Last updated on 2026-07-23 00:53:41 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.0-12 | 1.95 | 41.97 | 43.92 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 2.0-12 | 1.47 | 30.56 | 32.03 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 2.0-12 | 66.50 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0-12 | 29.96 | NOTE | |||
| r-devel-windows-x86_64 | 2.0-12 | 4.00 | 64.00 | 68.00 | NOTE | |
| r-patched-linux-x86_64 | 2.0-12 | 2.32 | 33.94 | 36.26 | OK | |
| r-release-linux-x86_64 | 2.0-12 | 1.85 | 33.72 | 35.57 | OK | |
| r-release-macos-arm64 | 2.0-12 | 1.00 | 12.00 | 13.00 | OK | |
| r-release-macos-x86_64 | 2.0-12 | 2.00 | 38.00 | 40.00 | OK | |
| r-release-windows-x86_64 | 2.0-12 | 3.00 | 63.00 | 66.00 | OK | |
| r-oldrel-macos-arm64 | 2.0-12 | NOTE | ||||
| r-oldrel-macos-x86_64 | 2.0-12 | 2.00 | 43.00 | 45.00 | NOTE | |
| r-oldrel-windows-x86_64 | 2.0-12 | 4.00 | 69.00 | 73.00 | NOTE |
Version: 2.0-12
Check: tests
Result: NOTE
Running ‘allexercises.R’ [5s/6s]
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...
180c180
< V = 13858, p-value = 4.225e-14
---
> V = 9283.5, p-value = 2.075e-13
471c471
< V = 0, p-value = 0.0452
---
> V = 0, p-value = 0.05906
Running ‘allscripts.R’ [4s/6s]
Comparing ‘allscripts.Rout’ to ‘allscripts.Rout.save’ ... OK
Flavor: r-devel-linux-x86_64-debian-clang
Version: 2.0-12
Check: tests
Result: ERROR
Running ‘allexercises.R’ [4s/5s]
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...
180c180
< V = 13858, p-value = 4.225e-14
---
> V = 9283.5, p-value = 2.075e-13
471c471
< V = 0, p-value = 0.0452
---
> V = 0, p-value = 0.05906
Running ‘allscripts.R’ [0s/1s]
Running the tests in ‘tests/allscripts.R’ failed.
Complete output:
> library(ISwR)
> .make.epsf <- Sys.getenv("EPSF")=="y"
> ps.options(height=3.5, width=4.4, pointsize=8, horiz=F)
> if (.make.epsf) X11(height=3.5,width=4.4,pointsize=8) else postscript()
> dev.copy2eps <- function(...) invisible(grDevices::dev.copy2eps(...))
> par(mar=c(4,4,3,2)+.1)
> options(width=66, useFancyQuotes="TeX")
> suppressWarnings(RNGversion("1.5.1")) #Yes, Kinderman-Ramage was buggy...
> set.seed(310367)
> #Rprof(interval=.001)
> plot(rnorm(500))
> 2 + 2
[1] 4
> exp(-2)
[1] 0.1353353
> rnorm(15)
[1] -0.18326112 -0.59753287 -0.67017905 0.16075723 1.28199575
[6] 0.07976977 0.13683303 0.77155246 0.85986694 -1.01506772
[11] -0.49448567 0.52433026 1.07732656 1.09748097 -1.09318582
> x <- 2
> x
[1] 2
> x + x
[1] 4
> weight <- c(60, 72, 57, 90, 95, 72)
> weight
[1] 60 72 57 90 95 72
> height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)
> bmi <- weight/height^2
> bmi
[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630
> sum(weight)
[1] 446
> sum(weight)/length(weight)
[1] 74.33333
> xbar <- sum(weight)/length(weight)
> weight - xbar
[1] -14.333333 -2.333333 -17.333333 15.666667 20.666667
[6] -2.333333
> (weight - xbar)^2
[1] 205.444444 5.444444 300.444444 245.444444 427.111111
[6] 5.444444
> sum((weight - xbar)^2)
[1] 1189.333
> sqrt(sum((weight - xbar)^2)/(length(weight) - 1))
[1] 15.42293
> mean(weight)
[1] 74.33333
> sd(weight)
[1] 15.42293
> t.test(bmi, mu=22.5)
One Sample t-test
data: bmi
t = 0.34488, df = 5, p-value = 0.7442
alternative hypothesis: true mean is not equal to 22.5
95 percent confidence interval:
18.41734 27.84791
sample estimates:
mean of x
23.13262
> plot(height,weight)
> if (.make.epsf) dev.copy2eps(file="h-w.ps")
> plot(height, weight, pch=2)
> if (.make.epsf) dev.copy2eps(file="h-w-triangle.ps")
> hh <- c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90)
> lines(hh, 22.5 * hh^2)
> if (.make.epsf) dev.copy2eps(file="h-w-line.ps")
> # args(plot.default) # disabled because unimportant and changed btw 4.5 & 4.6
> c("Huey","Dewey","Louie")
[1] "Huey" "Dewey" "Louie"
> c('Huey','Dewey','Louie')
[1] "Huey" "Dewey" "Louie"
> c(T,T,F,T)
[1] TRUE TRUE FALSE TRUE
> bmi > 25
[1] FALSE FALSE FALSE FALSE TRUE FALSE
> cat(c("Huey","Dewey","Louie"))
Huey Dewey Louie> cat("Huey","Dewey","Louie", "\n")
Huey Dewey Louie
> cat("What is \"R\"?\n")
What is "R"?
