R : Copyright 2003, The R Foundation for Statistical Computing
Version 1.9.0 Under development (unstable) (2003-11-29), ISBN 3-900051-00-3
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> ## Regression tests for which the printed output is the issue
> ### _and_ must work (no Recommended packages, please)
>
> postscript("reg-tests-2.ps")
> RNGversion("1.6.2")
Warning message:
Buggy version of Kinderman-Ramage generator used. in: RNGkind("Marsaglia-Multicarry", "Buggy Kinderman-Ramage")
>
> ### moved from various .Rd files
> ## abbreviate
> data(state)
> for(m in 1:5) {
+ cat("\n",m,":\n")
+ print(as.vector(abbreviate(state.name, minl=m)))
+ }
1 :
[1] "Alb" "Als" "Arz" "Ark" "Clf" "Clr" "Cn" "D" "F" "G"
[11] "H" "Id" "Il" "In" "Iw" "Kns" "Knt" "L" "Man" "Mr"
[21] "Mssc" "Mc" "Mnn" "Msss" "Mssr" "Mnt" "Nb" "Nv" "NH" "NJ"
[31] "NM" "NY" "NC" "ND" "Oh" "Ok" "Or" "P" "RI" "SC"
[41] "SD" "Tn" "Tx" "U" "Vrm" "Vrg" "Wsh" "WV" "Wsc" "Wy"
2 :
[1] "Alb" "Als" "Arz" "Ark" "Clf" "Clr" "Cn" "Dl" "Fl" "Gr"
[11] "Hw" "Id" "Il" "In" "Iw" "Kns" "Knt" "Ls" "Man" "Mr"
[21] "Mssc" "Mc" "Mnn" "Msss" "Mssr" "Mnt" "Nb" "Nv" "NH" "NJ"
[31] "NM" "NY" "NC" "ND" "Oh" "Ok" "Or" "Pn" "RI" "SC"
[41] "SD" "Tn" "Tx" "Ut" "Vrm" "Vrg" "Wsh" "WV" "Wsc" "Wy"
3 :
[1] "Alb" "Als" "Arz" "Ark" "Clf" "Clr" "Cnn" "Dlw" "Flr" "Grg"
[11] "Haw" "Idh" "Ill" "Ind" "Iow" "Kns" "Knt" "Lsn" "Man" "Mry"
[21] "Mssc" "Mch" "Mnn" "Msss" "Mssr" "Mnt" "Nbr" "Nvd" "NwH" "NwJ"
[31] "NwM" "NwY" "NrC" "NrD" "Ohi" "Okl" "Org" "Pnn" "RhI" "StC"
[41] "StD" "Tnn" "Txs" "Uth" "Vrm" "Vrg" "Wsh" "WsV" "Wsc" "Wym"
4 :
[1] "Albm" "Alsk" "Arzn" "Arkn" "Clfr" "Clrd" "Cnnc" "Dlwr" "Flrd" "Gerg"
[11] "Hawa" "Idah" "Illn" "Indn" "Iowa" "Knss" "Kntc" "Losn" "Main" "Mryl"
[21] "Mssc" "Mchg" "Mnns" "Msss" "Mssr" "Mntn" "Nbrs" "Nevd" "NwHm" "NwJr"
[31] "NwMx" "NwYr" "NrtC" "NrtD" "Ohio" "Oklh" "Orgn" "Pnns" "RhdI" "SthC"
[41] "SthD" "Tnns" "Texs" "Utah" "Vrmn" "Vrgn" "Wshn" "WstV" "Wscn" "Wymn"
5 :
[1] "Alabm" "Alask" "Arizn" "Arkns" "Clfrn" "Colrd" "Cnnct" "Delwr" "Flord"
[10] "Georg" "Hawai" "Idaho" "Illns" "Indin" "Iowa" "Kanss" "Kntck" "Lousn"
[19] "Maine" "Mryln" "Mssch" "Mchgn" "Mnnst" "Mssss" "Missr" "Montn" "Nbrsk"
[28] "Nevad" "NwHmp" "NwJrs" "NwMxc" "NwYrk" "NrthC" "NrthD" "Ohio" "Oklhm"
[37] "Oregn" "Pnnsy" "RhdIs" "SthCr" "SthDk" "Tnnss" "Texas" "Utah" "Vrmnt"
[46] "Virgn" "Wshng" "WstVr" "Wscns" "Wymng"
>
> ## apply
> x <- cbind(x1 = 3, x2 = c(4:1, 2:5))
> dimnames(x)[[1]] <- letters[1:8]
> apply(x, 2, summary) # 6 x n matrix
x1 x2
Min. 3 1
1st Qu. 3 2
Median 3 3
Mean 3 3
3rd Qu. 3 4
Max. 3 5
> apply(x, 1, quantile)# 5 x n matrix
a b c d e f g h
0% 3.00 3 2.00 1.0 2.00 3 3.00 3.0
25% 3.25 3 2.25 1.5 2.25 3 3.25 3.5
50% 3.50 3 2.50 2.0 2.50 3 3.50 4.0
75% 3.75 3 2.75 2.5 2.75 3 3.75 4.5
100% 4.00 3 3.00 3.0 3.00 3 4.00 5.0
>
> d.arr <- 2:5
> arr <- array(1:prod(d.arr), d.arr,
+ list(NULL,letters[1:d.arr[2]],NULL,paste("V",4+1:d.arr[4],sep="")))
> aa <- array(1:20,c(2,2,5))
> str(apply(aa[FALSE,,,drop=FALSE], 1, dim))# empty integer, `incorrect' dim.
int[, 0 ]
> stopifnot(
+ apply(arr, 1:2, sum) == t(apply(arr, 2:1, sum)),
+ aa == apply(aa,2:3,function(x) x),
+ all.equal(apply(apply(aa,2:3, sum),2,sum),
+ 10+16*0:4, tol=4*.Machine$double.eps)
+ )
> marg <- list(1:2, 2:3, c(2,4), c(1,3), 2:4, 1:3, 1:4)
> for(m in marg) print(apply(arr, print(m), sum))
[1] 1 2
a b c
[1,] 1160 1200 1240
[2,] 1180 1220 1260
[1] 2 3
[,1] [,2] [,3] [,4]
a 495 555 615 675
b 515 575 635 695
c 535 595 655 715
[1] 2 4
V5 V6 V7 V8 V9
a 84 276 468 660 852
b 100 292 484 676 868
c 116 308 500 692 884
[1] 1 3
[,1] [,2] [,3] [,4]
[1,] 765 855 945 1035
[2,] 780 870 960 1050
[1] 2 3 4
, , V5
[,1] [,2] [,3] [,4]
a 3 15 27 39
b 7 19 31 43
c 11 23 35 47
, , V6
[,1] [,2] [,3] [,4]
a 51 63 75 87
b 55 67 79 91
c 59 71 83 95
, , V7
[,1] [,2] [,3] [,4]
a 99 111 123 135
b 103 115 127 139
c 107 119 131 143
, , V8
[,1] [,2] [,3] [,4]
a 147 159 171 183
b 151 163 175 187
c 155 167 179 191
, , V9
[,1] [,2] [,3] [,4]
a 195 207 219 231
b 199 211 223 235
c 203 215 227 239
[1] 1 2 3
, , 1
a b c
[1,] 245 255 265
[2,] 250 260 270
, , 2
a b c
[1,] 275 285 295
[2,] 280 290 300
, , 3
a b c
[1,] 305 315 325
[2,] 310 320 330
, , 4
a b c
[1,] 335 345 355
[2,] 340 350 360
[1] 1 2 3 4
, , 1, V5
a b c
[1,] 1 3 5
[2,] 2 4 6
, , 2, V5
a b c
[1,] 7 9 11
[2,] 8 10 12
, , 3, V5
a b c
[1,] 13 15 17
[2,] 14 16 18
, , 4, V5
a b c
[1,] 19 21 23
[2,] 20 22 24
, , 1, V6
a b c
[1,] 25 27 29
[2,] 26 28 30
, , 2, V6
a b c
[1,] 31 33 35
[2,] 32 34 36
, , 3, V6
a b c
[1,] 37 39 41
[2,] 38 40 42
, , 4, V6
a b c
[1,] 43 45 47
[2,] 44 46 48
, , 1, V7
a b c
[1,] 49 51 53
[2,] 50 52 54
, , 2, V7
a b c
[1,] 55 57 59
[2,] 56 58 60
, , 3, V7
a b c
[1,] 61 63 65
[2,] 62 64 66
, , 4, V7
a b c
[1,] 67 69 71
[2,] 68 70 72
, , 1, V8
a b c
[1,] 73 75 77
[2,] 74 76 78
, , 2, V8
a b c
[1,] 79 81 83
[2,] 80 82 84
, , 3, V8
a b c
[1,] 85 87 89
[2,] 86 88 90
, , 4, V8
a b c
[1,] 91 93 95
[2,] 92 94 96
, , 1, V9
a b c
[1,] 97 99 101
[2,] 98 100 102
, , 2, V9
a b c
[1,] 103 105 107
[2,] 104 106 108
, , 3, V9
a b c
[1,] 109 111 113
[2,] 110 112 114
, , 4, V9
a b c
[1,] 115 117 119
[2,] 116 118 120
> for(m in marg) ## 75% of the time here was spent on the names
+ print(dim(apply(arr, print(m), quantile, names=FALSE)) == c(5,d.arr[m]))
[1] 1 2
[1] TRUE TRUE TRUE
[1] 2 3
[1] TRUE TRUE TRUE
[1] 2 4
[1] TRUE TRUE TRUE
[1] 1 3
[1] TRUE TRUE TRUE
[1] 2 3 4
[1] TRUE TRUE TRUE TRUE
[1] 1 2 3
[1] TRUE TRUE TRUE TRUE
[1] 1 2 3 4
[1] TRUE TRUE TRUE TRUE TRUE
>
> ## Bessel
> nus <- c(0:5,10,20)
>
> x0 <- 2^(-20:10)
> plot(x0,x0, log='xy', ylab="", ylim=c(.1,1e60),type='n',
+ main = "Bessel Functions -Y_nu(x) near 0\n log - log scale")
> for(nu in sort(c(nus,nus+.5))) lines(x0, -besselY(x0,nu=nu), col = nu+2)
> legend(3,1e50, leg=paste("nu=", paste(nus,nus+.5, sep=",")), col=nus+2, lwd=1)
>
> x <- seq(3,500);yl <- c(-.3, .2)
> plot(x,x, ylim = yl, ylab="",type='n', main = "Bessel Functions Y_nu(x)")
> for(nu in nus){xx <- x[x > .6*nu]; lines(xx,besselY(xx,nu=nu), col = nu+2)}
> legend(300,-.08, leg=paste("nu=",nus), col = nus+2, lwd=1)
>
> x <- seq(10,50000,by=10);yl <- c(-.1, .1)
> plot(x,x, ylim = yl, ylab="",type='n', main = "Bessel Functions Y_nu(x)")
> for(nu in nus){xx <- x[x > .6*nu]; lines(xx,besselY(xx,nu=nu), col = nu+2)}
> summary(bY <- besselY(2,nu = nu <- seq(0,100,len=501)))
Min. 1st Qu. Median Mean 3rd Qu. Max.
-3.001e+155 -1.067e+107 -1.976e+62 -9.961e+152 -2.059e+23 5.104e-01
> which(bY >= 0)
[1] 1 2 3 4 5
> summary(bY <- besselY(2,nu = nu <- seq(3,300,len=51)))
Min. 1st Qu. Median Mean 3rd Qu. Max.
