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R : Copyright 2001, The R Development Core TeamVersion 1.3.0 Under development (unstable) (2001-06-11)R is free software and comes with ABSOLUTELY NO WARRANTY.You are welcome to redistribute it under certain conditions.Type `license()' or `licence()' for distribution details.R is a collaborative project with many contributors.Type `contributors()' for more information.Type `demo()' for some demos, `help()' for on-line help, or`help.start()' for a HTML browser interface to help.Type `q()' to quit R.> ### Regression tests for which the printed output is the issue>> ## PR 715 (Printing list elements w/attributes)> ##> l <- list(a=10)> attr(l$a, "xx") <- 23> l$a[1] 10attr(,"xx")[1] 23> ## Comments:> ## should print as> # $a:> # [1] 10> # attr($a, "xx"):> # [1] 23>> ## On the other hand> m <- matrix(c(1, 2, 3, 0, 10, NA), 3, 2)> na.omit(m)[,1] [,2][1,] 1 0[2,] 2 10attr(,"na.action")[1] 3attr(,"na.action")attr(,"class")[1] "omit"> ## should print as> # [,1] [,2]> # [1,] 1 0> # [2,] 2 10> # attr(,"na.action")> # [1] 3> # attr(,"na.action")attr(,"class")> # [1] "omit">> ## and> x <- 1> attr(x, "foo") <- list(a="a")> x[1] 1attr(,"foo")attr(,"foo")$a[1] "a"> ## should print as> # [1] 1> # attr(,"foo")> # attr(,"foo")$a> # [1] "a">>> ## PR 746 (printing of lists)> ##> test.list <- list(A = list(formula=Y~X, subset=TRUE),+ B = list(formula=Y~X, subset=TRUE))>> test.list$A$A$formulaY ~ X$A$subset[1] TRUE$B$B$formulaY ~ X$B$subset[1] TRUE> ## Comments:> ## should print as> # $A> # $A$formula> # Y ~ X> #> # $A$subset> # [1] TRUE> #> #> # $B> # $B$formula> # Y ~ X> #> # $B$subset> # [1] TRUE>> ## Marc Feldesman 2001-Feb-01. Precision in summary.data.frame & *.matrix> data(attenu)> summary(attenu)event mag station distMin. : 1.00 Min. :5.000 117 : 5 Min. : 0.501st Qu.: 9.00 1st Qu.:5.300 113 : 4 1st Qu.: 11.32Median :18.00 Median :6.100 1028 : 4 Median : 23.40Mean :14.74 Mean :6.084 475 : 3 Mean : 45.603rd Qu.:20.00 3rd Qu.:6.600 135 : 3 3rd Qu.: 47.55Max. :23.00 Max. :7.700 (Other):147 Max. :370.00NA's : 16accelMin. :0.003001st Qu.:0.04425Median :0.11300Mean :0.154223rd Qu.:0.21925Max. :0.81000> summary(attenu, digits = 5)event mag station distMin. : 1.000 Min. :5.0000 117 : 5 Min. : 0.5001st Qu.: 9.000 1st Qu.:5.3000 113 : 4 1st Qu.: 11.325Median :18.000 Median :6.1000 1028 : 4 Median : 23.400Mean :14.742 Mean :6.0841 475 : 3 Mean : 45.6033rd Qu.:20.000 3rd Qu.:6.6000 135 : 3 3rd Qu.: 47.550Max. :23.000 Max. :7.7000 (Other):147 Max. :370.000NA's : 16accelMin. :0.003001st Qu.:0.04425Median :0.11300Mean :0.154223rd Qu.:0.21925Max. :0.81000> summary(data.matrix(attenu), digits = 5)# the same for matrixevent mag station distMin. : 1.000 Min. :5.0000 Min. : 1.000 Min. : 0.5001st Qu.: 9.000 1st Qu.:5.3000 1st Qu.: 24.250 1st Qu.: 11.325Median :18.000 Median :6.1000 Median : 56.500 Median : 23.400Mean :14.742 Mean :6.0841 Mean : 56.928 Mean : 45.6033rd Qu.:20.000 3rd Qu.:6.6000 3rd Qu.: 86.750 3rd Qu.: 47.550Max. :23.000 Max. :7.7000 Max. :117.000 Max. :370.000NA's : 16.000accelMin. :0.003001st Qu.:0.04425Median :0.11300Mean :0.154223rd Qu.:0.21925Max. :0.81000> ## Comments:> ## No difference between these in 1.2.1 and earlier> set.seed(1)> x <- c(round(runif(10), 2), 10000)> summary(x)Min. 1st Qu. Median Mean 3rd Qu. Max.0.000 0.050 0.550 909.400 0.675 10000.000> summary(data.frame(x))xMin. : 0.0001st Qu.: 0.050Median : 0.550Mean : 909.4233rd Qu.: 0.675Max. :10000.000> ## Comments:> ## All entries show all 3 digits after the decimal point now.>> ## Chong Gu 2001-Feb-16. step on binomials> "detg1" <-+ structure(list(Temp = structure(c(2, 1, 2, 1, 2, 1, 2, 1, 2,+ 1, 2, 1), .Label = c("High", "Low"), class = "factor"), M.user = structure(c(1,+ 1, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2), .Label = c("N", "Y"), class = "factor"),+ Soft = structure(c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3), .Label = c("Hard",+ "Medium", "Soft"), class = "factor"), M = c(42, 30, 52, 43,+ 50, 23, 55, 47, 53, 27, 49, 29), X = c(68, 42, 37, 24, 66,+ 33, 47, 23, 63, 29, 57, 19)), .Names = c("Temp", "M.user",+ "Soft", "M", "X"), class = "data.frame", row.names = c("1", "3",+ "5", "7", "9", "11", "13", "15", "17", "19", "21", "23"))> detg1.m0 <- glm(cbind(X,M)~1,binomial,detg1)> detg1.m0Call: glm(formula = cbind(X, M) ~ 1, family = binomial, data = detg1)Coefficients:(Intercept)0.01587Degrees of Freedom: 11 Total (i.e. Null); 11 ResidualNull Deviance: 32.83Residual Deviance: 32.83 AIC: 92.52> step(detg1.m0,scope=list(upper=~M.user*Temp*Soft))Start: AIC= 92.52cbind(X, M) ~ 1Df Deviance AIC+ M.user 1 12.244 73.942+ Temp 1 28.464 90.162<none> 32.826 92.524+ Soft 2 32.430 96.128Step: AIC= 73.94cbind(X, M) ~ M.userDf Deviance AIC+ Temp 1 8.444 72.142<none> 12.244 73.942+ Soft 2 11.967 77.665- M.user 1 32.826 92.524Step: AIC= 72.14cbind(X, M) ~ M.user + TempDf Deviance AIC+ M.user:Temp 1 5.656 71.354<none> 8.444 72.142- Temp 1 12.244 73.942+ Soft 2 8.228 75.926- M.user 1 28.464 90.162Step: AIC= 71.35cbind(X, M) ~ M.user + Temp + M.user:TempDf Deviance AIC<none> 5.656 71.354- M.user:Temp 1 8.444 72.142+ Soft 2 5.495 75.193Call: glm(formula = cbind(X, M) ~ M.user + Temp + M.user:Temp, family = binomial, data = detg1)Coefficients:(Intercept) M.userY TempLow M.userY:TempLow0.26236 -0.85183 0.04411 0.44427Degrees of Freedom: 11 Total (i.e. Null); 8 ResidualNull Deviance: 32.83Residual Deviance: 5.656 AIC: 71.35>> ## PR 829 (empty values in all.vars)> ## This example by Uwe Ligges <ligges@statistik.uni-dortmund.de>>> temp <- matrix(1:4, 2)> all.vars(temp ~ 3) # OK[1] "temp"> all.vars(temp[1, ] ~ 3) # wrong in 1.2.1[1] "temp">> ## 2001-Feb-22 from David Scott.> ## rank-deficient residuals in a manova model.