> c(42,57,12,39,1,3,4)
[1] 42 57 12 39 1 3 4
> x <- c(1, 2, 3)
> y <- c(10, 20)
> c(x, y, 5)
[1] 1 2 3 10 20 5
> x <- c(red="Huey", blue="Dewey", green="Louie")
> x
red blue green
"Huey" "Dewey" "Louie"
> names(x)
[1] "red" "blue" "green"
> c(FALSE, 3)
[1] 0 3
> c(pi, "abc")
[1] "3.14159265358979" "abc"
> c(FALSE, "abc")
[1] "FALSE" "abc"
> seq(4,9)
[1] 4 5 6 7 8 9
> seq(4,10,2)
[1] 4 6 8 10
> 4:9
[1] 4 5 6 7 8 9
> oops <- c(7,9,13)
> rep(oops,3)
[1] 7 9 13 7 9 13 7 9 13
> rep(oops,1:3)
[1] 7 9 9 13 13 13
> rep(1:2,c(10,15))
[1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
> x <- 1:12
> dim(x) <- c(3,4)
> x
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
> matrix(1:12,nrow=3,byrow=T)
[,1] [,2] [,3] [,4]
[1,] 1 2 3 4
[2,] 5 6 7 8
[3,] 9 10 11 12
> x <- matrix(1:12,nrow=3,byrow=T)
> rownames(x) <- LETTERS[1:3]
> x
[,1] [,2] [,3] [,4]
A 1 2 3 4
B 5 6 7 8
C 9 10 11 12
> t(x)
A B C
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12
> cbind(A=1:4,B=5:8,C=9:12)
A B C
[1,] 1 5 9
[2,] 2 6 10
[3,] 3 7 11
[4,] 4 8 12
> rbind(A=1:4,B=5:8,C=9:12)
[,1] [,2] [,3] [,4]
A 1 2 3 4
B 5 6 7 8
C 9 10 11 12
> pain <- c(0,3,2,2,1)
> fpain <- factor(pain,levels=0:3)
> levels(fpain) <- c("none","mild","medium","severe")
> fpain
[1] none severe medium medium mild
Levels: none mild medium severe
> as.numeric(fpain)
[1] 1 4 3 3 2
> levels(fpain)
[1] "none" "mild" "medium" "severe"
> intake.pre <- c(5260,5470,5640,6180,6390,
+ 6515,6805,7515,7515,8230,8770)
> intake.post <- c(3910,4220,3885,5160,5645,
+ 4680,5265,5975,6790,6900,7335)
> mylist <- list(before=intake.pre,after=intake.post)
> mylist
$before
[1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770
$after
[1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335
> mylist$before
[1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770
> d <- data.frame(intake.pre,intake.post)
> d
intake.pre intake.post
1 5260 3910
2 5470 4220
3 5640 3885
4 6180 5160
5 6390 5645
6 6515 4680
7 6805 5265
8 7515 5975
9 7515 6790
10 8230 6900
11 8770 7335
> d$intake.pre
[1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770
> intake.pre[5]
[1] 6390
> intake.pre[c(3,5,7)]
[1] 5640 6390 6805
> v <- c(3,5,7)
> intake.pre[v]
[1] 5640 6390 6805
> intake.pre[1:5]
[1] 5260 5470 5640 6180 6390
> intake.pre[-c(3,5,7)]
[1] 5260 5470 6180 6515 7515 7515 8230 8770
> intake.post[intake.pre > 7000]
[1] 5975 6790 6900 7335
> intake.post[intake.pre > 7000 & intake.pre <= 8000]
[1] 5975 6790
> intake.pre > 7000 & intake.pre <= 8000
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
[11] FALSE
> d <- data.frame(intake.pre,intake.post)
> d[5,1]
[1] 6390
> d[5,]
intake.pre intake.post
5 6390 5645
> d[d$intake.pre>7000,]
intake.pre intake.post
8 7515 5975
9 7515 6790
10 8230 6900
11 8770 7335
> sel <- d$intake.pre>7000
> sel
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
