.819023 > cenfit(Cen(Atrazine$Atra, Atrazine$AtraCen)~Atrazine$Month) n n.cen median mean se(mean) Atrazine$Month=June 24 9 0.02 0.04416667 0.01519453 Atrazine$Month=Sept 24 5 0.06 3.97041669 3.59360355 > > > > cleanEx(); ..nameEx <- "cenfit" > > ### * cenfit > > flush(stderr()); flush(stdout()) > > ### Name: cenfit > ### Title: Compute an ECDF for Censored Data > ### Aliases: cenfit > ### Keywords: survival > > ### ** Examples > > > # Create a Kaplan-Meier ECDF, plot and summarize it. > > data(Cadmium) > > obs = Cadmium$Cd > censored = Cadmium$CdCen > > mycenfit = cenfit(obs, censored) > > plot(mycenfit) > summary(mycenfit) obs n.risk n.event prob std.err 0.95LCL 0.95UCL 1 0.2 1 0 0.1842105 0.50684537 0.0682161 0.4974415 2 0.3 2 0 0.1842105 0.50684537 0.0682161 0.4974415 3 0.4 6 3 0.1842105 0.50684537 0.0682161 0.4974415 4 0.6 9 2 0.3684211 0.30037570 0.2044863 0.6637808 5 0.7 10 1 0.4736842 0.24182542 0.2948810 0.7609061 6 0.8 11 1 0.5263158 0.21764288 0.3435488 0.8063143 7 1.4 12 1 0.5789474 0.19564640 0.3945523 0.8495200 8 2.9 13 1 0.6315789 0.17521916 0.4480029 0.8903781 9 3.0 14 1 0.6842105 0.15585730 0.5041082 0.9286578 10 3.4 15 1 0.7368421 0.13710212 0.5632133 0.9639976 11 3.5 16 1 0.7894737 0.11846978 0.6258871 0.9958165 12 4.6 17 1 0.8421053 0.09933993 0.6931194 1.0000000 13 4.9 18 1 0.8947368 0.07868895 0.7668584 1.0000000 14 81.3 19 1 0.9473684 0.05407381 0.8521012 1.0000000 > quantile(mycenfit, conf.int=TRUE) quantile obs 0.95LCL 0.95UCL 1 0.05 0.2 0.0682161 0.4974415 2 0.10 0.2 0.0682161 0.4974415 3 0.25 0.4 0.0682161 0.4974415 4 0.50 0.7 0.2948810 0.7609061 5 0.75 3.4 0.5632133 0.9639976 6 0.90 4.9 0.7668584 1.0000000 7 0.95 81.3 0.8521012 1.0000000 > median(mycenfit) [1] 0.7 > mean(mycenfit) rmean se(rmean) 5.778947 4.099211 > sd(mycenfit) [1] 17.86804 > predict(mycenfit, c(10, 20, 100), conf.int=TRUE) obs prob 0.95LCL 0.95UCL 1 10 0.9473684 0.8521012 1 2 20 0.9473684 0.8521012 1 3 100 0.9473684 0.8521012 1 > > # With groups > groups = Cadmium$Region > > cenfit(obs, censored, groups) n n.cen median mean se(mean) groups=COLOPLT 9 3 0.4 0.5622222 0.1133135 groups=SRKYMT 10 1 3.0 10.5000000 7.4795321 > > # Formula interface -- no groups > cenfit(Cen(obs, censored)) n n.cen median mean se(mean) 19.000000 4.000000 0.700000 5.778947 4.099211 > > # Formula interface -- with groups > cenfit(Cen(obs, censored)~groups) n n.cen median mean se(mean) groups=COLOPLT 9 3 0.4 0.5622222 0.1133135 groups=SRKYMT 10 1 3.0 10.5000000 7.4795321 > > > > cleanEx(); ..nameEx <- "hc.ppoints" > > ### * hc.ppoints > > flush(stderr()); flush(stdout()) > > ### Name: hc.ppoints > ### Title: Helsel-Cohn style plotting positions > ### Aliases: hc.ppoints hc.ppoints.uncen hc.ppoints.cen > ### Keywords: regression > > ### ** Examples > > obs = c(0.5, 0.5, 1.0, 1.5, 5.0, 10, 100) > censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE) > > hc.ppoints(obs, censored) [1] 0.1428571 0.3809524 0.4761905 0.2857143 0.6785714 0.7857143 0.8928571 > > > > cleanEx(); ..nameEx <- "pct.censored" > > ### * pct.censored > > flush(stderr()); flush(stdout()) > > ### Name: pct.censored > ### Title: Calculate the percentage of values censored > ### Aliases: pct.censored > ### Keywords: utilities > > ### ** Examples > > obs = c(0.5, 0.5, 1.0, 1.5, 5.0, 10, 100) > censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE) > > pct.censored(obs, censored) [1] 28.57143 > > > > cleanEx(); ..nameEx <- "quantile-methods" > > ### * quantile-methods > > flush(stderr()); flush(stdout()) > > ### Name: quantile-methods > ### Title: Methods for function quantile in Package NADA > ### Aliases: quantile-methods quantile,ANY-method quantile,cenfit-method > ### quantile,ros-method NADAprobs > ### Keywords: methods > > ### ** Examples > > > data(Cadmium) > > mymodel = cenfit(Cadmium$Cd, Cadmium$CdCen, Cadmium$Region) > > quantile(mymodel, conf.int=TRUE) Cadmium$Region=COLOPLT quantile obs 0.95LCL 0.95UCL 1 0.05 0.3 0.08287245 0.858079 2 0.10 0.3 0.08287245 0.858079 3 0.25 0.3 0.08287245 0.858079 4 0.50 0.4 0.08287245 0.858079 5 0.75 0.7 0.42002836 1.000000 6 0.90 1.4 0.70555750 1.000000 7 0.95 1.4 0.70555750 1.000000 Cadmium$Region=SRKYMT quantile obs 0.95LCL 0.95UCL 1 0.05 0.2 0.01557684 0.6419788 2 0.10 0.2 0.01557684 0.6419788 3 0.25 0.6 0.01557684 0.6419788 4 0.50 3.0 0.18723673 0.8545332 5 0.75 4.6 0.46653323 1.0000000 6 0.90 81.3 0.73201164 1.0000000 7 0.95 81.3 0.73201164 1.0000000 > > > > cleanEx(); ..nameEx <- "ros-class" > > ### * ros-class > > flush(stderr()); flush(stdout()) > > ### Name: ros-class > ### Title: Class "ros" > ### Aliases: ros-class > ### Keywords: classes > > ### ** Examples > > obs = c(0.5, 0.5, 1.0, 1.5, 5.0, 10, 100) > censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE) > > class(ros(obs, censored)) [1] "ros" "lm" > > > > cleanEx(); ..nameEx <- "ros" > > ### * ros > > flush(stderr()); flush(stdout()) > > ### Name: ros > ### Title: Regression on Order Statistics > ### Aliases: ros lros > ### Keywords: regression > > ### ** Examples > > obs = c(0.5, 0.5, 1.0, 1.5, 5.0, 10, 100) > censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE) > > myros = ros(obs, censored) > > plot(myros) > summary(myros) Call: lm(formula = obs.transformed ~ pp.nq) Residuals: 1 2 3 4 5 0.14292 0.03587 -0.07644 -0.46198 0.35963 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1606 0.1976 0.813 0.47591 pp.nq 3.2894 0.2806 11.724 0.00133 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3513 on 3 degrees of freedom Multiple R-Squared: 0.9786, Adjusted R-squared: 0.9715 F-statistic: 137.5 on 1 and 3 DF, p-value: 0.001333 > mean(myros); sd(myros) [1] 16.67393 [1] 36.92367 > quantile(myros); median(myros) 5% 10% 25% 50% 75% 90% 0.07927864 0.12351142 0.34124423 1.00000000 7.50000000 46.00000000 95% 73.00000000 [1] 1 > as.data.frame(myros) obs censored pp modeled 1 0.5 TRUE 0.1428571 0.03504586 2 0.5 FALSE 0.3809524 0.50000000 3 1.0 FALSE 0.4761905 1.00000000 4 1.5 TRUE 0.2857143 0.18248846 5 5.0 FALSE 0.6785714 5.00000000 6 10.0 FALSE 0.7857143 10.00000000 7 100.0 FALSE 0.8928571 100.00000000 > > > > cleanEx(); ..nameEx <- "splitQual" > > ### * splitQual > > flush(stderr()); flush(stdout()) > > ### Name: splitQual > ### Title: Split character qualifiers and numeric values from qualified > ### data > ### Aliases: splitQual > ### Keywords: utilities > > ### ** Examples > > v = c('<1', 1, '<1', 1, 2) > splitQual(v) $qual [1] 1 1 $unqual [1] 1 1 2 $qual.index [1] 1 3 $unqual.index [1] -1 -3 > > > > ### *