-Inf -Inf -2.248e+263 -Inf -3.777e+116 -1.128e+00
There were 22 warnings (use warnings() to see them)
> summary(bI <- besselI(x = x <- 10:700, 1))
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.671e+03 6.026e+77 3.161e+152 3.501e+299 2.409e+227 1.529e+302
> ## end of moved from Bessel.Rd
>
> ## data.frame
> set.seed(123)
> L3 <- LETTERS[1:3]
> str(d <- data.frame(cbind(x=1, y=1:10), fac=sample(L3, 10, repl=TRUE)))
`data.frame': 10 obs. of 3 variables:
$ x : num 1 1 1 1 1 1 1 1 1 1
$ y : num 1 2 3 4 5 6 7 8 9 10
$ fac: Factor w/ 2 levels "A","C": 1 1 2 2 2 2 2 2 2 1
> (d0 <- d[, FALSE]) # NULL dataframe with 10 rows
NULL data frame with 10 rows
> (d.0 <- d[FALSE, ]) # <0 rows> dataframe (3 cols)
[1] x y fac
<0 rows> (or 0-length row.names)
> (d00 <- d0[FALSE,]) # NULL dataframe with 0 rows
NULL data frame with 0 rows
> (d000 <- data.frame()) #but not quite the same as d00:
NULL data frame with 0 rows
> !identical(d00, d000)
[1] TRUE
> dput(d00)
structure(list(), .Names = character(0), row.names = character(0), class = "data.frame")
> dput(d000)
structure(list(), row.names = character(0), class = "data.frame")
> stopifnot(identical(d, cbind(d, d0)),
+ identical(d, cbind(d0, d)),
+ identical(d, rbind(d,d.0)),
+ identical(d, rbind(d.0,d)),
+ identical(d, rbind(d00,d)),
+ identical(d, rbind(d,d00)),
+
+ TRUE )
> ## Comments: failed before ver. 1.4.0
>
> ## diag
> diag(array(1:4, dim=5))
[,1] [,2] [,3] [,4] [,5]
[1,] 1 0 0 0 0
[2,] 0 2 0 0 0
[3,] 0 0 3 0 0
[4,] 0 0 0 4 0
[5,] 0 0 0 0 1
> ## test behaviour with 0 rows or columns
> diag(0)
<0 x 0 matrix>
> z <- matrix(0, 0, 4)
> diag(z)
numeric(0)
> diag(z) <- numeric(0)
> z
[,1] [,2] [,3] [,4]
> ## end of moved from diag.Rd
>
> ## format
> ## handling of quotes
> zz <- data.frame(a=I("abc"), b=I("def\"gh"))
> format(zz)
a b
1 abc def"gh
> ## " (E fontification)
>
> ## printing more than 16 is platform-dependent
> for(i in c(1:5,10,15,16)) cat(i,":\t",format(pi,digits=i),"\n")
1 : 3
2 : 3.1
3 : 3.14
4 : 3.142
5 : 3.1416
10 : 3.141592654
15 : 3.14159265358979
16 : 3.141592653589793
>
> p <- c(47,13,2,.1,.023,.0045, 1e-100)/1000
> format.pval(p)
[1] "0.0470" "0.0130" "0.0020" "0.0001" "2.3e-05" "4.5e-06" "< 2e-16"
> format.pval(p / 0.9)
[1] "0.05222222" "0.01444444" "0.00222222" "0.00011111" "2.5556e-05"
[6] "5.0000e-06" "< 2.22e-16"
> format.pval(p / 0.9, dig=3)
[1] "0.052222" "0.014444" "0.002222" "0.000111" "2.56e-05" "5.00e-06" "< 2e-16"
> ## end of moved from format.Rd
>
>
> ## is.finite
> x <- c(100,-1e-13,Inf,-Inf, NaN, pi, NA)
> x # 1.000000 -3.000000 Inf -Inf NA 3.141593 NA
[1] 1.000000e+02 -1.000000e-13 Inf -Inf NaN
[6] 3.141593e+00 NA
> names(x) <- formatC(x, dig=3)
> is.finite(x)
100 -1e-13 Inf -Inf NaN 3.14 NA
TRUE TRUE FALSE FALSE FALSE TRUE FALSE
> ##- 100 -1e-13 Inf -Inf NaN 3.14 NA
> ##- T T . . . T .
> is.na(x)
100 -1e-13 Inf -Inf NaN 3.14 NA
FALSE FALSE FALSE FALSE TRUE FALSE TRUE
> ##- 100 -1e-13 Inf -Inf NaN 3.14 NA
> ##- . . . . T . T
> which(is.na(x) & !is.nan(x))# only 'NA': 7
NA
7
>
> is.na(x) | is.finite(x)
100 -1e-13 Inf -Inf NaN 3.14 NA
TRUE TRUE FALSE FALSE TRUE TRUE TRUE
> ##- 100 -1e-13 Inf -Inf NaN 3.14 NA
> ##- T T . . T T T
> is.infinite(x)
100 -1e-13 Inf -Inf NaN 3.14 NA
FALSE FALSE TRUE TRUE FALSE FALSE FALSE
> ##- 100 -1e-13 Inf -Inf NaN 3.14 NA
> ##- . . T T . . .
>
> ##-- either finite or infinite or NA:
> all(is.na(x) != is.finite(x) | is.infinite(x)) # TRUE
[1] TRUE
> all(is.nan(x) != is.finite(x) | is.infinite(x)) # FALSE: have 'real' NA
[1] FALSE
>
> ##--- Integer
> (ix <- structure(as.integer(x),names= names(x)))
100 -1e-13 Inf -Inf NaN 3.14 NA
100 0 NA NA NA 3 NA
Warning message:
NAs introduced by coercion
> ##- 100 -1e-13 Inf -Inf NaN 3.14 NA
> ##- 100 0 NA NA NA 3 NA
> all(is.na(ix) != is.finite(ix) | is.infinite(ix)) # TRUE (still)
[1] TRUE
>
> storage.mode(ii <- -3:5)
[1] "integer"
> storage.mode(zm <- outer(ii,ii, FUN="*"))# integer
[1] "double"
> storage.mode(zd <- outer(ii,ii, FUN="/"))# double
[1] "double"
> range(zd, na.rm=TRUE)# -Inf Inf
[1] -Inf Inf
> zd[,ii==0]
[1] -Inf -Inf -Inf NaN Inf Inf Inf Inf Inf
>
> (storage.mode(print(1:1 / 0:0)))# Inf "double"
[1] Inf
[1] "double"
> (storage.mode(print(1:1 / 1:1)))# 1 "double"
[1] 1
[1] "double"
> (storage.mode(print(1:1 + 1:1)))# 2 "integer"
[1] 2
[1] "integer"
> (storage.mode(print(2:2 * 2:2)))# 4 "integer"
[1] 4
[1] "integer"
> ## end of moved from is.finite.Rd
>
>
> ## kronecker
> fred <- matrix(1:12, 3, 4, dimnames=list(LETTERS[1:3], LETTERS[4:7]))
> bill <- c("happy" = 100, "sad" = 1000)
> kronecker(fred, bill, make.dimnames = TRUE)
D: E: F: G:
A:happy 100 400 700 1000
A:sad 1000 4000 7000 10000
B:happy 200 500 800 1100
B:sad 2000 5000 8000 11000
C:happy 300 600 900 1200
C:sad 3000 6000 9000 12000
>
> bill <- outer(bill, c("cat"=3, "dog"=4))
> kronecker(fred, bill, make.dimnames = TRUE)
D:cat D:dog E:cat E:dog F:cat F:dog G:cat G:dog
A:happy 300 400 1200 1600 2100 2800 3000 4000
A:sad 3000 4000 12000 16000 21000 28000 30000 40000
B:happy 600 800 1500 2000 2400 3200 3300 4400
B:sad 6000 8000 15000 20000 24000 32000 33000 44000
C:happy 900 1200 1800 2400 2700 3600 3600 4800
C:sad 9000 12000 18000 24000 27000 36000 36000 48000
>
> # dimnames are hard work: let's test them thoroughly
>
> dimnames(bill) <- NULL
> kronecker(fred, bill, make=TRUE)
D: D: E: E: F: F: G: G:
A: 300 400 1200 1600 2100 2800 3000 4000
A: 3000 4000 12000 16000 21000 28000 30000 40000
B: 600 800 1500 2000 2400 3200 3300 4400
B: 6000 8000 15000 20000 24000 32000 33000 44000
C: 900 1200 1800 2400 2700 3600 3600 4800
C: 9000 12000 18000 24000 27000 36000 36000 48000
> kronecker(bill, fred, make=TRUE)
:D :E :F :G :D :E :F :G
:A 300 1200 2100 3000 400 1600 2800 4000
:B 600 1500 2400 3300 800 2000 3200 4400
:C 900 1800 2700 3600 1200 2400 3600 4800
:A 3000 12000 21000 30000 4000 16000 28000 40000
:B 6000 15000 24000 33000 8000 20000 32000 44000
:C 9000 18000 27000 36000 12000 24000 36000 48000
>
> dim(bill) <- c(2, 2, 1)
> dimnames(bill) <- list(c("happy", "sad"), NULL, "")
> kronecker(fred, bill, make=TRUE)
, , :
D: D: E: E: F: F: G: G:
A:happy 300 400 1200 1600 2100 2800 3000 4000
A:sad 3000 4000 12000 16000 21000 28000 30000 40000
B:happy 600 800 1500 2000 2400 3200 3300 4400
B:sad 6000 8000 15000 20000 24000 32000 33000 44000
C:happy 900 1200 1800 2400 2700 3600 3600 4800
C:sad 9000 12000 18000 24000 27000 36000 36000 48000
>
> bill <- array(1:24, c(3, 4, 2))
> dimnames(bill) <- list(NULL, NULL, c("happy", "sad"))
> kronecker(bill, fred, make=TRUE)
, , happy:
:D :E :F :G :D :E :F :G :D :E :F :G :D :E :F :G
:A 1 4 7 10 4 16 28 40 7 28 49 70 10 40 70 100
:B 2 5 8 11 8 20 32 44 14 35 56 77 20 50 80 110
:C 3 6 9 12 12 24 36 48 21 42 63 84 30 60 90 120
:A 2 8 14 20 5 20 35 50 8 32 56 80 11 44 77 110
:B 4 10 16 22 10 25 40 55 16 40 64 88 22 55 88 121
:C 6 12 18 24 15 30 45 60 24 48 72 96 33 66 99 132
:A 3 12 21 30 6 24 42 60 9 36 63 90 12 48 84 120
:B 6 15 24 33 12 30 48 66 18 45 72 99 24 60 96 132
:C 9 18 27 36 18 36 54 72 27 54 81 108 36 72 108 144
, , sad:
:D :E :F :G :D :E :F :G :D :E :F :G :D :E :F :G
:A 13 52 91 130 16 64 112 160 19 76 133 190 22 88 154 220
:B 26 65 104 143 32 80 128 176 38 95 152 209 44 110 176 242
:C 39 78 117 156 48 96 144 192 57 114 171 228 66 132 198 264
:A 14 56 98 140 17 68 119 170 20 80 140 200 23 92 161 230
:B 28 70 112 154 34 85 136 187 40 100 160 220 46 115 184 253
:C 42 84 126 168 51 102 153 204 60 120 180 240 69 138 207 276
:A 15 60 105 150 18 72 126 180 21 84 147 210 24 96 168 240
:B 30 75 120 165 36 90 144 198 42 105 168 231 48 120 192 264
:C 45 90 135 180 54 108 162 216 63 126 189 252 72 144 216 288
> kronecker(fred, bill, make=TRUE)
, , :happy
D: D: D: D: E: E: E: E: F: F: F: F: G: G: G: G:
A: 1 4 7 10 4 16 28 40 7 28 49 70 10 40 70 100
A: 2 5 8 11 8 20 32 44 14 35 56 77 20 50 80 110
A: 3 6 9 12 12 24 36 48 21 42 63 84 30 60 90 120
B: 2 8 14 20 5 20 35 50 8 32 56 80 11 44 77 110
B: 4 10 16 22 10 25 40 55 16 40 64 88 22 55 88 121
B: 6 12 18 24 15 30 45 60 24 48 72 96 33 66 99 132
C: 3 12 21 30 6 24 42 60 9 36 63 90 12 48 84 120
C: 6 15 24 33 12 30 48 66 18 45 72 99 24 60 96 132
C: 9 18 27 36 18 36 54 72 27 54 81 108 36 72 108 144
, , :sad
D: D: D: D: E: E: E: E: F: F: F: F: G: G: G: G:
A: 13 16 19 22 52 64 76 88 91 112 133 154 130 160 190 220
