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Version 1.2.2 Patched (2001-04-09)
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> ### 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
<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'
>
> ## 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
>
>
> ## 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
>