[11] TRUE
> d[sel,]
intake.pre intake.post
8 7515 5975
9 7515 6790
10 8230 6900
11 8770 7335
> d[1:2,]
intake.pre intake.post
1 5260 3910
2 5470 4220
> head(d)
intake.pre intake.post
1 5260 3910
2 5470 4220
3 5640 3885
4 6180 5160
5 6390 5645
6 6515 4680
> energy
expend stature
1 9.21 obese
2 7.53 lean
3 7.48 lean
4 8.08 lean
5 8.09 lean
6 10.15 lean
7 8.40 lean
8 10.88 lean
9 6.13 lean
10 7.90 lean
11 11.51 obese
12 12.79 obese
13 7.05 lean
14 11.85 obese
15 9.97 obese
16 7.48 lean
17 8.79 obese
18 9.69 obese
19 9.68 obese
20 7.58 lean
21 9.19 obese
22 8.11 lean
> exp.lean <- energy$expend[energy$stature=="lean"]
> exp.obese <- energy$expend[energy$stature=="obese"]
> l <- split(energy$expend, energy$stature)
> l
$lean
[1] 7.53 7.48 8.08 8.09 10.15 8.40 10.88 6.13 7.90 7.05
[11] 7.48 7.58 8.11
$obese
[1] 9.21 11.51 12.79 11.85 9.97 8.79 9.69 9.68 9.19
> lapply(thuesen, mean, na.rm=T)
$blood.glucose
[1] 10.3
$short.velocity
[1] 1.325652
> sapply(thuesen, mean, na.rm=T)
blood.glucose short.velocity
10.300000 1.325652
> replicate(10,mean(rexp(20)))
[1] 1.0677019 1.2166898 0.8923216 1.1281207 0.9636017 0.8406877
[7] 1.3357814 0.8249408 0.9488707 0.5724575
> m <- matrix(rnorm(12),4)
> m
[,1] [,2] [,3]
[1,] -2.5710730 0.2524470 -0.16886795
[2,] 0.5509498 1.5430648 0.05359794
[3,] 2.4002722 0.1624704 -1.23407417
[4,] 1.4791103 0.9484525 -0.84670929
> apply(m, 2, min)
[1] -2.5710730 0.1624704 -1.2340742
> tapply(energy$expend, energy$stature, median)
lean obese
7.90 9.69
> intake$post
[1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335
> sort(intake$post)
[1] 3885 3910 4220 4680 5160 5265 5645 5975 6790 6900 7335
> order(intake$post)
[1] 3 1 2 6 4 7 5 8 9 10 11
> o <- order(intake$post)
> intake$post[o]
[1] 3885 3910 4220 4680 5160 5265 5645 5975 6790 6900 7335
> intake$pre[o]
[1] 5640 5260 5470 6515 6180 6805 6390 7515 7515 8230 8770
> intake.sorted <- intake[o,]
> save.image("ch1.RData")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> load("ch1.RData")
> .foo <- dev.copy2eps
> rm(dev.copy2eps)
> ls()
[1] "bmi" "d" "exp.lean"
[4] "exp.obese" "fpain" "height"
[7] "hh" "intake.post" "intake.pre"
[10] "intake.sorted" "l" "m"
[13] "mylist" "o" "oops"
[16] "pain" "sel" "v"
[19] "weight" "x" "xbar"
[22] "y"
> dev.copy2eps <- .foo
> rm(height, weight)
> sink("myfile")
> ls()
> sink()
> attach(thuesen)
> blood.glucose
[1] 15.3 10.8 8.1 19.5 7.2 5.3 9.3 11.1 7.5 12.2 6.7 5.2
[13] 19.0 15.1 6.7 8.6 4.2 10.3 12.5 16.1 13.3 4.9 8.8 9.5
> search()
[1] ".GlobalEnv" "thuesen" "package:ISwR"
[4] "package:stats" "package:graphics" "package:grDevices"
[7] "package:utils" "package:datasets" "package:methods"
[10] "Autoloads" "package:base"
> detach()
> search()
[1] ".GlobalEnv" "package:ISwR" "package:stats"
[4] "package:graphics" "package:grDevices" "package:utils"
[7] "package:datasets" "package:methods" "Autoloads"