A: 14 17 20 23 56 68 80 92 98 119 140 161 140 170 200 230
A: 15 18 21 24 60 72 84 96 105 126 147 168 150 180 210 240
B: 26 32 38 44 65 80 95 110 104 128 152 176 143 176 209 242
B: 28 34 40 46 70 85 100 115 112 136 160 184 154 187 220 253
B: 30 36 42 48 75 90 105 120 120 144 168 192 165 198 231 264
C: 39 48 57 66 78 96 114 132 117 144 171 198 156 192 228 264
C: 42 51 60 69 84 102 120 138 126 153 180 207 168 204 240 276
C: 45 54 63 72 90 108 126 144 135 162 189 216 180 216 252 288
>
> fred <- outer(fred, c("frequentist"=4, "bayesian"=4000))
> kronecker(fred, bill, make=TRUE)
, , frequentist:happy
D: D: D: D: E: E: E: E: F: F: F: F: G: G: G: G:
A: 4 16 28 40 16 64 112 160 28 112 196 280 40 160 280 400
A: 8 20 32 44 32 80 128 176 56 140 224 308 80 200 320 440
A: 12 24 36 48 48 96 144 192 84 168 252 336 120 240 360 480
B: 8 32 56 80 20 80 140 200 32 128 224 320 44 176 308 440
B: 16 40 64 88 40 100 160 220 64 160 256 352 88 220 352 484
B: 24 48 72 96 60 120 180 240 96 192 288 384 132 264 396 528
C: 12 48 84 120 24 96 168 240 36 144 252 360 48 192 336 480
C: 24 60 96 132 48 120 192 264 72 180 288 396 96 240 384 528
C: 36 72 108 144 72 144 216 288 108 216 324 432 144 288 432 576
, , frequentist:sad
D: D: D: D: E: E: E: E: F: F: F: F: G: G: G: G:
A: 52 64 76 88 208 256 304 352 364 448 532 616 520 640 760 880
A: 56 68 80 92 224 272 320 368 392 476 560 644 560 680 800 920
A: 60 72 84 96 240 288 336 384 420 504 588 672 600 720 840 960
B: 104 128 152 176 260 320 380 440 416 512 608 704 572 704 836 968
B: 112 136 160 184 280 340 400 460 448 544 640 736 616 748 880 1012
B: 120 144 168 192 300 360 420 480 480 576 672 768 660 792 924 1056
C: 156 192 228 264 312 384 456 528 468 576 684 792 624 768 912 1056
C: 168 204 240 276 336 408 480 552 504 612 720 828 672 816 960 1104
C: 180 216 252 288 360 432 504 576 540 648 756 864 720 864 1008 1152
, , bayesian:happy
D: D: D: D: E: E: E: E: F: F: F:
A: 4000 16000 28000 40000 16000 64000 112000 160000 28000 112000 196000
A: 8000 20000 32000 44000 32000 80000 128000 176000 56000 140000 224000
A: 12000 24000 36000 48000 48000 96000 144000 192000 84000 168000 252000
B: 8000 32000 56000 80000 20000 80000 140000 200000 32000 128000 224000
B: 16000 40000 64000 88000 40000 100000 160000 220000 64000 160000 256000
B: 24000 48000 72000 96000 60000 120000 180000 240000 96000 192000 288000
C: 12000 48000 84000 120000 24000 96000 168000 240000 36000 144000 252000
C: 24000 60000 96000 132000 48000 120000 192000 264000 72000 180000 288000
C: 36000 72000 108000 144000 72000 144000 216000 288000 108000 216000 324000
F: G: G: G: G:
A: 280000 40000 160000 280000 400000
A: 308000 80000 200000 320000 440000
A: 336000 120000 240000 360000 480000
B: 320000 44000 176000 308000 440000
B: 352000 88000 220000 352000 484000
B: 384000 132000 264000 396000 528000
C: 360000 48000 192000 336000 480000
C: 396000 96000 240000 384000 528000
C: 432000 144000 288000 432000 576000
, , bayesian:sad
D: D: D: D: E: E: E: E: F: F: F:
A: 52000 64000 76000 88000 208000 256000 304000 352000 364000 448000 532000
A: 56000 68000 80000 92000 224000 272000 320000 368000 392000 476000 560000
A: 60000 72000 84000 96000 240000 288000 336000 384000 420000 504000 588000
B: 104000 128000 152000 176000 260000 320000 380000 440000 416000 512000 608000
B: 112000 136000 160000 184000 280000 340000 400000 460000 448000 544000 640000
B: 120000 144000 168000 192000 300000 360000 420000 480000 480000 576000 672000
C: 156000 192000 228000 264000 312000 384000 456000 528000 468000 576000 684000
C: 168000 204000 240000 276000 336000 408000 480000 552000 504000 612000 720000
C: 180000 216000 252000 288000 360000 432000 504000 576000 540000 648000 756000
F: G: G: G: G:
A: 616000 520000 640000 760000 880000
A: 644000 560000 680000 800000 920000
A: 672000 600000 720000 840000 960000
B: 704000 572000 704000 836000 968000
B: 736000 616000 748000 880000 1012000
B: 768000 660000 792000 924000 1056000
C: 792000 624000 768000 912000 1056000
C: 828000 672000 816000 960000 1104000
C: 864000 720000 864000 1008000 1152000
> ## end of moved from kronecker.Rd
>
> ## merge
> authors <- data.frame(
+ surname = c("Tukey", "Venables", "Tierney", "Ripley", "McNeil"),
+ nationality = c("US", "Australia", "US", "UK", "Australia"),
+ deceased = c("yes", rep("no", 4)))
> books <- data.frame(
+ name = c("Tukey", "Venables", "Tierney",
+ "Ripley", "Ripley", "McNeil", "R Core"),
+ title = c("Exploratory Data Analysis",
+ "Modern Applied Statistics ...",
+ "LISP-STAT",
+ "Spatial Statistics", "Stochastic Simulation",
+ "Interactive Data Analysis",
+ "An Introduction to R"),
+ other.author = c(NA, "Ripley", NA, NA, NA, NA,
+ "Venables & Smith"))
> b2 <- books; names(b2)[1] <- names(authors)[1]
>
> merge(authors, b2, all.x = TRUE)
surname nationality deceased title other.author
1 McNeil Australia no Interactive Data Analysis <NA>
2 Ripley UK no Spatial Statistics <NA>
3 Ripley UK no Stochastic Simulation <NA>
4 Tierney US no LISP-STAT <NA>
5 Tukey US yes Exploratory Data Analysis <NA>
6 Venables Australia no Modern Applied Statistics ... Ripley
> merge(authors, b2, all.y = TRUE)
surname nationality deceased title other.author
1 McNeil Australia no Interactive Data Analysis <NA>
2 Ripley UK no Spatial Statistics <NA>
3 Ripley UK no Stochastic Simulation <NA>
4 Tierney US no LISP-STAT <NA>
5 Tukey US yes Exploratory Data Analysis <NA>
6 Venables Australia no Modern Applied Statistics ... Ripley
7 R Core <NA> <NA> An Introduction to R Venables & Smith
>
> ## empty d.f. :
> merge(authors, b2[7,])
[1] surname nationality deceased title other.author
<0 rows> (or 0-length row.names)
>
> merge(authors, b2[7,], all.y = TRUE)
surname nationality deceased title other.author
1 R Core <NA> <NA> An Introduction to R Venables & Smith
> merge(authors, b2[7,], all.x = TRUE)
surname nationality deceased title other.author
1 McNeil Australia no <NA> <NA>
2 Ripley UK no <NA> <NA>
3 Tierney US no <NA> <NA>
4 Tukey US yes <NA> <NA>
5 Venables Australia no <NA> <NA>
> ## end of moved from merge.Rd
>
> ## NA
> is.na(c(1,NA))
[1] FALSE TRUE
> is.na(paste(c(1,NA)))
[1] FALSE FALSE
> is.na(list())# logical(0)
logical(0)
> ll <- list(pi,"C",NaN,Inf, 1:3, c(0,NA), NA)
> is.na (ll)
[1] FALSE FALSE TRUE FALSE FALSE FALSE TRUE
> is.nan(ll)
[1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE
> ## end of moved from NA.Rd
>
> ## scale
> ## test out NA handling
> tm <- matrix(c(2,1,0,1,0,NA,NA,NA,0), nrow=3)
> scale(tm, , FALSE)
[,1] [,2] [,3]
[1,] 1 0.5 NA
[2,] 0 -0.5 NA
[3,] -1 NA 0
attr(,"scaled:center")
[1] 1.0 0.5 0.0
> scale(tm)
[,1] [,2] [,3]
[1,] 1 0.7071068 NA
[2,] 0 -0.7071068 NA
[3,] -1 NA NaN
attr(,"scaled:center")
[1] 1.0 0.5 0.0
attr(,"scaled:scale")
[1] 1.0000000 0.7071068 0.0000000
> ## end of moved from scale.Rd
>
> ## tabulate
> tabulate(numeric(0))
[1] 0
> ## end of moved from tabulate.Rd
>
> ## ts
> # Ensure working arithmetic for `ts' objects :
> stopifnot(z == z)
> stopifnot(z-z == 0)
>
> ts(1:5, start=2, end=4) # truncate
Time Series:
Start = 2
End = 4
Frequency = 1
[1] 1 2 3
> ts(1:5, start=3, end=17)# repeat
Time Series:
Start = 3
End = 17
Frequency = 1
[1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
> ## end of moved from ts.Rd
>
> ### end of moved
>
>
> ## 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(,"class")
[1] "omit"
> ## should print as
> # [,1] [,2]
> # [1,] 1 0
> # [2,] 2 10
> # attr(,"na.action")
> # [1] 3
> # attr(,"na.action")
> # [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 1028 : 4 1st Qu.: 11.32
Median :18.00 Median :6.100 113 : 4 Median : 23.40
Mean :14.74 Mean :6.084 112 : 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 1028 : 4 1st Qu.: 11.325
Median :18.000 Median :6.1000 113 : 4 Median : 23.400
Mean :14.742 Mean :6.0841 112 : 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
<none> 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
<none> 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
<none> 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
<none> 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 <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.
> 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 1392.3429
> dist(z, method="canberra")
Girth Height
Height 21.66477
Volume 10.96200 13.63365
>
> ## F. Tusell 2001-03-07. printing kernels.
