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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("alr3-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('alr3') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "BGSall" > > ### * BGSall > > flush(stderr()); flush(stdout()) > > ### Name: BGSall > ### Title: Berkeley guidance study > ### Aliases: BGSall BGSboys BGSgirls > ### Keywords: datasets > > ### ** Examples > > data(BGSall) > data(BGSboys) > data(BGSgirls) > > > > cleanEx(); ..nameEx <- "BigMac2003" > > ### * BigMac2003 > > flush(stderr()); flush(stdout()) > > ### Name: BigMac2003 > ### Title: World cities data > ### Aliases: BigMac2003 > ### Keywords: datasets > > ### ** Examples > > data(BigMac2003) > > > > cleanEx(); ..nameEx <- "MWwords" > > ### * MWwords > > flush(stderr()); flush(stdout()) > > ### Name: MWwords > ### Title: Word frequencies from Mosteller and Wallace > ### Aliases: MWwords > ### Keywords: datasets > > ### ** Examples > > data(MWwords) > > > > cleanEx(); ..nameEx <- "Mitchell" > > ### * Mitchell > > flush(stderr()); flush(stdout()) > > ### Name: Mitchell > ### Title: Mitchell soil temperature > ### Aliases: Mitchell > ### Keywords: datasets > > ### ** Examples > > data(Mitchell) > > > > cleanEx(); ..nameEx <- "UN1" > > ### * UN1 > > flush(stderr()); flush(stdout()) > > ### Name: UN1 > ### Title: National statistics from the United Nations > ### Aliases: UN1 > ### Keywords: datasets > > ### ** Examples > > data(UN1) > > > > cleanEx(); ..nameEx <- "UN2" > > ### * UN2 > > flush(stderr()); flush(stdout()) > > ### Name: UN2 > ### Title: National statistics from the United Nations > ### Aliases: UN2 > ### Keywords: datasets > > ### ** Examples > > data(UN2) > > > > cleanEx(); ..nameEx <- "UN3" > > ### * UN3 > > flush(stderr()); flush(stdout()) > > ### Name: UN3 > ### Title: National statistics from the United Nations > ### Aliases: UN3 > ### Keywords: datasets > > ### ** Examples > > data(UN3) > > > > cleanEx(); ..nameEx <- "ais" > > ### * ais > > flush(stderr()); flush(stdout()) > > ### Name: ais > ### Title: Australian institute of sport data > ### Aliases: ais > ### Keywords: datasets > > ### ** Examples > > data(ais) > > > > cleanEx(); ..nameEx <- "allshoots" > > ### * allshoots > > flush(stderr()); flush(stdout()) > > ### Name: allshoots > ### Title: Apple shoots data > ### Aliases: longshoots shortshoots allshoots > ### Keywords: datasets > > ### ** Examples > > data(longshoots) > > > > cleanEx(); ..nameEx <- "anscombe" > > ### * anscombe > > flush(stderr()); flush(stdout()) > > ### Name: anscombe > ### Title: Anscombe's data on graphics > ### Aliases: anscombe > ### Keywords: datasets > > ### ** Examples > > data(anscombe) > > > > cleanEx(); ..nameEx <- "baeskel" > > ### * baeskel > > flush(stderr()); flush(stdout()) > > ### Name: baeskel > ### Title: Surface tension > ### Aliases: baeskel > ### Keywords: datasets > > ### ** Examples > > data(baeskel) > > > > cleanEx(); ..nameEx <- "banknote" > > ### * banknote > > flush(stderr()); flush(stdout()) > > ### Name: banknote > ### Title: Swiss banknote data > ### Aliases: banknote > ### Keywords: datasets > > ### ** Examples > > data(banknote) > > > > cleanEx(); ..nameEx <- "bctrans" > > ### * bctrans > > flush(stderr()); flush(stdout()) > > ### Name: bctrans > ### Title: Univariate and multivariate transformations to normality > ### Aliases: bctrans bctrans1 lrt.bctrans print.bctrans > ### Keywords: models regression > > ### ** Examples > > data(highway) > b <- highway[,c(8,1,2,10,5)] # select interesting columns > summary(ans <- bctrans1(b,family="yeo.johnson")) # zeros ==> use yeo.johnson yeo.johnson Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) Len 0.1053 0.2437 0.4321 -3.6721 ADT -0.0479 0.1440 -0.3323 -7.2763 Trks -1.0920 0.7089 -1.5403 -2.9509 Shld 1.5474 0.4483 3.4517 1.2210 Sigs -2.2883 0.6701 -3.4147 -4.9070 LRT df p.value LR test, all lambda equal 0 34.0220 5 2.357115e-06 LR test, all lambda equal 1 101.5205 5 0.000000e+00 > # or, compute using a formula and get the same answer. > summary(ans2 <- bctrans(~Len+ADT+Trks+Shld+Sigs,data=highway,family="yeo.johnson")) yeo.johnson Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) Len 0.1053 0.2437 0.4321 -3.6721 ADT -0.0479 0.1440 -0.3323 -7.2763 Trks -1.0920 0.7089 -1.5403 -2.9509 Shld 1.5474 0.4483 3.4517 1.2210 Sigs -2.2883 0.6701 -3.4147 -4.9070 LRT df p.value LR test, all lambda equal 0 34.0220 5 2.357115e-06 LR test, all lambda equal 1 101.5205 5 0.000000e+00 > # or, first fit an lm, and extract the formula > m1 <- lm(Rate~Len+ADT+Trks+Shld+Sigs,data=highway) > summary(ans3 <- bctrans(formula(m1),data=highway,family="yeo.johnson")) yeo.johnson Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) Len 0.1053 0.2437 0.4321 -3.6721 ADT -0.0479 0.1440 -0.3323 -7.2763 Trks -1.0920 0.7089 -1.5403 -2.9509 Shld 1.5474 0.4483 3.4517 1.2210 Sigs -2.2883 0.6701 -3.4147 -4.9070 LRT df p.value LR test, all lambda equal 0 34.0220 5 2.357115e-06 LR test, all lambda equal 1 101.5205 5 0.000000e+00 > # work with the response > b$Sigs <- (round(b$Sigs*b$Len)+1)/b$Len # redefine so no zeroes > summary(ans <- bctrans1(b)) # fit with box.cox box.cox Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) Len 0.1429 0.2126 0.6722 -4.0313 ADT 0.0501 0.1204 0.4160 -7.8863 Trks -0.7019 0.6178 -1.1361 -2.7547 Shld 1.3455 0.3631 3.7062 0.9517 Sigs -0.2440 0.1498 -1.6287 -8.3034 LRT df p.value LR test, all lambda equal 0 23.37304 5 0.0002864005 LR test, all lambda equal 1 133.17876 5 0.0000000000 > lrt.bctrans(ans,lrt=list(c(0,0,-1,1,0))) LRT df p.value LR test, all lambda equal 0 23.373039 5 0.0002864005 LR test, all lambda equal 1 133.178761 5 0.0000000000 LR test, lambda = 0 0 -1 1 0 4.392583 5 0.4943827817 > plot(ans,family="power") # plot, but use ordinary powers > b <- cbind(b,powtran(ans)) # add transformed variables to data frame > > > > cleanEx(); ..nameEx <- "blowdown" > > ### * blowdown > > flush(stderr()); flush(stdout()) > > ### Name: blowdown > ### Title: Blowdown data > ### Aliases: blowdown blowAPB blowBF > ### Keywords: datasets > > ### ** Examples > > data(blowBF) > > > > cleanEx(); ..nameEx <- "boot.case" > > ### * boot.case > > flush(stderr()); flush(stdout()) > > ### Name: boot.case > ### Title: Case bootstrap for regression models > ### Aliases: boot.case boot.case.default boot.case.nls next.boot > ### next.boot.default next.boot.nls > ### Keywords: regression internal > > ### ** Examples > > data(transact) > m1 <- lm(Time~ T1 + T2, data=transact) > betahat <- coef(m1) > betahat.boot <- boot.case(m1,B=99) # 99 bootstrap samples--too small to be useful > summary(betahat.boot) # default summary X.Intercept. T1 T2 Min. :-273.00 Min. :3.721 Min. :1.671 1st Qu.: 53.72 1st Qu.:5.095 1st Qu.:1.920 Median : 160.23 Median :5.596 Median :2.012 Mean : 163.43 Mean :5.541 Mean :2.017 3rd Qu.: 283.53 3rd Qu.:5.997 3rd Qu.:2.118 Max. : 669.49 Max. :7.149 Max. :2.360 > # bootstrap standard errors > apply(betahat.boot,2,sd) (Intercept) T1 T2 187.2807450 0.7189440 0.1503252 > # bootstrap 95 > cl <- function(x) quantile(x,c(.025,.975)) > apply(betahat.boot,2,cl) (Intercept) T1 T2 2.5% -211.7724 4.157844 1.722338 97.5% 552.8808 6.860916 2.310432 > > > > > cleanEx(); ..nameEx <- "brains" > > ### * brains > > flush(stderr()); flush(stdout()) > > ### Name: brains > ### Title: Mammal brain weights > ### Aliases: brains > ### Keywords: datasets > > ### ** Examples > > data(brains) > > > > cleanEx(); ..nameEx <- "cakes" > > ### * cakes > > flush(stderr()); flush(stdout()) > > ### Name: cakes > ### Title: Cakes data > ### Aliases: cakes > ### Keywords: datasets > > ### ** Examples > > data(cakes) > lm(Y~X1+X2+I(X1^2)+I(X2^2)+X1:X2, data=cakes) Call: lm(formula = Y ~ X1 + X2 + I(X1^2) + I(X2^2) + X1:X2, data = cakes) Coefficients: (Intercept) X1 X2 I(X1^2) I(X2^2) X1:X2 -2.204e+03 2.592e+01 9.918e+00 -1.569e-01 -1.195e-02 -4.162e-02 > > > > > cleanEx(); ..nameEx <- "cathedral" > > ### * cathedral > > flush(stderr()); flush(stdout()) > > ### Name: cathedral > ### Title: Cathedrals > ### Aliases: cathedral > ### Keywords: datasets > > ### ** Examples > > data(cathedral) > > > > cleanEx(); ..nameEx <- "caution" > > ### * caution > > flush(stderr()); flush(stdout()) > > ### Name: caution > ### Title: Caution data > ### Aliases: caution > ### Keywords: datasets > > ### ** Examples > > data(caution) > > > > cleanEx(); ..nameEx <- "challeng" > > ### * challeng > > flush(stderr()); flush(stdout()) > > ### Name: challeng > ### Title: Challenger data > ### Aliases: challeng > ### Keywords: datasets > > ### ** Examples > > data(challeng) > > > > cleanEx(); ..nameEx <- "chloride" > > ### * chloride > > flush(stderr()); flush(stdout()) > > ### Name: chloride > ### Title: Chloride data > ### Aliases: chloride > ### Keywords: datasets > > ### ** Examples > > data(chloride) > library(nlme) # Use the mixed-effects package > # Fit a random coefficients model > summary(m1 <- lme(Cl~Month+Type, data=chloride, random=~1|Marsh)) Linear mixed-effects model fit by REML Data: chloride AIC BIC logLik 229.6910 236.5274 -109.8455 Random effects: Formula: ~1 | Marsh (Intercept) Residual StdDev: 13.33542 6.387205 Fixed effects: Cl ~ Month + Type Value Std.Error DF t-value p-value (Intercept) -5.50383 7.717221 22 -0.713188 0.4832 Month 1.85381 0.529897 22 3.498427 0.0020 TypeRoadside 50.57187 9.253148 7 5.465370 0.0009 Correlation: (Intr) Month Month -0.448 TypeRoadside -0.664 -0.006 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.76576225 -0.55836362 0.01713448 0.47871248 2.03492555 Number of Observations: 