[10] "package:base"
> thue2 <- subset(thuesen,blood.glucose<7)
> thue2
blood.glucose short.velocity
6 5.3 1.49
11 6.7 1.25
12 5.2 1.19
15 6.7 1.52
17 4.2 1.12
22 4.9 1.03
> thue3 <- transform(thuesen,log.gluc=log(blood.glucose))
> thue3
blood.glucose short.velocity log.gluc
1 15.3 1.76 2.727853
2 10.8 1.34 2.379546
3 8.1 1.27 2.091864
4 19.5 1.47 2.970414
5 7.2 1.27 1.974081
6 5.3 1.49 1.667707
7 9.3 1.31 2.230014
8 11.1 1.09 2.406945
9 7.5 1.18 2.014903
10 12.2 1.22 2.501436
11 6.7 1.25 1.902108
12 5.2 1.19 1.648659
13 19.0 1.95 2.944439
14 15.1 1.28 2.714695
15 6.7 1.52 1.902108
16 8.6 NA 2.151762
17 4.2 1.12 1.435085
18 10.3 1.37 2.332144
19 12.5 1.19 2.525729
20 16.1 1.05 2.778819
21 13.3 1.32 2.587764
22 4.9 1.03 1.589235
23 8.8 1.12 2.174752
24 9.5 1.70 2.251292
> thue4 <- within(thuesen,{
+ log.gluc <- log(blood.glucose)
+ m <- mean(log.gluc)
+ centered.log.gluc <- log.gluc - m
+ rm(m)
+ })
> thue4
blood.glucose short.velocity centered.log.gluc log.gluc
1 15.3 1.76 0.481879807 2.727853
2 10.8 1.34 0.133573113 2.379546
3 8.1 1.27 -0.154108960 2.091864
4 19.5 1.47 0.724441444 2.970414
5 7.2 1.27 -0.271891996 1.974081
6 5.3 1.49 -0.578266201 1.667707
7 9.3 1.31 -0.015958621 2.230014
8 11.1 1.09 0.160972087 2.406945
9 7.5 1.18 -0.231070001 2.014903
10 12.2 1.22 0.255462930 2.501436
11 6.7 1.25 -0.343865495 1.902108
12 5.2 1.19 -0.597314396 1.648659
13 19.0 1.95 0.698465958 2.944439
14 15.1 1.28 0.468721722 2.714695
15 6.7 1.52 -0.343865495 1.902108
16 8.6 NA -0.094210818 2.151762
17 4.2 1.12 -0.810888496 1.435085
18 10.3 1.37 0.086170874 2.332144
19 12.5 1.19 0.279755623 2.525729
20 16.1 1.05 0.532846250 2.778819
21 13.3 1.32 0.341791014 2.587764
22 4.9 1.03 -0.656737817 1.589235
23 8.8 1.12 -0.071221300 2.174752
24 9.5 1.70 0.005318777 2.251292
> d <- par(mar=c(5,4,4,2)+.1)
> x <- runif(50,0,2)
> y <- runif(50,0,2)
> plot(x, y, main="Main title", sub="subtitle",
+ xlab="x-label", ylab="y-label")
> text(0.6,0.6,"text at (0.6,0.6)")
> abline(h=.6,v=.6)
> for (side in 1:4) mtext(-1:4,side=side,at=.7,line=-1:4)
> mtext(paste("side",1:4), side=1:4, line=-1,font=2)
> if (.make.epsf) dev.copy2eps(file="layout.ps")
> par(d)
> plot(x, y, type="n", xlab="", ylab="", axes=F)
> points(x,y)
> axis(1)
> axis(2,at=seq(0.2,1.8,0.2))
> box()
> title(main="Main title", sub="subtitle",
+ xlab="x-label", ylab="y-label")
> set.seed(1234) #make it happen....
> x <- rnorm(100)
> hist(x,freq=F)
> curve(dnorm(x),add=T)
> h <- hist(x, plot=F)
> ylim <- range(0, h$density, dnorm(0))
> hist(x, freq=F, ylim=ylim)
> curve(dnorm(x), add=T)
> if (.make.epsf) dev.copy2eps(file="hist+norm.ps")
> hist.with.normal <- function(x, xlab=deparse(substitute(x)),...)
+ {
+ h <- hist(x, plot=F, ...)
+ s <- sd(x)
+ m <- mean(x)
+ ylim <- range(0,h$density,dnorm(0,sd=s))
+ hist(x, freq=F, ylim=ylim, xlab=xlab, ...)