> 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 <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
> res <- svd(rbind(1:7))## $v lost dimensions in 1.2.3
> if(res$u[1,1] < 0) {res$u <- -res$u; res$v <- -res$v}
> res
$d
[1] 11.83216
$u
[,1]
[1,] 1
$v
[,1]
[1,] 0.08451543
[2,] 0.16903085
[3,] 0.25354628
[4,] 0.33806170
[5,] 0.42257713
[6,] 0.50709255
[7,] 0.59160798
>
>
> ## Make sure on.exit() keeps being evaluated in the proper env [from PD]:
> ## A more complete example:
> g1 <- function(fitted) { on.exit(remove(fitted)); return(function(foo) foo) }
> g2 <- function(fitted) { on.exit(remove(fitted)); function(foo) foo }
> f <- function(g) { fitted <- 1; h <- g(fitted); print(fitted)
+ ls(envir=environment(h)) }
> f(g1)
[1] 1
character(0)
> f(g2)
[1] 1
character(0)
>
> f2 <- function()
+ {
+ g.foo <- g1
+ g.bar <- g2
+ g <- function(x,...) UseMethod("g")
+ fitted <- 1; class(fitted) <- "foo"
+ h <- g(fitted); print(fitted); print(ls(envir=environment(h)))
+ fitted <- 1; class(fitted) <- "bar"
+ h <- g(fitted); print(fitted); print(ls(envir=environment(h)))
+ invisible(NULL)
+ }
> f2()
[1] 1
attr(,"class")
[1] "foo"
character(0)
[1] 1
attr(,"class")
[1] "bar"
character(0)
> ## The first case in f2() is broken in 1.3.0(-patched).
>
> ## on.exit() consistency check from Luke:
> g <- function() as.environment(-1)
> f <- function(x) UseMethod("f")
> f.foo <- function(x) { on.exit(e <<- g()); NULL }
> f.bar <- function(x) { on.exit(e <<- g()); return(NULL) }
> f(structure(1,class = "foo"))
NULL
> ls(env = e)# only "x", i.e. *not* the GlobalEnv
[1] "x"
> f(structure(1,class = "bar"))
NULL
> stopifnot("x" == ls(env = e))# as above; wrongly was .GlobalEnv in R 1.3.x
>
>
> ## some tests that R supports logical variables in formulae
> ## it coerced them to numeric prior to 1.4.0
> ## they should appear like 2-level factors, following S
>
> oldCon <- options("contrasts")
> y <- rnorm(10)
> x <- rep(c(TRUE, FALSE), 5)
> model.matrix(y ~ x)
(Intercept) xTRUE
1 1 1
2 1 0
3 1 1
4 1 0
5 1 1
6 1 0
7 1 1
8 1 0
9 1 1
10 1 0
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$x
[1] "contr.treatment"
> lm(y ~ x)
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) xTRUE
0.1230 0.3170
> DF <- data.frame(x, y)
> lm(y ~ x, data=DF)
Call:
lm(formula = y ~ x, data = DF)
Coefficients:
(Intercept) xTRUE
0.1230 0.3170
> options(contrasts=c("contr.helmert", "contr.poly"))
> model.matrix(y ~ x)
(Intercept) x1
1 1 1
2 1 -1
3 1 1
4 1 -1
5 1 1
6 1 -1
7 1 1
8 1 -1
9 1 1
10 1 -1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$x
[1] "contr.helmert"
> lm(y ~ x, data=DF)
Call:
lm(formula = y ~ x, data = DF)
Coefficients:
(Intercept) x1
0.2814 0.1585
> z <- 1:10
> lm(y ~ x*z)
Call:
lm(formula = y ~ x * z)
Coefficients:
(Intercept) x1 z x1:z
0.49064 -0.68273 -0.02433 0.15074
> lm(y ~ x*z - 1)
Call:
lm(formula = y ~ x * z - 1)
Coefficients:
xFALSE xTRUE z x1:z
1.17337 -0.19209 -0.02433 0.15074
> options(oldCon)
>
> ## diffinv, Adrian Trapletti, 2001-08-27
> x <- ts(1:10)
> diffinv(diff(x),xi=x[1])
Time Series:
Start = 1
End = 10
Frequency = 1
[1] 1 2 3 4 5 6 7 8 9 10
> diffinv(diff(x,lag=1,differences=2),lag=1,differences=2,xi=x[1:2])
Time Series:
Start = 1
End = 10
Frequency = 1
[1] 1 2 3 4 5 6 7 8 9 10
> ## last had wrong start and end
>
> ## 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.
>
> ## PR#1092 (rowsum dimnames)
> rowsum(matrix(1:12, 3,4), c("Y","X","Y"))
[,1] [,2] [,3] [,4]
X 2 5 8 11
Y 4 10 16 22
> ## rownames were 1,2 in <= 1.3.1.
>
> ## PR#1115 (saving strings with ascii=TRUE)
> x <- y <- unlist(as.list(
+ parse(text=paste("\"\\",
+ as.character(structure(0:255,class="octmode")),
+ "\"",sep=""))))
> save(x, ascii=T, file=(fn <- tempfile()))
> load(fn)
> all(x==y)
[1] TRUE
> unlink(fn)
> ## 1.3.1 had trouble with \
>
>
> ## Some tests of sink() and connections()
> ## capture all the output to a file.
> zz <- file("all.Rout", open="wt")
> sink(zz)
> sink(zz, type="message")
> try(log("a"))
> ## back to the console
> sink(type="message")
> sink()
> try(log("a"))
Error in log(x) : Non-numeric argument to mathematical function
>
> ## capture all the output to a file.
> zz <- file("all.Rout", open="wt")
> sink(zz)
> sink(zz, type="message")
> try(log("a"))
>
> ## bail out
> closeAllConnections()
> (foo <- showConnections())
description class mode text isopen can read can write
> stopifnot(nrow(foo) == 0)
> try(log("a"))
Error in log(x) : Non-numeric argument to mathematical function
> unlink("all.Rout")
> ## many of these were untested before 1.4.0.
>
>
> ## test mean() works on logical but not factor
> x <- c(TRUE, FALSE, TRUE, TRUE)
> mean(x)
[1] 0.75
> mean(as.factor(x))
[1] NA
Warning message:
argument is not numeric or logical: returning NA in: mean.default(as.factor(x))
> ## last had confusing error message in 1.3.1.
>
>
> ## Kurt Hornik 2001-Nov-13
> z <- table(x = 1:2, y = 1:2)
> z - 1
y
x 1 2
1 0 -1
2 -1 0
> unclass(z - 1)
y
x 1 2
1 0 -1
2 -1 0
> ## lost object bit prior to 1.4.0, so printed class attribute.
>
>
> ## PR#1226 (predict.mlm ignored newdata)
> ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
> trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
> group <- gl(2,10,20, labels = c("Ctl","Trt"))
> weight <- c(ctl, trt)
> data <- data.frame(weight, group)
> fit <- lm(cbind(w=weight, w2=weight^2) ~ group, data=data)
> predict(fit, newdata=data[1:2, ])
w w2
1 5.032 25.62702
2 5.032 25.62702
> ## was 20 rows in R <= 1.4.0
>
>
> ## Chong Gu 2002-Feb-8: `.' not expanded in drop1
> data(HairEyeColor)
> lab <- dimnames(HairEyeColor)
> HairEye <- cbind(expand.grid(Hair=lab$Hair, Eye=lab$Eye, Sex=lab$Sex),
+ Fr=as.vector(HairEyeColor))
> HairEye.fit <- glm(Fr ~ . ^2, poisson, HairEye)
> drop1(HairEye.fit)
Single term deletions
Model:
Fr ~ .^2
Df Deviance AIC
<none> 8.19 192.94
Hair:Eye 9 162.21 328.96
Hair:Sex 3 22.03 200.78
Eye:Sex 3 23.08 201.83
> ## broken around 1.2.1 it seems.
>
>
> ## PR#1329 (subscripting matrix lists)
> m <- list(a1=1:3, a2=4:6, a3=pi, a4=c("a","b","c"))
> dim(m) <- c(2,2)
> m
[,1] [,2]
[1,] Integer,3 3.141593
[2,] Integer,3 Character,3
> m[,2]
[[1]]
[1] 3.141593
[[2]]
[1] "a" "b" "c"
> m[2,2]
[[1]]
[1] "a" "b" "c"
> ## 1.4.1 returned null components: the case was missing from a switch.
>
> m <- list(a1=1:3, a2=4:6, a3=pi, a4=c("a","b","c"))
> matrix(m, 2, 2)
[,1] [,2]
[1,] Integer,3 3.141593
[2,] Integer,3 Character,3
> ## 1.4.1 gave `Unimplemented feature in copyVector'
>
> x <- vector("list",6)
> dim(x) <- c(2,3)
> x[1,2] <- list(letters[10:11])
> x
[,1] [,2] [,3]
[1,] NULL Character,2 NULL
[2,] NULL NULL NULL
> ## 1.4.1 gave `incompatible types in subset assignment'
>
>
> ## printing of matrix lists
> m <- list(as.integer(1), pi, 3+5i, "testit", TRUE, factor("foo"))
> dim(m) <- c(1, 6)
> m
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 3.141593 3+5i "testit" TRUE factor,1
> ## prior to 1.5.0 had quotes for 2D case (but not kD, k > 2),
> ## gave "numeric,1" etc, (even "numeric,1" for integers and factors)
>
>
> ## ensure RNG is unaltered.
> for(type in c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper",
+ "Mersenne-Twister", "Knuth-TAOCP", "Knuth-TAOCP-2002"))
+ {
+ set.seed(123, type)
+ print(RNGkind())
+ runif(100); print(runif(4))
+ set.seed(1000, type)
+ runif(100); print(runif(4))
+ set.seed(77, type)
+ runif(100); print(runif(4))
+ }
[1] "Wichmann-Hill" "Buggy Kinderman-Ramage"
[1] 0.8308841 0.4640221 0.9460082 0.8764644
[1] 0.12909876 0.07294851 0.45594560 0.68884911
[1] 0.4062450 0.7188432 0.6241738 0.2511611
[1] "Marsaglia-Multicarry" "Buggy Kinderman-Ramage"
[1] 0.3479705 0.9469351 0.2489207 0.7329251
[1] 0.5041512 0.3617873 0.1469184 0.3798119
[1] 0.14388128 0.04196294 0.36214015 0.86053575
[1] "Super-Duper" "Buggy Kinderman-Ramage"
[1] 0.2722510 0.9230240 0.3971743 0.8284474
[1] 0.5706241 0.1806023 0.9633860 0.8434444
[1] 0.09356585 0.41081124 0.38635627 0.72993396
[1] "Mersenne-Twister" "Buggy Kinderman-Ramage"
[1] 0.5999890 0.3328235 0.4886130 0.9544738
[1] 0.5993679 0.4516818 0.1368254 0.7261788
[1] 0.09594961 0.31235651 0.81244335 0.72330846
[1] "Knuth-TAOCP" "Buggy Kinderman-Ramage"
[1] 0.9445502 0.3366297 0.6296881 0.5914161
[1] 0.9213954 0.5468138 0.8817100 0.4442237
[1] 0.8016962 0.9226080 0.1473484 0.8827707
[1] "Knuth-TAOCP-2002" "Buggy Kinderman-Ramage"
[1] 0.9303634 0.2812239 0.1085806 0.8053228
[1] 0.2916627 0.9085017 0.7958965 0.1980655
[1] 0.05247575 0.28290867 0.20930324 0.16794887
> RNGkind(normal.kind = "Kinderman-Ramage")
> set.seed(123)
> RNGkind()
[1] "Knuth-TAOCP-2002" "Kinderman-Ramage"
> rnorm(4)
[1] -1.9699090 -2.2429340 0.5339321 0.2097153
> RNGkind(normal.kind = "Ahrens-Dieter")
> set.seed(123)
> RNGkind()
[1] "Knuth-TAOCP-2002" "Ahrens-Dieter"
> rnorm(4)
[1] 0.06267229 0.12421568 -1.86653499 -0.14535921
> RNGkind(normal.kind = "Box-Muller")
> set.seed(123)
> RNGkind()
[1] "Knuth-TAOCP-2002" "Box-Muller"
> rnorm(4)
[1] 2.26160990 0.59010303 0.30176045 -0.01346139
> set.seed(123)
> runif(4)
[1] 0.04062130 0.06511825 0.99290488 0.95540467
> set.seed(123, "default")
> set.seed(123, "Marsaglia-Multicarry") ## Careful, not the default anymore
> runif(4)
[1] 0.1200427 0.1991600 0.7292821 0.8115922
> ## last set.seed failed < 1.5.0.