32 Number of Groups: 9 > > > > cleanEx(); ..nameEx <- "cloud" > > ### * cloud > > flush(stderr()); flush(stdout()) > > ### Name: cloud > ### Title: Florida area cumulus experiment, FACE I. > ### Aliases: cloud > ### Keywords: datasets > > ### ** Examples > > data(cloud) > > > > cleanEx(); ..nameEx <- "conf.intervals" > > ### * conf.intervals > > flush(stderr()); flush(stdout()) > > ### Name: conf.intervals > ### Title: Compute marginal confidence intervals for regression estimates > ### Aliases: conf.intervals conf.intervals.default conf.intervals.lm > ### Keywords: regression > > ### ** Examples > > data(forbes) > m1 <- lm(Lpres ~ Temp, data = forbes) > conf.intervals(m1, level = .95) # .95 is the default. Coef est Lower Upper (Intercept) -42.1377793 -49.2572447 -35.0183139 Temp 0.8954937 0.8604276 0.9305598 > > > > cleanEx(); ..nameEx <- "delta.method" > > ### * delta.method > > flush(stderr()); flush(stdout()) > > ### Name: delta.method > ### Title: Standard error of a nonlinear function of regression > ### coefficients > ### Aliases: delta.method delta.method.default delta.method.nls > ### compute.delta.method > ### Keywords: models regression > > ### ** Examples > > # cakes is a data frame with response Y, predictors X1 X2 > data(cakes) > m1 <- lm(Y~ X2 + I(X2^2), data = cakes) # quadratic polynomial > delta.method(m1, "-b1/(2*b2)") # X2 that maximizes the quadratic Functions of parameters: expression(-b1/(2 * b2)) Estimate = 354.2029 with se = 2.089266 > # second order polynomial in two predictors: > m2 <- lm(Y ~ X1 + X2 + I(X1^2) + I(X2^2) + X1:X2, data=cakes) > # Find X1 to maximize Y when X2=350: > delta.method(m2,"(b1+b5*350)/(-2*b3)") Functions of parameters: expression((b1 + b5 * 350)/(-2 * b3)) Estimate = 36.1715 with se = 0.3809635 > > > > cleanEx(); ..nameEx <- "domedata" > > ### * domedata > > flush(stderr()); flush(stdout()) > > ### Name: domedata > ### Title: Metrodome fan experiment > ### Aliases: domedata domedata1 > ### Keywords: datasets > > ### ** Examples > > data(domedata1) > > > > cleanEx(); ..nameEx <- "donner" > > ### * donner > > flush(stderr()); flush(stdout()) > > ### Name: donner > ### Title: Donner party > ### Aliases: donner > ### Keywords: datasets > > ### ** Examples > > data(donner) > > > > cleanEx(); ..nameEx <- "downer" > > ### * downer > > flush(stderr()); flush(stdout()) > > ### Name: downer > ### Title: Downer data > ### Aliases: downer > ### Keywords: datasets > > ### ** Examples > > data(downer) > > > > cleanEx(); ..nameEx <- "drugcost" > > ### * drugcost > > flush(stderr()); flush(stdout()) > > ### Name: drugcost > ### Title: Drug cost. > ### Aliases: drugcost > ### Keywords: datasets > > ### ** Examples > > data(drugcost) > > > > cleanEx(); ..nameEx <- "dwaste" > > ### * dwaste > > flush(stderr()); flush(stdout()) > > ### Name: dwaste > ### Title: Crock data. > ### Aliases: dwaste > ### Keywords: datasets > > ### ** Examples > > data(dwaste) > > > > cleanEx(); ..nameEx <- "florida" > > ### * florida > > flush(stderr()); flush(stdout()) > > ### Name: florida > ### Title: Florida presidential election > ### Aliases: florida > ### Keywords: datasets > > ### ** Examples > > data(florida) > ## maybe str(florida) ; plot(florida) ... > > > > cleanEx(); ..nameEx <- "forbes" > > ### * forbes > > flush(stderr()); flush(stdout()) > > ### Name: forbes > ### Title: Forbes data > ### Aliases: forbes > ### Keywords: datasets > > ### ** Examples > > data(forbes) > > > > cleanEx(); ..nameEx <- "ftcollinssnow" > > ### * ftcollinssnow > > flush(stderr()); flush(stdout()) > > ### Name: ftcollinssnow > ### Title: Ft. Collins snowfall > ### Aliases: ftcollinssnow > ### Keywords: datasets > > ### ** Examples > > data(ftcollinssnow) > > > > cleanEx(); ..nameEx <- "fuel2001" > > ### * fuel2001 > > flush(stderr()); flush(stdout()) > > ### Name: fuel2001 > ### Title: Fuel consumption > ### Aliases: fuel2001 > ### Keywords: datasets > > ### ** Examples > > data(fuel2001) > fuel2001$Dlic <- 1000*fuel2001$Drivers/fuel2001$Pop > fuel2001$Fuel <- 1000*fuel2001$FuelC/fuel2001$Pop > fuel2001$Income <- fuel2001$Income/1000 > fuel2001$logMiles <- log(fuel2001$Miles,2) > pairs(Fuel~Tax+Dlic+Income+logMiles,data=fuel2001) > > > > cleanEx(); ..nameEx <- "galapagos" > > ### * galapagos > > flush(stderr()); flush(stdout()) > > ### Name: galapagos > ### Title: Galapagos species data > ### Aliases: galapagos > ### Keywords: datasets > > ### ** Examples > > data(galapagos) > > > > cleanEx(); ..nameEx <- "galtonpeas" > > ### * galtonpeas > > flush(stderr()); flush(stdout()) > > ### Name: galtonpeas > ### Title: Galton's peas > ### Aliases: galtonpeas > ### Keywords: datasets > > ### ** Examples > > data(galtonpeas) > > > > cleanEx(); ..nameEx <- "heights" > > ### * heights > > flush(stderr()); flush(stdout()) > > ### Name: heights > ### Title: Pearson-Lee data > ### Aliases: heights > ### Keywords: datasets > > ### ** Examples > > data(pearlee1) Warning in data(pearlee1) : data set 'pearlee1' not found > > > > cleanEx(); ..nameEx <- "highway" > > ### * highway > > flush(stderr()); flush(stdout()) > > ### Name: highway > ### Title: Highway accidents > ### Aliases: highway > ### Keywords: datasets > > ### ** Examples > > data(highway) > > > > cleanEx(); ..nameEx <- "hooker" > > ### * hooker > > flush(stderr()); flush(stdout()) > > ### Name: hooker > ### Title: Hooker's data > ### Aliases: hooker > ### Keywords: datasets > > ### ** Examples > > data(hooker) > > > > cleanEx(); ..nameEx <- "htwt" > > ### * htwt > > flush(stderr()); flush(stdout()) > > ### Name: htwt > ### Title: Artificial height and weight data > ### Aliases: htwt > ### Keywords: datasets > > ### ** Examples > > data(htwt) > > > > cleanEx(); ..nameEx <- "inf.index" > > ### * inf.index > > flush(stderr()); flush(stdout()) > > ### Name: inf.index > ### Title: Influence index plot > ### Aliases: inf.index inf.index.lm > ### Keywords: hplot regression > > ### ** Examples > > data(rat) > names(rat) [1] "BodyWt" "LiverWt" "Dose" "y" > m1 <- lm(y~BodyWt+LiverWt+Dose,data=rat) > inf.index(m1) > > > > cleanEx(); ..nameEx <- "inv.res.plot" > > ### * inv.res.plot > > flush(stderr()); flush(stdout()) > > ### Name: inv.res.plot > ### Title: Inverse response plots to transform the response > ### Aliases: inv.res.plot inverse.response.plot > ### inverse.response.plot.default > ### Keywords: hplot regression > > ### ** Examples > > data(highway) > highway$Sigs1 <- (round(highway$Sigs*highway$Len)+1)/highway$Len > attach(highway) > d <- data.frame(Rate=Rate,logLen=logb(Len,2), + logADT=logb(ADT,2),logTrks=logb(Trks,2), + Slim=Slim,Shld=Shld,logSigs1=logb(Sigs1,2)) > attach(d) > m2 <- lm(Rate~logLen+logADT+logTrks+Slim+Shld+logSigs1,d) > inv.res.plot(m2,key=c(6,2)) lambda RSS 1 0.1859631 30.63893 2 0.0000000 30.74512 3 1.0000000 32.46638 > > > > cleanEx(); ..nameEx <- "inv.tran.plot" > > ### * inv.tran.plot > > flush(stderr()); flush(stdout()) > > ### Name: inv.tran.plot > ### Title: Choose a predictor transformation visually or numerically > ### Aliases: inv.tran.plot inv.tran.estimate > ### Keywords: hplot regression > > ### ** Examples > > data(baeskel) > attach(baeskel) > inv.tran.plot(Sulfur,Tension,key=c(.6,450)) lambda RSS 1 0.03441688 2484.107 2 -1.00000000 35691.735 3 0.00000000 2535.896 4 1.00000000 35824.332 > ans <-inv.tran.estimate(Sulfur,Tension) > # redraw the plot, including the nls estimate > inv.tran.plot(Sulfur,Tension,lambda=c(ans$lambda,-1,0,1),key=c(.6,450)) lambda RSS 1 0.03441688 2484.107 2 0.03441688 2484.107 3 -1.00000000 35691.735 4 0.00000000 2535.896 5 1.00000000 35824.332 > > > > cleanEx(); ..nameEx <- "jevons" > > ### * jevons > > flush(stderr()); flush(stdout()) > > ### Name: jevons > ### Title: Jevon's gold coin data > ### Aliases: jevons > ### Keywords: datasets > > ### ** Examples > > data(jevons) > > > > cleanEx(); ..nameEx <- "lakemary" > > ### * lakemary > > flush(stderr()); flush(stdout()) > > ### Name: lakemary > ### Title: Lake Mary bluegills > ### Aliases: lakemary > ### Keywords: datasets > > ### ** Examples > > data(lakemary) > > > > cleanEx(); ..nameEx <- "lakes" > > ### * lakes > > flush(stderr()); flush(stdout()) > > ### Name: lakes > ### Title: Lake zooplankton diversity > ### Aliases: lakes > ### Keywords: datasets > > ### ** Examples > > data(lakes) > > > > cleanEx(); ..nameEx <- "landrent" > > ### * landrent > > flush(stderr()); flush(stdout()) > > ### Name: landrent > ### Title: Land rent > ### Aliases: landrent > ### Keywords: datasets > > ### ** Examples > > data(landrent) > > > > cleanEx(); ..nameEx <- "lathe1" > > ### * lathe1 > > flush(stderr()); flush(stdout()) > > ### Name: lathe1 > ### Title: Lathe data > ### Aliases: lathe1 > ### Keywords: datasets > > ### ** Examples > > data(lathe1) > > > > cleanEx(); ..nameEx <- "longley" > > ### * longley > > flush(stderr()); flush(stdout()) > > ### Name: longley > ### Title: Longley data > ### Aliases: longley > ### Keywords: datasets > > ### ** Examples > > data(longley) > > > > cleanEx(); ..nameEx <- "mantel" > > ### * mantel > > flush(stderr()); flush(stdout()) > > ### Name: mantel > ### Title: Mantel's artifical data for stepwise regression > ### Aliases: mantel > ### Keywords: datasets > > ### ** Examples > > data(mantel) > > > > cleanEx(); ..nameEx <- "mile" > > ### * mile > > flush(stderr()); flush(stdout()) > > ### Name: mile > ### Title: World records for the mile run > ### Aliases: mile > ### Keywords: datasets > > ### ** Examples > > data(mile) > > > > cleanEx(); ..nameEx <- "mmps" > > ### * mmps > > flush(stderr()); flush(stdout()) > > ### Name: mmps > ### Title: Marginal model plot > ### Aliases: mmps mmp mmp.lm mmp.glm marginal.model.plot > ### Keywords: hplot regression > > ### ** Examples > > data(ufcwc) > c1 <- lm(Height~Dbh,ufcwc) > mmp(c1,ufcwc$Dbh,label="Diameter, Dbh",color=c("black","black")) > mmps(c1) > > > > > cleanEx(); ..nameEx <- "npdata" > > ### * npdata > > flush(stderr()); flush(stdout()) > > ### Name: npdata > ### Title: Northern pike catch per unit effort > ### Aliases: npdata > ### Keywords: datasets > > ### ** Examples > > data(npdata) > > > > cleanEx(); ..nameEx <- "oldfaith" > > ### * oldfaith > > flush(stderr()); flush(stdout()) > > ### Name: oldfaith > ### Title: Old Faithful Geyser data > ### Aliases: oldfaith > ### Keywords: datasets > > ### ** Examples > > data(oldfaith) > > > > cleanEx(); ..nameEx <- "outlier.t.test" > > ### * outlier.t.test > > flush(stderr()); flush(stdout()) > > ### Name: outlier.t.test > ### Title: Bonferroni test for outliers in linear models > ### Aliases: outlier.t.test outlier.t.test.default > ### Keywords: regression > > ### ** Examples > > data(cloud) > m1 <- lm(logb(Rain,2)~S+logb(C,2)+logb(P,2)+E+A,data=cloud,subset=-2) > outlier.t.test(m1) tvalue Bonf.pvals 7 -5.011069 0.002943525 > > > > cleanEx(); ..nameEx <- "physics" > > ### * physics > > flush(stderr()); flush(stdout()) > > ### Name: physics > ### Title: Physics data > ### Aliases: physics physics1 > ### Keywords: datasets > > ### ** Examples > > data(physics1) > > > > cleanEx(); ..nameEx <- "pipeline" > > ### * pipeline > > flush(stderr()); flush(stdout()) > > ### Name: pipeline > ### Title: Alaska pipeline > ### Aliases: pipeline > ### Keywords: datasets > > ### ** Examples > > data(pipeline) > > > > cleanEx(); ..nameEx <- "plot.bctrans" > > ### * plot.bctrans > > flush(stderr()); flush(stdout()) > > ### Name: plot.bctrans > ### Title: Plot and summary helpers for bctrans objects > ### Aliases: plot.bctrans summary.bctrans coef.bctrans vcov.bctrans > ### Keywords: models regression > > ### ** Examples > > data(highway) > b <- highway[,c(8,1,2,10,5)] # select interesting columns > summary(ans <- bctrans(b,family="yeo.johnson")) # zeros ==> use yeo.johnson yeo.johnson Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) ADT -0.0258 0.1482 -0.1741 -6.9209 Trks -1.0608 0.7236 -1.4660 -2.8480 Shld 1.5523 0.4476 3.4683 1.2340 Sigs -2.2894 0.6700 -3.4169 -4.9094 LRT df p.value LR test, all lambda equal 0 33.63116 4 8.869239e-07 LR test, all lambda equal 1 86.53335 4 0.000000e+00 > b$Sigs <- (round(b$Sigs*b$Len)+1)/b$Len # redefine so no zeroes > summary(ans <- bctrans(b)) # fit with box.cox box.cox Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) ADT 0.0503 0.1216 0.4134 -7.8120 Trks -0.7407 0.6269 -1.1815 -2.7767 Shld 1.3452 0.3625 3.7107 0.9523 Sigs -0.2153 0.1520 -1.4162 -7.9946 LRT df p.value LR test, all lambda equal 0 22.47344 4 0.0001612947 LR test, all lambda equal 1 115.88454 4 0.0000000000 > data(highway) > b <- highway[,c(8,1,2,10,5)] # select interesting columns > summary(ans <- bctrans(b,family="yeo.johnson")) # zeros ==> use yeo.johnson yeo.johnson Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) ADT -0.0258 0.1482 -0.1741 -6.9209 Trks -1.0608 0.7236 -1.4660 -2.8480 Shld 1.5523 0.4476 3.4683 1.2340 Sigs -2.2894 0.6700 -3.4169 -4.9094 LRT df p.value LR test, all lambda equal 0 33.63116 4 8.869239e-07 LR test, all lambda equal 1 86.53335 4 0.000000e+00 > b$Sigs <- (round(b$Sigs*b$Len)+1)/b$Len # redefine so no zeroes > summary(ans <- bctrans(b)) # fit with box.cox box.cox Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) ADT 0.0503 0.1216 0.4134 -7.8120 Trks -0.7407 0.6269 -1.1815 -2.7767 Shld 1.3452 0.3625 3.7107 0.9523 Sigs -0.2153 0.1520 -1.4162 -7.9946 LRT df p.value LR test, all lambda equal 0 22.47344 4 0.0001612947 LR test, all lambda equal 1 115.88454 4 0.0000000000 > lrt.bctrans(ans,lrt=list(c(0,-1,1,0))) LRT df p.value LR test, all lambda equal 0 22.473437 4 0.0001612947 LR test, all lambda equal 1 115.884543 4 0.0000000000 LR test, lambda = 0 -1 1 0 3.291497 4 0.5102805391 > plot(ans,family="power") # plot, but use ordinary powers > b <- cbind(b,powtran(ans)) # add transformed variables to data frame > > > > cleanEx(); ..nameEx <- "pod" > > ### * pod > > flush(stderr()); flush(stdout()) > > ### Name: pod > ### Title: Fit partial one-dimensional, or POD models, based on a linear > ### model > ### Aliases: pod pod.lm pod.formula print.pod summary.pod coef.pod > ### anova.pod deviance.pod resid.pod formula.pod fitted.pod podresponse > ### df.residual.pod predict.pod plot.pod plot.pod.lm podnls.fit > ### residuals.pod vcov.pod > ### Keywords: models regression hplot > > ### ** Examples > > data(ais) > m1 <- pod(LBM ~ Ht + Wt + RCC, data= ais, group= Sex) > anova(m1) # compare four models POD Analysis of Variance