> gofX.df<-+ structure(list(A = c(0.696706709347165, 0.362357754476673,+ -0.0291995223012888,+ 0.696706709347165, 0.696706709347165, -0.0291995223012888, 0.696706709347165,+ -0.0291995223012888, 0.362357754476673, 0.696706709347165, -0.0291995223012888,+ 0.362357754476673, -0.416146836547142, 0.362357754476673, 0.696706709347165,+ 0.696706709347165, 0.362357754476673, -0.416146836547142, -0.0291995223012888,+ -0.416146836547142, 0.696706709347165, -0.416146836547142, 0.362357754476673,+ -0.0291995223012888), B = c(0.717356090899523, 0.932039085967226,+ 0.999573603041505, 0.717356090899523, 0.717356090899523, 0.999573603041505,+ 0.717356090899523, 0.999573603041505, 0.932039085967226, 0.717356090899523,+ 0.999573603041505, 0.932039085967226, 0.909297426825682, 0.932039085967226,+ 0.717356090899523, 0.717356090899523, 0.932039085967226, 0.909297426825682,+ 0.999573603041505, 0.909297426825682, 0.717356090899523, 0.909297426825682,+ 0.932039085967226, 0.999573603041505), C = c(-0.0291995223012888,+ -0.737393715541246, -0.998294775794753, -0.0291995223012888,+ -0.0291995223012888, -0.998294775794753, -0.0291995223012888,+ -0.998294775794753, -0.737393715541246, -0.0291995223012888,+ -0.998294775794753, -0.737393715541246, -0.653643620863612, -0.737393715541246,+ -0.0291995223012888, -0.0291995223012888, -0.737393715541246,+ -0.653643620863612, -0.998294775794753, -0.653643620863612,+ -0.0291995223012888,+ -0.653643620863612, -0.737393715541246, -0.998294775794753),+ D = c(0.999573603041505, 0.67546318055115, -0.0583741434275801,+ 0.999573603041505, 0.999573603041505, -0.0583741434275801,+ 0.999573603041505, -0.0583741434275801, 0.67546318055115,+ 0.999573603041505, -0.0583741434275801, 0.67546318055115,+ -0.756802495307928, 0.67546318055115, 0.999573603041505,+ 0.999573603041505, 0.67546318055115, -0.756802495307928,+ -0.0583741434275801, -0.756802495307928, 0.999573603041505,+ -0.756802495307928, 0.67546318055115, -0.0583741434275801+ ), groups = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2,+ 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3), class = "factor", .Label = c("1",+ "2", "3"))), .Names = c("A", "B", "C", "D", "groups"), row.names = c("1",+ "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",+ "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24"+ ), class = "data.frame")>> gofX.manova <- manova(formula = cbind(A, B, C, D) ~ groups, data = gofX.df)> try(summary(gofX.manova))Error in summary.manova(gofX.manova) : residuals have rank 3 < 4> ## should fail with an error message `residuals have rank 3 < 4'>> ## Prior to 1.3.0 dist did not handle missing values, and the> ## internal C code was incorrectly scaling for missing values.