>
>
> ## merging, ggrothendieck@yifan.net, 2002-03-16
> d.df <- data.frame(x = 1:3, y = c("A","D","E"), z = c(6,9,10))
> merge(d.df[1,], d.df)
x y z
1 1 A 6
> ## 1.4.1 got confused by inconsistencies in as.character
>
>
> ## PR#1394 (levels<-.factor)
> f <- factor(c("a","b"))
> levels(f) <- list(C="C", A="a", B="b")
> f
[1] A B
Levels: C A B
> ## was [1] C A; Levels: C A in 1.4.1
>
>
> ## PR#1408 Inconsistencies in sum()
> x <- as.integer(2^30)
> sum(x, x) # did not warn in 1.4.1
[1] NA
Warning message:
Integer overflow in sum(.); use sum(as.numeric(.))
> sum(c(x, x)) # did warn
[1] NA
Warning message:
Integer overflow in sum(.); use sum(as.numeric(.))
> (z <- sum(x, x, 0.0)) # was NA in 1.4.1
[1] 2147483648
> typeof(z)
[1] "double"
>
>
> ## NA levels in factors
> (x <- factor(c("a", "NA", "b"), exclude=NULL))
[1] a NA b
Levels: NA a b
> ## 1.4.1 had wrong order for levels
> is.na(x)[3] <- TRUE
> x
[1] a NA <NA>
Levels: NA a b
> ## missing entry prints as <NA>
>
>
> ## printing/formatting NA strings
> (x <- c("a", "NA", NA, "b"))
[1] "a" "NA" NA "b"
> print(x, quote = FALSE)
[1] a NA <NA> b
> paste(x)
[1] "a" "NA" "NA" "b"
> format(x)
[1] "a " "NA" "NA" "b "
> format(x, justify = "right")
[1] " a" "NA" "NA" " b"
> format(x, justify = "none")
[1] "a" "NA" NA "b"
> ## not ideal.
>
>
> ## print.ts problems ggrothendieck@yifan.net on R-help, 2002-04-01
> x <- 1:20
> tt1 <- ts(x,start=c(1960,2), freq=12)
> tt2 <- ts(10+x,start=c(1960,2), freq=12)
> cbind(tt1, tt2)
tt1 tt2
Feb 1960 1 11
Mar 1960 2 12
Apr 1960 3 13
May 1960 4 14
Jun 1960 5 15
Jul 1960 6 16
Aug 1960 7 17
Sep 1960 8 18
Oct 1960 9 19
Nov 1960 10 20
Dec 1960 11 21
Jan 1960 12 22
Feb 1961 13 23
Mar 1961 14 24
Apr 1961 15 25
May 1961 16 26
Jun 1961 17 27
Jul 1961 18 28
Aug 1961 19 29
Sep 1961 20 30
> ## 1.4.1 had `Jan 1961' as `NA 1961'
>
> ## glm boundary bugs (related to PR#1331)
> x <- c(0.35, 0.64, 0.12, 1.66, 1.52, 0.23, -1.99, 0.42, 1.86, -0.02,
+ -1.64, -0.46, -0.1, 1.25, 0.37, 0.31, 1.11, 1.65, 0.33, 0.89,
+ -0.25, -0.87, -0.22, 0.71, -2.26, 0.77, -0.05, 0.32, -0.64, 0.39,
+ 0.19, -1.62, 0.37, 0.02, 0.97, -2.62, 0.15, 1.55, -1.41, -2.35,
+ -0.43, 0.57, -0.66, -0.08, 0.02, 0.24, -0.33, -0.03, -1.13, 0.32,
+ 1.55, 2.13, -0.1, -0.32, -0.67, 1.44, 0.04, -1.1, -0.95, -0.19,
+ -0.68, -0.43, -0.84, 0.69, -0.65, 0.71, 0.19, 0.45, 0.45, -1.19,
+ 1.3, 0.14, -0.36, -0.5, -0.47, -1.31, -1.02, 1.17, 1.51, -0.33,
+ -0.01, -0.59, -0.28, -0.18, -1.07, 0.66, -0.71, 1.88, -0.14,
+ -0.19, 0.84, 0.44, 1.33, -0.2, -0.45, 1.46, 1, -1.02, 0.68, 0.84)
> y <- c(1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,
+ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1,
+ 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,
+ 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,
+ 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0)
> try(glm(y ~ x, family = poisson(identity)))
Error: no valid set of coefficients has been found:please supply starting values
In addition: Warning message:
NaNs produced in: log(x)
> ## failed because start = NULL in 1.4.1
> ## now gives useful error message
> glm(y ~ x, family = poisson(identity), start = c(1,0))
Call: glm(formula = y ~ x, family = poisson(identity), start = c(1, 0))
Coefficients:
(Intercept) x
0.5114 0.1690
Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
Null Deviance: 68.01
Residual Deviance: 60.66 AIC: 168.7
Warning messages:
1: Step size truncated: out of bounds
2: Step size truncated: out of bounds
> ## step reduction failed in 1.4.1
> set.seed(123)
> y <- rpois(100, pmax(3*x, 0))
> glm(y ~ x, family = poisson(identity), start = c(1,0))
Call: glm(formula = y ~ x, family = poisson(identity), start = c(1, 0))
Coefficients:
(Intercept) x
1.1561 0.4413
Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
Null Deviance: 317.2
Residual Deviance: 228.5 AIC: 344.7
There were 27 warnings (use warnings() to see them)
> warnings()
Warning messages:
1: Step size truncated: out of bounds
2: Step size truncated: out of bounds
3: Step size truncated: out of bounds
4: Step size truncated: out of bounds
5: Step size truncated: out of bounds
6: Step size truncated: out of bounds
7: Step size truncated: out of bounds
8: Step size truncated: out of bounds
9: Step size truncated: out of bounds
10: Step size truncated: out of bounds
11: Step size truncated: out of bounds
12: Step size truncated: out of bounds
13: Step size truncated: out of bounds
14: Step size truncated: out of bounds
15: Step size truncated: out of bounds
16: Step size truncated: out of bounds
17: Step size truncated: out of bounds
18: Step size truncated: out of bounds
19: Step size truncated: out of bounds
20: Step size truncated: out of bounds
21: Step size truncated: out of bounds
22: Step size truncated: out of bounds
23: Step size truncated: out of bounds
24: Step size truncated: out of bounds
25: Step size truncated: out of bounds
26: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, ...
27: Algorithm stopped at boundary value in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, ...
>
>
> ## extending char arrrays
> x <- y <- LETTERS[1:2]
> x[5] <- "C"
> length(y) <- 5
> x
[1] "A" "B" NA NA "C"
> y
[1] "A" "B" NA NA NA
> ## x was filled with "", y with NA in 1.5.0
>
>
> ## formula with no intercept, 2002-07-22
> oldcon <- options(contrasts = c("contr.helmert", "contr.poly"))
> U <- gl(3, 6, 18, labels=letters[1:3])
> V <- gl(3, 2, 18, labels=letters[1:3])
> A <- rep(c(0, 1), 9)
> B <- rep(c(1, 0), 9)
> set.seed(1); y <- rnorm(18)
> terms(y ~ A:U + A:V - 1)
y ~ A:U + A:V - 1
attr(,"variables")
list(y, A, U, V)
attr(,"factors")
A:U A:V
y 0 0
A 2 2
U 2 0
V 0 1
attr(,"term.labels")
[1] "A:U" "A:V"
attr(,"order")
[1] 2 2
attr(,"intercept")
[1] 0
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
> lm(y ~ A:U + A:V - 1)$coef # 1.5.1 used dummies coding for V
A:Ua A:Ub A:Uc A:V1 A:V2
0.25303884 -0.21875499 -0.71708528 -0.61467193 -0.09030436
> lm(y ~ (A + B) : (U + V) - 1) # 1.5.1 used dummies coding for A:V but not B:V
Call:
lm(formula = y ~ (A + B):(U + V) - 1)
Coefficients:
A:Ua A:Ub A:Uc A:V1 A:V2 B:Ua B:Ub B:Uc
0.2530 -0.2188 -0.7171 -0.6147 -0.0903 1.7428 0.0613 0.7649
B:V1 B:V2
-0.4420 0.5388
> options(oldcon)
> ## 1.5.1 miscomputed the first factor in the formula.
>
>
> ## quantile extremes, MM 13 Apr 2000 and PR#1852
> for(k in 0:5)
+ print(quantile(c(rep(-Inf,k+1), 0:k, rep(Inf, k)), pr=seq(0,1, .1)))
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
-Inf NaN NaN NaN NaN NaN NaN NaN NaN NaN 0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
-Inf -Inf -Inf NaN NaN 0.0 0.4 0.8 Inf Inf Inf
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
-Inf -Inf -Inf NaN NaN 0.5 1.2 1.9 Inf Inf Inf
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
-Inf -Inf -Inf -Inf 0 1 2 3 Inf Inf Inf
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
-Inf -Inf -Inf -Inf 0.2 1.5 2.8 Inf Inf Inf Inf
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
-Inf -Inf -Inf -Inf 0.4 2.0 3.6 Inf Inf Inf Inf
> x <- c(-Inf, -Inf, Inf, Inf)
> median(x)
[1] NaN
> quantile(x)
0% 25% 50% 75% 100%
-Inf -Inf NaN Inf Inf
> ## 1.5.1 had -Inf not NaN in several places
>
>
> ## NAs in matrix dimnames
> z <- matrix(1:9, 3, 3)
> dimnames(z) <- list(c("x", "y", NA), c(1, NA, 3))
> z
1 <NA> 3
x 1 4 7
y 2 5 8
<NA> 3 6 9
> ## NAs in dimnames misaligned when printing in 1.5.1
>
>
> ## weighted aov (PR#1930)
> r <- c(10,23,23,26,17,5,53,55,32,46,10,8,10,8,23,0,3,22,15,32,3)
> n <- c(39,62,81,51,39,6,74,72,51,79,13,16,30,28,45,4,12,41,30,51,7)
> trt <- factor(rep(1:4,c(5,6,5,5)))
> Y <- r/n
> z <- aov(Y ~ trt, weights=n)
> ## 1.5.1 gave unweighted RSS
>
>
> ## rbind (PR#2266)
> test <- as.data.frame(matrix(1:25, 5, 5))
> test1 <- matrix(-(1:10), 2, 5)
> rbind(test, test1)
V1 V2 V3 V4 V5
1 1 6 11 16 21
2 2 7 12 17 22
3 3 8 13 18 23
4 4 9 14 19 24
5 5 10 15 20 25
11 -1 -3 -5 -7 -9
21 -2 -4 -6 -8 -10
> rbind(test1, test)
V1 V2 V3 V4 V5
1 -1 -3 -5 -7 -9
2 -2 -4 -6 -8 -10
11 1 6 11 16 21
21 2 7 12 17 22
3 3 8 13 18 23
4 4 9 14 19 24
5 5 10 15 20 25
> ## 1.6.1 treated matrix as a vector.
