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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] 10 attr(,"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 10 attr(,"na.action") [1] 3 attr(,"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] 1 attr(,"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 $A$formula Y ~ X $A$subset [1] TRUE $B $B$formula Y ~ 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 dist Min. : 1.00 Min. :5.000 117 : 5 Min. : 0.50 1st Qu.: 9.00 1st Qu.:5.300 113 : 4 1st Qu.: 11.32 Median :18.00 Median :6.100 1028 : 4 Median : 23.40 Mean :14.74 Mean :6.084 475 : 3 Mean : 45.60 3rd Qu.:20.00 3rd Qu.:6.600 135 : 3 3rd Qu.: 47.55 Max. :23.00 Max. :7.700 (Other):147 Max. :370.00 NA's : 16 accel Min. :0.00300 1st Qu.:0.04425 Median :0.11300 Mean :0.15422 3rd Qu.:0.21925 Max. :0.81000 > summary(attenu, digits = 5) event mag station dist Min. : 1.000 Min. :5.0000 117 : 5 Min. : 0.500 1st Qu.: 9.000 1st Qu.:5.3000 113 : 4 1st Qu.: 11.325 Median :18.000 Median :6.1000 1028 : 4 Median : 23.400 Mean :14.742 Mean :6.0841 475 : 3 Mean : 45.603 3rd Qu.:20.000 3rd Qu.:6.6000 135 : 3 3rd Qu.: 47.550 Max. :23.000 Max. :7.7000 (Other):147 Max. :370.000 NA's : 16 accel Min. :0.00300 1st Qu.:0.04425 Median :0.11300 Mean :0.15422 3rd Qu.:0.21925 Max. :0.81000 > summary(data.matrix(attenu), digits = 5)# the same for matrix event mag station dist Min. : 1.000 Min. :5.0000 Min. : 1.000 Min. : 0.500 1st Qu.: 9.000 1st Qu.:5.3000 1st Qu.: 24.250 1st Qu.: 11.325 Median :18.000 Median :6.1000 Median : 56.500 Median : 23.400 Mean :14.742 Mean :6.0841 Mean : 56.928 Mean : 45.603 3rd Qu.:20.000 3rd Qu.:6.6000 3rd Qu.: 86.750 3rd Qu.: 47.550 Max. :23.000 Max. :7.7000 Max. :117.000 Max. :370.000 NA's : 16.000 accel Min. :0.00300 1st Qu.:0.04425 Median :0.11300 Mean :0.15422 3rd Qu.:0.21925 Max. :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)) x Min. : 0.000 1st Qu.: 0.050 Median : 0.550 Mean : 909.423 3rd Qu.: 0.675 Max. :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.m0 Call: glm(formula = cbind(X, M) ~ 1, family = binomial, data = detg1) Coefficients: (Intercept) 0.01587 Degrees of Freedom: 11 Total (i.e. Null); 11 Residual Null Deviance: 32.83 Residual Deviance: 32.83 AIC: 92.52 > step(detg1.m0,scope=list(upper=~M.user*Temp*Soft)) Start: AIC= 92.52 cbind(X, M) ~ 1 Df Deviance AIC + M.user 1 12.244 73.942 + Temp 1 28.464 90.162 32.826 92.524 + Soft 2 32.430 96.128 Step: AIC= 73.94 cbind(X, M) ~ M.user Df Deviance AIC + Temp 1 8.444 72.142 12.244 73.942 + Soft 2 11.967 77.665 - M.user 1 32.826 92.524 Step: AIC= 72.14 cbind(X, M) ~ M.user + Temp Df Deviance AIC + M.user:Temp 1 5.656 71.354 8.444 72.142 - Temp 1 12.244 73.942 + Soft 2 8.228 75.926 - M.user 1 28.464 90.162 Step: AIC= 71.35 cbind(X, M) ~ M.user + Temp + M.user:Temp Df Deviance AIC 5.656 71.354 - M.user:Temp 1 8.444 72.142 + Soft 2 5.495 75.193 Call: glm(formula = cbind(X, M) ~ M.user + Temp + M.user:Temp, family = binomial, data = detg1) Coefficients: (Intercept) M.userY TempLow M.userY:TempLow 0.26236 -0.85183 0.04411 0.44427 Degrees of Freedom: 11 Total (i.e. Null); 8 Residual Null Deviance: 32.83 Residual Deviance: 5.656 AIC: 71.35 > > ## PR 829 (empty values in all.vars) > ## This example by Uwe Ligges > > 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 Height Height 352.4365 Volume 123.5503 261.5802 > dist(z, method="maximum") Girth Height Height 72.7 Volume 56.4 63.3 > dist(z, method="manhattan") Girth Height Height 1954.8821 Volume 557.1448 1392.343 > dist(z, method="canberra") Girth Height Height 21.66477 Volume 10.96200 13.63365 > detach("package:mva") > > ## F. Tusell 2001-03-07. printing kernels. > library(ts) > kernel("daniell", m=5) Daniell(5) coef[-5] = 0.09091 coef[-4] = 0.09091 coef[-3] = 0.09091 coef[-2] = 0.09091 coef[-1] = 0.09091 coef[ 0] = 0.09091 coef[ 1] = 0.09091 coef[ 2] = 0.09091 coef[ 3] = 0.09091 coef[ 4] = 0.09091 coef[ 5] = 0.09091 > kernel("modified.daniell", m=5) mDaniell(5) coef[-5] = 0.05 coef[-4] = 0.10 coef[-3] = 0.10 coef[-2] = 0.10 coef[-1] = 0.10 coef[ 0] = 0.10 coef[ 1] = 0.10 coef[ 2] = 0.10 coef[ 3] = 0.10 coef[ 4] = 0.10 coef[ 5] = 0.05 > kernel("daniell", m=c(3,5,7)) unknown coef[-15] = 0.0008658 coef[-14] = 0.0025974 coef[-13] = 0.0051948 coef[-12] = 0.0086580 coef[-11] = 0.0129870 coef[-10] = 0.0181818 coef[ -9] = 0.0242424 coef[ -8] = 0.0303030 coef[ -7] = 0.0363636 coef[ -6] = 0.0424242 coef[ -5] = 0.0484848 coef[ -4] = 0.0536797 coef[ -3] = 0.0580087 coef[ -2] = 0.0614719 coef[ -1] = 0.0640693 coef[ 0] = 0.0649351 coef[ 1] = 0.0640693 coef[ 2] = 0.0614719 coef[ 3] = 0.0580087 coef[ 4] = 0.0536797 coef[ 5] = 0.0484848 coef[ 6] = 0.0424242 coef[ 7] = 0.0363636 coef[ 8] = 0.0303030 coef[ 9] = 0.0242424 coef[ 10] = 0.0181818 coef[ 11] = 0.0129870 coef[ 12] = 0.0086580 coef[ 13] = 0.0051948 coef[ 14] = 0.0025974 coef[ 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 Dec 1920 1 2 3 4 5 6 1921 7 8 9 10 > cbind(tt, tt + 1) tt tt + 1 Jul 1920 1 2 Aug 1920 2 3 Sep 1920 3 4 Oct 1920 4 5 Nov 1920 5 6 Dec 1920 6 7 Jan 1921 7 8 Feb 1921 8 9 Mar 1921 9 10 Apr 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 > 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 > > > ## PR#1072 (Reading Inf and NaN values) > as.numeric(as.character(NaN)) [1] NaN > as.numeric(as.character(Inf)) [1] Inf > ## were NA on Windows at least under 1.3.0. >