> library(mva)> data(trees)> z <- as.matrix(t(trees))> z[1,1] <- z[2,2] <- z[3,3] <- z[2,4] <- NA> dist(z, method="euclidean")Girth HeightHeight 352.4365Volume 123.5503 261.5802> dist(z, method="maximum")Girth HeightHeight 72.7Volume 56.4 63.3> dist(z, method="manhattan")Girth HeightHeight 1954.8821Volume 557.1448 1392.343> dist(z, method="canberra")Girth HeightHeight 21.66477Volume 10.96200 13.63365> detach("package:mva")>> ## F. Tusell 2001-03-07. printing kernels.> library(ts)> kernel("daniell", m=5)Daniell(5)coef[-5] = 0.09091coef[-4] = 0.09091coef[-3] = 0.09091coef[-2] = 0.09091coef[-1] = 0.09091coef[ 0] = 0.09091coef[ 1] = 0.09091coef[ 2] = 0.09091coef[ 3] = 0.09091coef[ 4] = 0.09091coef[ 5] = 0.09091> kernel("modified.daniell", m=5)mDaniell(5)coef[-5] = 0.05coef[-4] = 0.10coef[-3] = 0.10coef[-2] = 0.10coef[-1] = 0.10coef[ 0] = 0.10coef[ 1] = 0.10coef[ 2] = 0.10coef[ 3] = 0.10coef[ 4] = 0.10coef[ 5] = 0.05> kernel("daniell", m=c(3,5,7))unknowncoef[-15] = 0.0008658coef[-14] = 0.0025974coef[-13] = 0.0051948coef[-12] = 0.0086580coef[-11] = 0.0129870coef[-10] = 0.0181818coef[ -9] = 0.0242424coef[ -8] = 0.0303030coef[ -7] = 0.0363636coef[ -6] = 0.0424242coef[ -5] = 0.0484848coef[ -4] = 0.0536797coef[ -3] = 0.0580087coef[ -2] = 0.0614719coef[ -1] = 0.0640693coef[ 0] = 0.0649351coef[ 1] = 0.0640693coef[ 2] = 0.0614719coef[ 3] = 0.0580087coef[ 4] = 0.0536797coef[ 5] = 0.0484848coef[ 6] = 0.0424242coef[ 7] = 0.0363636coef[ 8] = 0.0303030coef[ 9] = 0.0242424coef[ 10] = 0.0181818coef[ 11] = 0.0129870coef[ 12] = 0.0086580coef[ 13] = 0.0051948coef[ 14] = 0.0025974coef[ 15] = 0.0008658> ## fixed by patch from Adrian Trapletti 2001-03-08>> ## Start new year (i.e. line) at Jan:> (tt <- ts(1:10, start = c(1920,7), end = c(1921,4), freq = 12))Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec1920 1 2 3 4 5 61921 7 8 9 10> cbind(tt, tt + 1)tt tt + 1Jul 1920 1 2Aug 1920 2 3Sep 1920 3 4Oct 1920 4 5Nov 1920 5 6Dec 1920 6 7Jan 1921 7 8Feb 1921 8 9Mar 1921 9 10Apr 1921 10 11>>> ## PR 883 (cor(x,y) when is.null(y))> try(cov(rnorm(10), NULL))Error in cov(rnorm(10), NULL) : supply both x and y or a matrix-like x> try(cor(rnorm(10), NULL))Error in cor(rnorm(10), NULL) : supply both x and y or a matrix-like x> ## gave the variance and 1 respectively in 1.2.2.> try(var(NULL))Error in var(NULL) : `x' is empty> try(var(numeric(0)))Error in var(numeric(0)) : `x' is empty> ## gave NA in 1.2.2>>> ## PR 960 (format() of a character matrix converts to vector)> ## example from <John.Peters@tip.csiro.au>> a <- matrix(c("axx","b","c","d","e","f","g","h"), nrow=2)> format(a)[,1] [,2] [,3] [,4][1,] "axx" "c " "e " "g "[2,] "b " "d " "f " "h "> format(a, justify="right")[,1] [,2] [,3] [,4][1,] "axx" " c" " e" " g"[2,] " b" " d" " f" " h"> ## lost dimensions in 1.2.3>> ## PR 963> svd(rbind(1:7))## $v lost dimensions in 1.2.3$d[1] 11.83216$u[,1][1,] 1$v[,1][1,] 0.08451543[2,] 0.16903085[3,] 0.25354628[4,] 0.33806170[5,] 0.42257713[6,] 0.50709255[7,] 0.59160798>>