>
>
> ## escapes in non-quoted printing
> x <- "\\abc\\"
> names(x) <- 1
> x
1
"\\abc\\"
> print(x, quote=FALSE)
1
\\abc\\
> ## 1.6.2 had label misaligned
>
>
> ## summary on data frames containing data frames (PR#1891)
> x <- data.frame(1:10)
> x$z <- data.frame(x=1:10,yyy=11:20)
> summary(x)
X1.10 z.x z.yyy
Min. : 1.00 Min. : 1.00 Min. :11.00
1st Qu.: 3.25 1st Qu.: 3.25 1st Qu.:13.25
Median : 5.50 Median : 5.50 Median :15.50
Mean : 5.50 Mean : 5.50 Mean :15.50
3rd Qu.: 7.75 3rd Qu.: 7.75 3rd Qu.:17.75
Max. :10.00 Max. :10.00 Max. :20.00
> ## 1.6.2 had NULL labels on output with z columns stacked.
>
>
> ## re-orderings in terms.formula (PR#2206)
> form <- formula(y ~ a + b:c + d + e + e:d)
> (tt <- terms(form))
y ~ a + b:c + d + e + e:d
attr(,"variables")
list(y, a, b, c, d, e)
attr(,"factors")
a d e b:c d:e
y 0 0 0 0 0
a 1 0 0 0 0
b 0 0 0 2 0
c 0 0 0 2 0
d 0 1 0 0 1
e 0 0 1 0 1
attr(,"term.labels")
[1] "a" "d" "e" "b:c" "d:e"
attr(,"order")
[1] 1 1 1 2 2
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
> (tt2 <- terms(formula(tt)))
y ~ a + b:c + d + e + e:d
attr(,"variables")
list(y, a, b, c, d, e)
attr(,"factors")
a d e b:c d:e
y 0 0 0 0 0
a 1 0 0 0 0
b 0 0 0 2 0
c 0 0 0 2 0
d 0 1 0 0 1
e 0 0 1 0 1
attr(,"term.labels")
[1] "a" "d" "e" "b:c" "d:e"
attr(,"order")
[1] 1 1 1 2 2
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
> stopifnot(identical(tt, tt2))
> terms(delete.response(tt))
~a + b:c + d + e + e:d
attr(,"variables")
list(a, b, c, d, e)
attr(,"factors")
a d e b:c d:e
a 1 0 0 0 0
b 0 0 0 2 0
c 0 0 0 2 0
d 0 1 0 0 1
e 0 0 1 0 1
attr(,"term.labels")
[1] "a" "d" "e" "b:c" "d:e"
attr(,"order")
[1] 1 1 1 2 2
attr(,"intercept")
[1] 1
attr(,"response")
[1] 0
attr(,".Environment")
<environment: R_GlobalEnv>
> ## both tt and tt2 re-ordered the formula < 1.7.0
> ## now try with a dot
> data(warpbreaks)
> terms(breaks ~ ., data = warpbreaks)
breaks ~ wool + tension
attr(,"variables")
list(breaks, wool, tension)
attr(,"factors")
wool tension
breaks 0 0
wool 1 0
tension 0 1
attr(,"term.labels")
[1] "wool" "tension"
attr(,"order")
[1] 1 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
> terms(breaks ~ . - tension, data = warpbreaks)
breaks ~ (wool + tension) - tension
attr(,"variables")
list(breaks, wool, tension)
attr(,"factors")
wool
breaks 0
wool 1
tension 0
attr(,"term.labels")
[1] "wool"
attr(,"order")
[1] 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
> terms(breaks ~ . - tension, data = warpbreaks, simplify = TRUE)
breaks ~ wool
attr(,"variables")
list(breaks, wool, tension)
attr(,"factors")
wool
breaks 0
wool 1
tension 0
attr(,"term.labels")
[1] "wool"
attr(,"order")
[1] 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
> terms(breaks ~ . ^2, data = warpbreaks)
breaks ~ (wool + tension)^2
attr(,"variables")
list(breaks, wool, tension)
attr(,"factors")
wool tension wool:tension
breaks 0 0 0
wool 1 0 1
tension 0 1 1
attr(,"term.labels")
[1] "wool" "tension" "wool:tension"
attr(,"order")
[1] 1 1 2
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
> terms(breaks ~ . ^2, data = warpbreaks, simplify = TRUE)
breaks ~ wool + tension + wool:tension
attr(,"variables")
list(breaks, wool, tension)
attr(,"factors")
wool tension wool:tension
breaks 0 0 0
wool 1 0 1
tension 0 1 1
attr(,"term.labels")
[1] "wool" "tension" "wool:tension"
attr(,"order")
[1] 1 1 2
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: R_GlobalEnv>
> ## 1.6.2 expanded these formulae out as in simplify = TRUE
>
>
> ## printing attributes (PR#2506)
> (x <- structure(1:4, other=as.factor(LETTERS[1:3])))
[1] 1 2 3 4
attr(,"other")
[1] A B C
Levels: A B C
> ## < 1.7.0 printed the codes of the factor attribute
>
>
> ## add logical matrix replacement indexing for data frames
> TEMP <- data.frame(VAR1=c(1,2,3,4,5), VAR2=c(5,4,3,2,1), VAR3=c(1,1,1,1,NA))
> TEMP[,c(1,3)][TEMP[,c(1,3)]==1 & !is.na(TEMP[,c(1,3)])] < -10
1 <NA> <NA> <NA> <NA>
FALSE FALSE FALSE FALSE FALSE
> TEMP
VAR1 VAR2 VAR3
1 1 5 1
2 2 4 1
3 3 3 1
4 4 2 1
5 5 1 NA
> ##
>
> ## moved from reg-plot.R as exact output depends on rounding error
> ## PR 390 (axis for small ranges)
>
> relrange <- function(x) {
+ ## The relative range in EPS units
+ r <- range(x)
+ diff(r)/max(abs(r))/.Machine$double.eps
+ }
>
> x <- c(0.12345678912345678,
+ 0.12345678912345679,
+ 0.12345678912345676)
> relrange(x) ## 1.0125
[1] 1.0125
> plot(x) # `extra horizontal' ; +- ok on Solaris; label off on Linux
>
> y <- c(0.9999563255363383973418,
+ 0.9999563255363389524533,
+ 0.9999563255363382863194)
> ## The relative range number:
> relrange(y) ## 3.000131
[1] 3.000131
> plot(y)# once gave infinite loop on Solaris [TL]; y-axis too long
>
> ## Comments: The whole issue was finally deferred to main/graphics.c l.1944
> ## error("relative range of values is too small to compute accurately");
> ## which is not okay.
>
> set.seed(101)
> par(mfrow = c(3,3))
> for(j.fac in 1e-12* c(10, 1, .7, .3, .2, .1, .05, .03, .01)) {
+ ## ====
+ #set.seed(101) # or don't
+ x <- pi + jitter(numeric(101), f = j.fac)
+ rrtxt <- paste("rel.range =", formatC(relrange(x), dig = 4),"* EPS")
+ cat("j.f = ", format(j.fac)," ; ", rrtxt,"\n",sep="")
+ plot(x, type = "l", main = rrtxt)
+ cat("par(\"usr\")[3:4]:", formatC(par("usr")[3:4], wid = 10),"\n",
+ "par(\"yaxp\") : ", formatC(par("yaxp"), wid = 10),"\n\n", sep="")
+ }
j.f = 1e-11 ; rel.range = 553.9 * EPS
par("usr")[3:4]: 3.142 3.142
par("yaxp") : 3.142 3.142 3
j.f = 1e-12 ; rel.range = 56.02 * EPS
par("usr")[3:4]: 3.142 3.142
par("yaxp") : 3.142 3.142 1
j.f = 7e-13 ; rel.range = 39.47 * EPS
par("usr")[3:4]: 3.142 3.142
par("yaxp") : 3.142 3.142 1
j.f = 3e-13 ; rel.range = 16.55 * EPS
par("usr")[3:4]: 3.142 3.142
par("yaxp") : 3.142 3.142 1
j.f = 2e-13 ; rel.range = 11.46 * EPS
par("usr")[3:4]: 3.108 3.176
par("yaxp") : 3.11 3.17 6
j.f = 1e-13 ; rel.range = 5.093 * EPS
par("usr")[3:4]: 3.108 3.176
par("yaxp") : 3.11 3.17 6
j.f = 5e-14 ; rel.range = 2.546 * EPS
par("usr")[3:4]: 3.108 3.176
par("yaxp") : 3.11 3.17 6
j.f = 3e-14 ; rel.range = 1.273 * EPS
par("usr")[3:4]: 3.108 3.176
par("yaxp") : 3.11 3.17 6
j.f = 1e-14 ; rel.range = 0 * EPS
par("usr")[3:4]: 1.784 4.499
par("yaxp") : 2 4 4
Warning messages:
1: relative range of values = 43 * EPS, is small (axis 2).
2: relative range of values = 36 * EPS, is small (axis 2).
3: relative range of values = 17 * EPS, is small (axis 2).
> par(mfrow = c(1,1))
> ## The warnings from inside GScale() will differ in their relrange() ...
> ## >> do sloppy testing
> ## 2003-02-03 hopefully no more. BDR
> ## end of PR 390
>
>
> ## print/show dispatch
> hasMethods <- .isMethodsDispatchOn()
> require(methods)
[1] TRUE
> setClass("bar", representation(a="numeric"))
[1] "bar"
> foo <- new("bar", a=pi)
> foo
An object of class "bar"
Slot "a":
[1] 3.141593
> show(foo)
An object of class "bar"
Slot "a":
[1] 3.141593
> print(foo)
An object of class "bar"
Slot "a":
[1] 3.141593
>
> setMethod("show", "bar", function(object){cat("show method\n")})
[1] "show"
> show(foo)
show method
> foo
show method
> print(foo)
show method
> print(foo, digits = 4)
An object of class "bar"
Slot "a":
[1] 3.142
>
> print.bar <- function(x, ...) cat("print method\n")
> foo
print method
> print(foo)
print method
> show(foo)
show method
>
> setMethod("print", "bar", function(x, ...){cat("S4 print method\n")})
Creating a new generic function for "print" in ".GlobalEnv"
[1] "print"
> foo
S4 print method
> print(foo)
S4 print method
> show(foo)
show method
> print(foo, digits = 4)
S4 print method
> if(!hasMethods) detach("package:methods")
> ##
>
>
> ## scoping rules calling step inside a function
> "cement" <-
+ structure(list(x1 = c(7, 1, 11, 11, 7, 11, 3, 1, 2, 21, 1, 11, 10),
+ x2 = c(26, 29, 56, 31, 52, 55, 71, 31, 54, 47, 40, 66, 68),
+ x3 = c(6, 15, 8, 8, 6, 9, 17, 22, 18, 4, 23, 9, 8),
+ x4 = c(60, 52, 20, 47, 33, 22, 6, 44, 22, 26, 34, 12, 12),
+ y = c(78.5, 74.3, 104.3, 87.6, 95.9, 109.2, 102.7, 72.5,
+ 93.1, 115.9, 83.8, 113.3, 109.4)),
+ .Names = c("x1", "x2", "x3", "x4", "y"), class = "data.frame",
+ row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9",
+ "10", "11", "12", "13"))
> teststep <- function(formula, data)
+ {
+ d2 <- data
+ fit <- lm(formula, data=d2)
+ step(fit)
+ }
> teststep(formula(y ~ .), cement)
Start: AIC= 26.94
y ~ x1 + x2 + x3 + x4
Df Sum of Sq RSS AIC
- x3 1 0.109 47.973 24.974
- x4 1 0.247 48.111 25.011
- x2 1 2.972 50.836 25.728
<none> 47.864 26.944
- x1 1 25.951 73.815 30.576
Step: AIC= 24.97
y ~ x1 + x2 + x4
Df Sum of Sq RSS AIC
<none> 47.97 24.97
- x4 1 9.93 57.90 25.42
- x2 1 26.79 74.76 28.74
- x1 1 820.91 868.88 60.63
Call:
lm(formula = y ~ x1 + x2 + x4, data = d2)
Coefficients:
(Intercept) x1 x2 x4
71.6483 1.4519 0.4161 -0.2365
> ## failed in 1.6.2
>
> str(array(1))# not a scalar
num [, 1] 1
>
>
> ## na.print="" shouldn't apply to (dim)names!