Table for LBM, grouped by Sex 1: LBM ~ Ht + Wt + RCC 2: LBM ~ Ht + Wt + RCC + Sex 3: LBM ~ eta0 + eta1 * Ht + eta2 * Wt + eta3 * RCC + Sex1 * (th02 + 3: th12 * (eta1 * Ht + eta2 * Wt + eta3 * RCC)) 4: LBM ~ Ht + Wt + RCC + Sex + Ht:Sex + Wt:Sex + RCC:Sex Res.Df RSS Df Sum of Sq F Pr(>F) 1: common 198 2936.71 2: parallel 197 1457.43 1 1479.28 245.6466 < 2.2e-16 *** 3: pod 196 1185.91 1 271.52 45.0876 2.030e-10 *** 4: pod + 2fi 194 1168.27 2 17.65 1.4651 0.2336 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > plot(m1) # draw the plot > m2 <- update(m1, mean.function="parallel") > plot(m2,key=c(55,95)) > > > > cleanEx(); ..nameEx <- "powtran" > > ### * powtran > > flush(stderr()); flush(stdout()) > > ### Name: powtran > ### Title: Power transformations > ### Aliases: powtran powtran.default powtran.matrix powtran.data.frame > ### powtran.bctrans > ### Keywords: regression > > ### ** Examples > > data(ufcwc) > attach(ufcwc) > powtran(Height,lambda=0) [1] 1190.643 1209.259 1159.438 1204.242 1287.900 1241.540 1294.771 1254.063 [9] 1273.491 1249.967 1271.997 1126.052 1182.962 1209.259 1265.921 1287.900 [17] 1307.908 1209.259 1307.908 1223.669 1153.148 1273.491 1193.855 1146.676 [25] 1049.482 1258.086 1078.024 1232.783 1249.967 1086.780 1265.921 1214.167 [33] 1214.167 1241.540 1016.750 1232.783 1254.063 1232.783 1249.967 1171.510 [41] 1182.962 1204.242 1273.491 1241.540 1193.855 1232.783 1258.086 1273.491 [49] 1258.086 1214.167 1241.540 1249.967 1314.198 1332.069 1228.273 1228.273 [57] 1287.900 1273.491 1262.038 1028.202 1059.407 1294.771 1153.148 1284.384 [65] 1159.438 1225.522 1287.900 1291.362 1249.967 1238.949 1277.181 1291.362 [73] 1212.217 1194.916 1182.962 1103.327 1153.148 1171.510 1298.128 1188.475 [81] 1249.967 1262.038 1249.967 1146.676 1111.162 1103.327 1218.968 1245.793 [89] 1028.202 1028.202 1103.327 1159.438 1249.967 1232.783 1086.780 1214.167 [97] 1337.722 1298.128 1311.075 1188.475 1273.491 1269.738 1146.676 1262.038 [105] 1214.167 1296.791 1134.531 1249.967 1264.376 1004.678 1311.075 1175.007 [113] 1188.475 1167.956 1059.407 1083.319 1078.024 1146.676 1188.475 1277.181 [121] 1228.273 1258.086 1218.968 1228.273 1258.086 1171.510 1232.783 1140.011 [129] 1146.676 1126.052 1182.962 1068.909 1068.909 1254.063 1188.475 1103.327 [137] 1262.038 1232.783 1188.475 > inv.tran.plot(Dbh,Height, lam = c(-1, 0, 1), family="box.cox") lambda RSS 1 0.04787804 152122.8 2 -1.00000000 197352.2 3 0.00000000 152232.3 4 1.00000000 193739.7 > summary(ans <- bctrans1(cbind(Dbh,Height))) # bctrans1 is used for a matrix input box.cox Transformations to Multinormality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) Dbh 0.1226 0.1219 1.0060 -7.1972 Height 0.7259 0.2009 3.6139 -1.3648 LRT df p.value LR test, all lambda equal 0 14.85000 2 5.961596e-04 LR test, all lambda equal 1 50.78665 2 9.371726e-12 > plot(ans) > > > > cleanEx(); ..nameEx <- "prodscore" > > ### * prodscore > > flush(stderr()); flush(stdout()) > > ### Name: prodscore > ### Title: Soil productivity > ### Aliases: prodscore > ### Keywords: datasets > > ### ** Examples > > data(prodscore) > > > > cleanEx(); ..nameEx <- "pure.error.anova" > > ### * pure.error.anova > > flush(stderr()); flush(stdout()) > > ### Name: pure.error.anova > ### Title: Pure Error analysis of variance > ### Aliases: pure.error.anova > ### Keywords: models regression > > ### ** Examples > > x <- c(1,1,1,2,3,3,4,4,4,4) > y <- c(2.55,2.75,2.57,2.40,4.19,4.70,3.81,4.87,2.93,4.52) > m1 <- lm(y~x) > anova(m1) # ignore pure error Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) x 1 4.5693 4.5693 8.669 0.01859 * Residuals 8 4.2166 0.5271 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > pure.error.anova(m1) # include pure error Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) x 1 4.5693 4.5693 11.6247 0.01433 * Lack.of.Fit 2 1.8582 0.9291 2.3638 0.17496 Residuals 6 2.3584 0.3931 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > data(forbes) > m2 <- lm(Lpres~Temp, data=forbes) > pure.error.anova(m2) # function does nothing because there is no pure error Analysis of Variance Table Response: Lpres Df Sum Sq Mean Sq F value Pr(>F) Temp 1 425.64 425.64 2962.8 < 2.2e-16 *** Residuals 15 2.15 0.14 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > > > > cleanEx(); ..nameEx <- "random.lin.comb" > > ### * random.lin.comb > > flush(stderr()); flush(stdout()) > > ### Name: random.lin.comb > ### Title: Compute a random linear combination of the columns of a matrix > ### or data frame > ### Aliases: random.lin.comb random.lin.comb.default random.lin.comb.lm > ### Keywords: manip > > ### ** Examples > > random.lin.comb(matrix(rnorm(9),ncol=3)) [,1] [1,] 2.8502051 [2,] -2.1277454 [3,] -0.7224597 > > > > cleanEx(); ..nameEx <- "rat" > > ### * rat > > flush(stderr()); flush(stdout()) > > ### Name: rat > ### Title: Rat data > ### Aliases: rat > ### Keywords: datasets > > ### ** Examples > > data(rat) > pairs(rat) > summary(m1 <- lm(y~BodyWt+LiverWt+Dose, data=rat)) Call: lm(formula = y ~ BodyWt + LiverWt + Dose, data = rat) Residuals: Min 1Q Median 3Q Max -0.100557 -0.063233 0.007131 0.045971 0.134691 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.265922 0.194585 1.367 0.1919 BodyWt -0.021246 0.007974 -2.664 0.0177 * LiverWt 0.014298 0.017217 0.830 0.4193 Dose 4.178111 1.522625 2.744 0.0151 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.07729 on 15 degrees of freedom Multiple R-Squared: 0.3639, Adjusted R-squared: 0.2367 F-statistic: 2.86 on 3 and 15 DF, p-value: 0.07197 > inf.index(m1) > > > > cleanEx(); ..nameEx <- "residual.plots" > > ### * residual.plots > > flush(stderr()); flush(stdout()) > > ### Name: residual.plots > ### Title: Residual plots and curvature tests for linear model fits > ### Aliases: residual.plots residual.plots.lm resplot resid.curv.test > ### tukey.nonadd.test > ### Keywords: hplot regression > > ### ** Examples > > data(highway) > highway$Sigs <- (round(highway$Sigs*highway$Len)+1)/highway$Len > attach(highway) > d <- data.frame(Rate=Rate,logLen=logb(Len,2), + logADT=logb(ADT,2),logTrks=logb(Trks,2), + Slim=Slim,Shld=Shld,logSigs1=logb(Sigs,2)) > attach(d) > m2 <- lm(Rate~logLen+logADT+logTrks+Slim+Shld+logSigs1,d) > residual.plots(m2) Test stat Pr(>|t|) logLen 1.089586 0.28429312 logADT 1.243364 0.22305499 logTrks 1.355295 0.18511697 Slim 1.841148 0.07518794 Shld 0.284605 0.77783942 logSigs1 1.550059 0.13127737 Tukey test 2.171441 0.02989786 > > > > cleanEx(); ..nameEx <- "salary" > > ### * salary > > flush(stderr()); flush(stdout()) > > ### Name: salary > ### Title: Salary data > ### Aliases: salary > ### Keywords: datasets > > ### ** Examples > > data(salary) > > > > cleanEx(); ..nameEx <- "salarygov" > > ### * salarygov > > flush(stderr()); flush(stdout()) > > ### Name: salarygov > ### Title: Government salary study > ### Aliases: salarygov > ### Keywords: datasets > > ### ** Examples > > data(salarygov) > > > > cleanEx(); ..nameEx <- "segreg" > > ### * segreg > > flush(stderr()); flush(stdout()) > > ### Name: segreg > ### Title: Energy consumption > ### Aliases: segreg > ### Keywords: datasets > > ### ** Examples > > data(segreg) > > > > cleanEx(); ..nameEx <- "shocks" > > ### * shocks > > flush(stderr()); flush(stdout()) > > ### Name: shocks > ### Title: Small electric shocks in dairy cows > ### Aliases: shocks > ### Keywords: datasets > > ### ** Examples > > data(shocks) > > > > cleanEx(); ..nameEx <- "sigma.hat" > > ### * sigma.hat > > flush(stderr()); flush(stdout()) > > ### Name: sigma.hat > ### Title: Return the scale estimate for a regression model > ### Aliases: sigma.hat sigma.hat.default sigma.hat.glm > ### Keywords: regression > > ### ** Examples > > data(forbes) > m1 <- lm(Lpres ~ Temp, data=forbes) > sigma.hat(m1) [1] 0.3790275 > > > > cleanEx(); ..nameEx <- "sleep1" > > ### * sleep1 > > flush(stderr()); flush(stdout()) > > ### Name: sleep1 > ### Title: Sleep in mammals > ### Aliases: sleep1 > ### Keywords: datasets > > ### ** Examples > > data(sleep1) > > > > cleanEx(); ..nameEx <- "snake" > > ### * snake > > flush(stderr()); flush(stdout()) > > ### Name: snake > ### Title: Snake river data > ### Aliases: snake > ### Keywords: datasets > > ### ** Examples > > data(snake) > > > > cleanEx(); ..nameEx <- "sniffer" > > ### * sniffer > > flush(stderr()); flush(stdout()) > > ### Name: sniffer > ### Title: Sniffer data > ### Aliases: sniffer > ### Keywords: datasets > > ### ** Examples > > data(sniffer) > > > > cleanEx(); ..nameEx <- "snowgeese" > > ### * snowgeese > > flush(stderr()); flush(stdout()) > > ### Name: snowgeese > ### Title: Snow geese > ### Aliases: snowgeese > ### Keywords: datasets > > ### ** Examples > > data(snowgeese) > > > > cleanEx(); ..nameEx <- "stopping" > > ### * stopping > > flush(stderr()); flush(stdout()) > > ### Name: stopping > ### Title: Stopping distances > ### Aliases: stopping > ### Keywords: datasets > > ### ** Examples > > data(stopping) > > > > cleanEx(); ..nameEx <- "swan96" > > ### * swan96 > > flush(stderr()); flush(stdout()) > > ### Name: swan96 > ### Title: Black crappie study on Swan Lake, Minnesota > ### Aliases: swan96 > ### Keywords: datasets > > ### ** Examples > > data(swan96) > > > > cleanEx(); ..nameEx <- "titanic" > > ### * titanic > > flush(stderr()); flush(stdout()) > > ### Name: titanic > ### Title: Titanic > ### Aliases: titanic > ### Keywords: datasets > > ### ** Examples > > data(titanic) > > > > cleanEx(); ..nameEx <- "tran.family" > > ### * tran.family > > flush(stderr()); flush(stdout()) > > ### Name: tran.family.box.cox > ### Title: Compute transformation families > ### Aliases: tran.family.box.cox tran.family tran.family.default > ### tran.family.boxcox tran.family.yeo.johnson tran.family.YJ > ### tran.family.power tran.family.basic > ### Keywords: internal > > ### ** Examples > > data(ufcwc) > attach(ufcwc) > powtran(Height,lambda=0) [1] 1190.643 1209.259 1159.438 1204.242 1287.900 1241.540 1294.771 1254.063 [9] 1273.491 1249.967 1271.997 1126.052 1182.962 1209.259 1265.921 1287.900 [17] 1307.908 1209.259 1307.908 1223.669 1153.148 1273.491 1193.855 1146.676 [25] 1049.482 1258.086 1078.024 1232.783 1249.967 1086.780 1265.921 1214.167 [33] 1214.167 1241.540 1016.750 1232.783 1254.063 1232.783 1249.967 1171.510 [41] 1182.962 1204.242 1273.491 1241.540 1193.855 1232.783 1258.086 1273.491 [49] 1258.086 1214.167 1241.540 1249.967 1314.198 1332.069 1228.273 1228.273 [57] 1287.900 1273.491 1262.038 1028.202 1059.407 1294.771 1153.148 1284.384 [65] 1159.438 1225.522 1287.900 1291.362 1249.967 1238.949 1277.181 1291.362 [73] 1212.217 1194.916 1182.962 1103.327 1153.148 1171.510 1298.128 1188.475 [81] 1249.967 1262.038 1249.967 1146.676 1111.162 1103.327 1218.968 1245.793 [89] 1028.202 1028.202 1103.327 1159.438 1249.967 1232.783 1086.780 1214.167 [97] 1337.722 1298.128 1311.075 1188.475 1273.491 1269.738 1146.676 1262.038 [105] 1214.167 1296.791 1134.531 1249.967 1264.376 1004.678 1311.075 1175.007 [113] 1188.475 1167.956 1059.407 1083.319 1078.024 1146.676 1188.475 1277.181 [121] 1228.273 1258.086 1218.968 1228.273 1258.086 1171.510 1232.783 1140.011 [129] 1146.676 1126.052 1182.962 1068.909 1068.909 1254.063 1188.475 1103.327 [137] 1262.038 1232.783 1188.475 > > > > cleanEx(); ..nameEx <- "transact" > > ### * transact > > flush(stderr()); flush(stdout()) > > ### Name: transact > ### Title: Transaction data > ### Aliases: transact > ### Keywords: datasets > > ### ** Examples > > data(transact) > > > > cleanEx(); ..nameEx <- "turk0" > > ### * turk0 > > flush(stderr()); flush(stdout()) > > ### Name: turk0 > ### Title: Turkey data, one source > ### Aliases: turk0 > ### Keywords: datasets > > ### ** Examples > > data(turk0) > > > > cleanEx(); ..nameEx <- "turkey" > > ### * turkey > > flush(stderr()); flush(stdout()) > > ### Name: turkey > ### Title: Turkey data, all sources > ### Aliases: turkey > ### Keywords: datasets > > ### ** Examples > > data(turkey) > > > > cleanEx(); ..nameEx <- "twins" > > ### * twins > > flush(stderr()); flush(stdout()) > > ### Name: twins > ### Title: Burt's twin data > ### Aliases: twins > ### Keywords: datasets > > ### ** Examples > > data(twins) > > > > cleanEx(); ..nameEx <- "ufc" > > ### * ufc > > flush(stderr()); flush(stdout()) > > ### Name: ufc > ### Title: Height-diameter data for Upper Flat Creek, Idaho > ### Aliases: ufc ufcgf ufcwc > ### Keywords: datasets > > ### ** Examples > > data(ufcgf) > > > > cleanEx(); ..nameEx <- "walleye" > > ### * walleye > > flush(stderr()); flush(stdout()) > > ### Name: walleye > ### Title: Walleye length at age > ### Aliases: walleye > ### Keywords: datasets > > ### ** Examples > > data(walleye) > > > > cleanEx(); ..nameEx <- "water" > > ### * water > > flush(stderr()); flush(stdout()) > > ### Name: water > ### Title: California water > ### Aliases: water > ### Keywords: datasets > > ### ** Examples > > data(dwp) Warning in data(dwp) : data set 'dwp' not found > > > > cleanEx(); ..nameEx <- "wblake" > > ### * wblake > > flush(stderr()); flush(stdout()) > > ### Name: wblake > ### Title: West Bearskin Lake walleye data. > ### Aliases: wblake wblake2 > ### Keywords: datasets > > ### ** Examples > > data(wblake) # excludes fish age 9 or older > data(wblake2) # all fish > > > > cleanEx(); ..nameEx <- "wm1" > > ### * wm1 > > flush(stderr()); flush(stdout()) > > ### Name: wm1 > ### Title: Simple windmill data > ### Aliases: wm1 > ### Keywords: datasets > > ### ** Examples > > data(wm1) > > > > cleanEx(); ..nameEx <- "wm2" > > ### * wm2 > > flush(stderr()); flush(stdout()) > > ### Name: wm2 > ### Title: Windmill data with direction information > ### Aliases: wm2 > ### Keywords: datasets > > ### ** Examples > > data(wm2) > > > > cleanEx(); ..nameEx <- "wm3" > > ### * wm3 > > flush(stderr()); flush(stdout()) > > ### Name: wm3 > ### Title: Binned wind speed data > ### Aliases: wm3 > ### Keywords: datasets > > ### ** Examples > > data(wm3) > > > > cleanEx(); ..nameEx <- "wm4" > > ### * wm4 > > flush(stderr()); flush(stdout()) > > ### Name: wm4 > ### Title: Windmill data with direction information and four reference > ### sites > ### Aliases: wm4 > ### Keywords: datasets > > ### ** Examples > > data(wm4) > > > > cleanEx(); ..nameEx <- "wool" > > ### * wool > > flush(stderr()); flush(stdout()) > > ### Name: wool > ### Title: Wool data > ### Aliases: wool > ### Keywords: datasets > > ### ** Examples > > data(wool) > > > > ### *