> (tf <- table(ff <- factor(c(1:2,NA,2), exclude=NULL)))
1 2 <NA>
1 2 1
> identical(levels(ff), dimnames(tf)[[1]])
[1] TRUE
> str(levels(ff))
chr [1:3] "1" "2" NA
> ## not quite ok previous to 1.7.0
>
>
> ## PR#3058 printing with na.print and right=TRUE
> a <- matrix( c(NA, "a", "b", "10",
+ NA, NA, "d", "12",
+ NA, NA, NA, "14"),
+ byrow=T, ncol=4 )
> print(a, right=TRUE, na.print=" ")
[,1] [,2] [,3] [,4]
[1,] "a" "b" "10"
[2,] "d" "12"
[3,] "14"
> print(a, right=TRUE, na.print="----")
[,1] [,2] [,3] [,4]
[1,] ---- "a" "b" "10"
[2,] ---- ---- "d" "12"
[3,] ---- ---- ---- "14"
> ## misaligned in 1.7.0
>
>
> ## assigning factors to dimnames
> A <- matrix(1:4, 2)
> aa <- factor(letters[1:2])
> dimnames(A) <- list(aa, NULL)
> A
[,1] [,2]
a 1 3
b 2 4
> dimnames(A)
[[1]]
[1] "a" "b"
[[2]]
NULL
> ## 1.7.0 gave internal codes as display and dimnames()
> ## 1.7.1beta gave NAs via dimnames()
> ## 1.8.0 converts factors to character
>
>
> ## wishlist PR#2776: aliased coefs in lm/glm
> set.seed(123)
> x2 <- x1 <- 1:10
> x3 <- 0.1*(1:10)^2
> y <- x1 + rnorm(10)
> (fit <- lm(y ~ x1 + x2 + x3))
Call:
lm(formula = y ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
1.4719 0.5867 NA 0.2587
> summary(fit, cor = TRUE)
Call:
lm(formula = y ~ x1 + x2 + x3)
Residuals:
Min 1Q Median 3Q Max
-1.0572 -0.4836 0.0799 0.4424 1.2699
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4719 0.9484 1.552 0.165
x1 0.5867 0.3961 1.481 0.182
x2 NA NA NA NA
x3 0.2587 0.3509 0.737 0.485
Residual standard error: 0.8063 on 7 degrees of freedom
Multiple R-Squared: 0.9326, Adjusted R-squared: 0.9134
F-statistic: 48.43 on 2 and 7 DF, p-value: 7.946e-05
Correlation of Coefficients:
(Intercept) x1
x1 -0.91
x3 0.81 -0.97
> (fit <- glm(y ~ x1 + x2 + x3))
Call: glm(formula = y ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
1.4719 0.5867 NA 0.2587
Degrees of Freedom: 9 Total (i.e. Null); 7 Residual
Null Deviance: 67.53
Residual Deviance: 4.551 AIC: 28.51
> summary(fit, cor = TRUE)
Call:
glm(formula = y ~ x1 + x2 + x3)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.0572 -0.4836 0.0799 0.4424 1.2699
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4719 0.9484 1.552 0.165
x1 0.5867 0.3961 1.481 0.182
x2 NA NA NA NA
x3 0.2587 0.3509 0.737 0.485
(Dispersion parameter for gaussian family taken to be 0.6501753)
Null deviance: 67.5316 on 9 degrees of freedom
Residual deviance: 4.5512 on 7 degrees of freedom
AIC: 28.507
Number of Fisher Scoring iterations: 2
Correlation of Coefficients:
(Intercept) x1
x1 -0.91
x3 0.81 -0.97
> ## omitted silently in summary.glm < 1.8.0
>
>
> ## list-like indexing of data frames with drop specified
> data(women)
> women["height"]
height
1 58
2 59
3 60
4 61
5 62
6 63
7 64
8 65
9 66
10 67
11 68
12 69
13 70
14 71
15 72
> women["height", drop = FALSE] # same with a warning
height
1 58
2 59
3 60
4 61
5 62
6 63
7 64
8 65
9 66
10 67
11 68
12 69
13 70
14 71
15 72
Warning message:
drop argument will be ignored in: "[.data.frame"(women, "height", drop = FALSE)
> women["height", drop = TRUE] # ditto
height
1 58
2 59
3 60
4 61
5 62
6 63
7 64
8 65
9 66
10 67
11 68
12 69
13 70
14 71
15 72
Warning message:
drop argument will be ignored in: "[.data.frame"(women, "height", drop = TRUE)
> women[,"height", drop = FALSE] # no warning
height
1 58
2 59
3 60
4 61
5 62
6 63
7 64
8 65
9 66
10 67
11 68
12 69
13 70
14 71
15 72
> women[,"height", drop = TRUE] # a vector
[1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
> ## second and third were interpreted as women["height", , drop] in 1.7.x
>
>
> ## make.names
> make.names("")
[1] "X"
> make.names(".aa")
[1] ".aa"
> ## was "X.aa" in 1.7.1
> make.names(".2")
[1] "X.2"
> make.names(".2a") # not valid in R
[1] "X.2a"
> make.names(as.character(NA))
[1] "NA."
> ##
>
>
> ## strange names in data frames
> as.data.frame(list(row.names=17)) # 0 rows in 1.7.1
row.names
1 17
> aa <- data.frame(aa=1:3)
> aa[["row.names"]] <- 4:6
> aa # fine in 1.7.1
aa row.names
1 1 4
2 2 5
3 3 6
> A <- matrix(4:9, 3, 2)
> colnames(A) <- letters[1:2]
> aa[["row.names"]] <- A
> aa
aa row.names.a row.names.b
1 1 4 7
2 2 5 8
3 3 6 9
> ## wrong printed names in 1.7.1
>
> ## assigning to NULL
> a <- NULL
> a[["a"]] <- 1
> a
a
1
> a <- NULL
> a[["a"]] <- "something"
> a
a
"something"
> a <- NULL
> a[["a"]] <- 1:3
> a
$a
[1] 1 2 3
> ## Last was an error in 1.7.1
>
>
> ## examples of 0-rank models, some empty, some rank-deficient
> y <- rnorm(10)
> x <- rep(0, 10)
> (fit <- lm(y ~ 0))
Call:
lm(formula = y ~ 0)
No coefficients
> summary(fit)
Call:
lm(formula = y ~ 0)
Residuals:
Min 1Q Median 3Q Max
-1.369192 -0.210726 0.008405 0.084366 0.552922
No Coefficients
Residual standard error: 0.5235 on 10 degrees of freedom
> anova(fit)
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 10 2.74036 0.27404
> predict(fit)
1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 0 0 0 0 0
> predict(fit, data.frame(x=x), se=TRUE)
$fit
[1] 0 0 0 0 0 0 0 0 0 0
$se.fit
[1] 0 0 0 0 0 0 0 0 0 0
$df
[1] 10
$residual.scale
[1] 0.5234843
> predict(fit, type="terms", se=TRUE)
$fit
[1,]
[2,]
[3,]
[4,]
[5,]
[6,]
[7,]
[8,]
[9,]
[10,]
attr(,"constant")
[1] 0
$se.fit
[1,]
[2,]
[3,]
[4,]
[5,]
[6,]
[7,]
[8,]
[9,]
[10,]
$df
[1] 10
$residual.scale
[1] 0.5234843
> variable.names(fit) #should be empty
character(0)
> model.matrix(fit)
1
2
3
4
5
6
7
8
9
10
attr(,"assign")
numeric(0)
>
> (fit <- lm(y ~ x + 0))
Call:
lm(formula = y ~ x + 0)
Coefficients:
x
NA
> summary(fit)
Call:
lm(formula = y ~ x + 0)
Residuals:
Min 1Q Median 3Q Max
-1.369192 -0.210726 0.008405 0.084366 0.552922
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
x NA NA NA NA
Residual standard error: 0.5235 on 10 degrees of freedom
> anova(fit)
Analysis of Variance Table
Response: y
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 10 2.74036 0.27404
> predict(fit)
1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 0 0 0 0 0
> predict(fit, data.frame(x=x), se=TRUE)
$fit
1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 0 0 0 0 0
$se.fit
[1] 0 0 0 0 0 0 0 0 0 0
$df
[1] 10
$residual.scale
[1] 0.5234843
Warning message:
prediction from a rank-deficient fit may be misleading in: predict.lm(fit, data.frame(x = x), se = TRUE)
> predict(fit, type="terms", se=TRUE)
$fit
x
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
attr(,"constant")
[1] 0
$se.fit
x
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
$df
[1] 10
$residual.scale
[1] 0.5234843
> variable.names(fit) #should be empty
character(0)
> model.matrix(fit)
x
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
attr(,"assign")
[1] 1
>
> (fit <- glm(y ~ 0))
Call: glm(formula = y ~ 0)
No coefficients
Degrees of Freedom: 10 Total (i.e. Null); 10 Residual
Null Deviance: 2.74
Residual Deviance: 2.74 AIC: 17.43
> summary(fit)
Call:
glm(formula = y ~ 0)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.369192 -0.210726 0.008405 0.084366 0.552922
No Coefficients
(Dispersion parameter for gaussian family taken to be 0.2740358)
Null deviance: 2.7404 on 10 degrees of freedom
Residual deviance: 2.7404 on 10 degrees of freedom
AIC: 17.434
Number of Fisher Scoring iterations: 0
> anova(fit)
Analysis of Deviance Table
Model: gaussian, link: identity
Response: y
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL 10 2.7404
> predict(fit)
1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 0 0 0 0 0
> predict(fit, data.frame(x=x), se=TRUE)
$fit
[1] 0 0 0 0 0 0 0 0 0 0
$se.fit
[1] 0 0 0 0 0 0 0 0 0 0
$residual.scale
[1] 0.5234843
> predict(fit, type="terms", se=TRUE)
$fit
[1,]
[2,]
[3,]
[4,]
[5,]
[6,]
[7,]
[8,]
[9,]
[10,]
attr(,"constant")
[1] 0
$se.fit
[1,]
[2,]
[3,]
[4,]
[5,]
[6,]
[7,]
[8,]
[9,]
[10,]
$residual.scale
[1] 0.5234843
>
> (fit <- glm(y ~ x + 0))
Call: glm(formula = y ~ x + 0)
Coefficients:
x
NA
Degrees of Freedom: 10 Total (i.e. Null); 10 Residual
Null Deviance: 2.74
Residual Deviance: 2.74 AIC: 17.43
> summary(fit)
Call:
glm(formula = y ~ x + 0)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.369192 -0.210726 0.008405 0.084366 0.552922
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
x NA NA NA NA
(Dispersion parameter for gaussian family taken to be 0.2740358)
Null deviance: 2.7404 on 10 degrees of freedom
Residual deviance: 2.7404 on 10 degrees of freedom
AIC: 17.434
Number of Fisher Scoring iterations: 2
> anova(fit)
Analysis of Deviance Table
Model: gaussian, link: identity
Response: y
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL 10 2.7404
x 0 0.0000 10 2.7404
> predict(fit)
1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 0 0 0 0 0
> predict(fit, data.frame(x=x), se=TRUE)
$fit
1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 0 0 0 0 0
$se.fit
[1] 0 0 0 0 0 0 0 0 0 0
$residual.scale
[1] 0.5234843
Warning message:
prediction from a rank-deficient fit may be misleading in: predict.lm(object, newdata, se.fit, scale = residual.scale, type = ifelse(type ==
> predict(fit, type="terms", se=TRUE)
$fit
x
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
attr(,"constant")
[1] 0
$se.fit
x
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
$residual.scale
[1] 0.5234843
> ## Lots of problems in 1.7.x
>
>
> ## lm.influence on deficient lm models
> dat <- data.frame(y=rnorm(10), x1=1:10, x2=1:10, x3 = 0, wt=c(0,rep(1, 9)),
+ row.names=letters[1:10])
> dat[3, 1] <- dat[4, 2] <- NA
> lm.influence(lm(y ~ x1 + x2, data=dat, weights=wt, na.action=na.omit))
$hat
b e f g h i j
0.6546053 0.2105263 0.1546053 0.1447368 0.1809211 0.2631579 0.3914474
$coefficients
(Intercept) x1
b 1.39138784 -0.173267165
e -0.70930972 0.068642877
f 0.12039809 -0.007818058
g 0.01971595 0.001314397
h 0.03272637 -0.017325726
i -0.36929526 0.092323814
j 0.33861311 -0.070163076
$sigma
b e f g h i j
0.9641441 0.7434598 1.0496727 1.0681908 1.0389586 0.7633748 1.0093187
$wt.res
b e f g h i j
0.5513046 -1.3728575 0.4018482 0.1708716 -0.4793451 1.2925334 -0.5643552
> lm.influence(lm(y ~ x1 + x2, data=dat, weights=wt, na.action=na.exclude))
$hat
b e c d f g h i
0.6546053 0.2105263 0.0000000 0.0000000 0.1546053 0.1447368 0.1809211 0.2631579
j
0.3914474
$coefficients
(Intercept) x1
b 1.39138784 -0.173267165
e -0.70930972 0.068642877
c 0.00000000 0.000000000
d 0.00000000 0.000000000
f 0.12039809 -0.007818058
g 0.01971595 0.001314397
h 0.03272637 -0.017325726
i -0.36929526 0.092323814
j 0.33861311 -0.070163076
$sigma
b e c d f g h i
0.9641441 0.7434598 0.9589854 0.9589854 1.0496727 1.0681908 1.0389586 0.7633748
j
1.0093187
$wt.res
b e c d f g h
0.5513046 -1.3728575 NA NA 0.4018482 0.1708716 -0.4793451
i j
1.2925334 -0.5643552
> lm.influence(lm(y ~ 0, data=dat, weights=wt, na.action=na.omit))
$hat
b d e f g h i j
0 0 0 0 0 0 0 0
$coefficients
b
d
e
f
g
h
i
j
$sigma
b d e f g h i j
0.8830622 0.8830622 0.8830622 0.8830622 0.8830622 0.8830622 0.8830622 0.8830622
$wt.res
b d e f g h i
0.3604547 0.1146812 -1.1426753 0.7723744 0.6817419 0.1718693 2.0840918
j
0.3675473
> lm.influence(lm(y ~ 0, data=dat, weights=wt, na.action=na.exclude))
$hat
b d c e f g h i j
0 0 0 0 0 0 0 0 0
$coefficients
b
d
c
e
f
g
h
i
j
$sigma
b d c e f g h i
0.8830622 0.8830622 0.8830622 0.8830622 0.8830622 0.8830622 0.8830622 0.8830622
j
0.8830622
$wt.res
b d c e f g h
0.3604547 0.1146812 NA -1.1426753 0.7723744 0.6817419 0.1718693
i j
2.0840918 0.3675473
> lm.influence(lm(y ~ 0 + x3, data=dat, weights=wt, na.action=na.omit))
$hat
b d e f g h i j
0 0 0 0 0 0 0 0
$coefficients
b
d
e
f
g
h
i
j
$sigma
b d e f g h i j
0.9366289 0.9366289 0.9366289 0.9366289 0.9366289 0.9366289 0.9366289 0.9366289
$wt.res
b d e f g h i
0.3604547 0.1146812 -1.1426753 0.7723744 0.6817419 0.1718693 2.0840918
j
0.3675473
> lm.influence(lm(y ~ 0 + x3, data=dat, weights=wt, na.action=na.exclude))
$hat
b d c e f g h i j
0 0 0 0 0 0 0 0 0
$coefficients
b
d
c
e
f
g
h
i
j
$sigma
b d c e f g h i
0.9366289 0.9366289 0.9366289 0.9366289 0.9366289 0.9366289 0.9366289 0.9366289
j
0.9366289
$wt.res
b d c e f g h
0.3604547 0.1146812 NA -1.1426753 0.7723744 0.6817419 0.1718693
i j
2.0840918 0.3675473
> lm.influence(lm(y ~ 0, data=dat, na.action=na.exclude))
$hat
a b c d e f g h i j
0 0 0 0 0 0 0 0 0 0
$coefficients
a
b
c
d
e
f
g
h
i
j
$sigma
a b c d e f g h
0.8860916 0.8860916 0.8860916 0.8860916 0.8860916 0.8860916 0.8860916 0.8860916
i j
0.8860916 0.8860916
$wt.res
a b c d e f g
0.2196280 0.3604547 NA 0.1146812 -1.1426753 0.7723744 0.6817419
h i j
0.1718693 2.0840918 0.3675473
> ## last three misbehaved in 1.7.x, none had proper names.
>
>
> ## length of results in ARMAacf when lag.max is used
> ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=1) # was 4 in 1.7.1
0 1
1.0000000 0.7644046
> ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=2)
0 1 2
1.0000000 0.7644046 0.2676056
> ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=3)
0 1 2 3
1.0000000 0.7644046 0.2676056 -0.2343150
> ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=4)
0 1 2 3 4
1.0000000 0.7644046 0.2676056 -0.2343150 -0.5180538
> ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=5) # failed in 1.7.1
0 1 2 3 4 5
1.0000000 0.7644046 0.2676056 -0.2343150 -0.5180538 -0.5099616
> ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=6)
0 1 2 3 4 5 6
1.0000000 0.7644046 0.2676056 -0.2343150 -0.5180538 -0.5099616 -0.2784942
> ARMAacf(ar=c(1.3,-0.6, -0.2, 0.1),lag.max=10)
0 1 2 3 4 5 6
1.0000000 0.7644046 0.2676056 -0.2343150 -0.5180538 -0.5099616 -0.2784942
7 8 9 10
0.0241137 0.2486313 0.3134551 0.2256408
> ##
>
>
> ## Indexing non-existent columns in a data frame
> x <- data.frame(a = 1, b = 2)
> try(x[c("a", "c")])
Error in "[.data.frame"(x, c("a", "c")) : undefined columns selected
> try(x[, c("a", "c")])
Error in "[.data.frame"(x, , c("a", "c")) :
undefined columns selected
> try(x[1, c("a", "c")])
Error in "[.data.frame"(x, 1, c("a", "c")) :
undefined columns selected
> ## Second succeeded, third gave uniformative error message in 1.7.x.
>
>
> ## methods(class = ) with namespaces, .Primitives etc (many missing in 1.7.x):
> meth2gen <- function(cl)
+ noquote(sub(paste("\\.",cl,"$",sep=""),"", methods(class = cl)))
> meth2gen("data.frame")
[1] $<- [ [<- [[ [[<-
[6] aggregate as.data.frame as.list as.matrix by
[11] dim dimnames dimnames<- duplicated edit
[16] format formula is.na mean merge
[21] na.exclude na.omit plot print prompt
[26] row.names row.names<- rowsum split split<-
[31] stack str subset summary t
[36] transform unique unstack
> meth2gen("dendrogram")
[1] [[ cut plot print reorder rev str
> ## --> the output may need somewhat frequent updating..
>
>
> ## subsetting a 1D array lost the dimensions
> x <- array(1:5, dim=c(5))
> dim(x)
[1] 5
> dim(x[, drop=TRUE])
[1] 5
> dim(x[2:3])
[1] 2
> dim(x[2])
NULL
> dim(x[2, drop=FALSE])
[1] 1
> dimnames(x) <- list(some=letters[1:5])
> x[]
[1] 1 2 3 4 5
> x[2:3]
some
b c
2 3
> x[2]
b
2
> x[2, drop=FALSE]
some
b
2
> ## both dim and dimnames lost in 1.8.0
>
>
> ## print.dist() didn't show NA's prior to 1.8.1
> x <- cbind(c(1,NA,2,3), c(NA,2,NA,1))
> (d <- dist(x))
1 2 3
2 NA
3 1.414214 NA
4 2.828427 1.414214 1.414214
> print(d, diag = TRUE)
1 2 3 4
1 0.000000
2 NA 0.000000
3 1.414214 NA 0.000000
4 2.828427 1.414214 1.414214 0.000000
> ##
>
>
> ## offsets in model terms where sometimes not deleted correctly
> attributes(terms(~ a + b + a:b + offset(c)))[c("offset", "term.labels")]
$offset
[1] 3
$term.labels
[1] "a" "b" "a:b"
> attributes(terms(y ~ a + b + a:b + offset(c)))[c("offset", "term.labels")]
$offset
[1] 4
$term.labels
[1] "a" "b" "a:b"
> attributes(terms(~ offset(c) + a + b + a:b))[c("offset", "term.labels")]
$offset
[1] 1
$term.labels
[1] "a" "b" "a:b"
> attributes(terms(y ~ offset(c) + a + b + a:b))[c("offset", "term.labels")]
$offset
[1] 2
$term.labels
[1] "a" "b" "a:b"
> ## errors prior to 1.8.1
>
>
> ## 0-level factors gave nonsensical answers in model.matrix
> m <- model.frame(~x, data.frame(x=NA), na.action=na.pass)
> model.matrix(~x, m)
(Intercept) xTRUE
1 1 NA
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$x
[1] "contr.treatment"
> lm.fit <- lm(y ~ x, data.frame(x=1:10, y=1:10))
> try(predict(lm.fit, data.frame(x=NA)))
Error: variable 'x' was fitted with numeric but logical was supplied
> ## wrong answers in 1.8.0, refused to run in 1.8.1
>
>