+ curve(dnorm(x,m,s), add=T)
+ }
> hist.with.normal(rnorm(200))
> y <- 12345
> x <- y/2
> while (abs(x*x-y) > 1e-10) x <- (x + y/x)/2
> x
[1] 111.1081
> x^2
[1] 12345
> x <- y/2
> repeat{
+ x <- (x + y/x)/2
+ if (abs(x*x-y) < 1e-10) break
+ }
> x
[1] 111.1081
> x <- seq(0, 1,.05)
> plot(x, x, ylab="y", type="l")
> for ( j in 2:8 ) lines(x, x^j)
> t.test(bmi, mu=22.5)$p.value
[1] 0.7442183
> print
function (x, ...)
UseMethod("print")
<bytecode: 0x5602faad5278>
<environment: namespace:base>
> ## length(methods("print")) # quoted in text
> ## (this test zapped 2024-10-01 due to platform dependency)
> thuesen2 <- read.table(
+ system.file("rawdata","thuesen.txt",package="ISwR"), header=T)
> thuesen2
blood.glucose short.velocity
1 15.3 1.76
2 10.8 1.34
3 8.1 1.27
4 19.5 1.47
5 7.2 1.27
6 5.3 1.49
7 9.3 1.31
8 11.1 1.09
9 7.5 1.18
10 12.2 1.22
11 6.7 1.25
12 5.2 1.19
13 19.0 1.95
14 15.1 1.28
15 6.7 1.52
16 8.6 NA
17 4.2 1.12
18 10.3 1.37
19 12.5 1.19
20 16.1 1.05
21 13.3 1.32
22 4.9 1.03
23 8.8 1.12
24 9.5 1.70
> levels(secretin$time)
[1] "20" "30" "60" "90" "pre"
> ## IGNORE_RDIFF_BEGIN
> # keep CRAN happy - this output is obviously system-dependent
> system.file("rawdata", "thuesen.txt", package="ISwR")
[1] "/home/hornik/tmp/R.check/r-devel-gcc/Work/build/Packages/ISwR/rawdata/thuesen.txt"
> ## IGNORE_RDIFF_END
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> sample(1:40,5)
[1] 19 8 33 21 31
> sample(c("H","T"), 10, replace=T)
[1] "T" "T" "H" "T" "T" "T" "H" "T" "H" "H"
> sample(c("succ", "fail"), 10, replace=T, prob=c(0.9, 0.1))
[1] "fail" "fail" "succ" "succ" "fail" "succ" "succ" "succ"
[9] "succ" "succ"
> 1/prod(40:36)
[1] 1.266449e-08
> prod(5:1)/prod(40:36)
[1] 1.519738e-06
> 1/choose(40,5)
[1] 1.519738e-06
> x <- seq(-4,4,0.1)
> plot(x,dnorm(x),type="l")
> if (.make.epsf) dev.copy2eps(file="bellcurve.ps")
> x <- 0:50
> plot(x,dbinom(x,size=50,prob=.33),type="h")
> if (.make.epsf) dev.copy2eps(file="binomdist.ps")
> 1-pnorm(160,mean=132,sd=13)
[1] 0.01562612
> pbinom(16,size=20,prob=.5)
[1] 0.9987116
> 1-pbinom(15,size=20,prob=.5)
[1] 0.005908966
> 1-pbinom(15,20,.5)+pbinom(4,20,.5)
[1] 0.01181793
> xbar <- 83
> sigma <- 12
> n <- 5
> sem <- sigma/sqrt(n)
> sem
[1] 5.366563
> xbar + sem * qnorm(0.025)
[1] 72.48173
> xbar + sem * qnorm(0.975)
[1] 93.51827
> set.seed(310367)
> rnorm(10)
[1] -0.2996466 -0.1718510 -0.1955634 1.2280843 -2.6074190
[6] -0.2999453 -0.4655102 -1.5680666 1.2545876 -1.8028839
> rnorm(10)
[1] 1.7082495 0.1432875 -1.0271750 -0.9246647 0.6402383
[6] 0.7201677 -0.3071239 1.2090712 0.8699669 0.5882753
> rnorm(10,mean=7,sd=5)
[1] 8.934983 8.611855 4.675578 3.670129 4.223117 5.484290
[7] 12.141946 8.057541 -2.893164 13.590586
> rbinom(10,size=20,prob=.5)
[1] 12 11 10 8 11 8 11 8 8 13
> ## no data sets used by exercises
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> x <- rnorm(50)
> mean(x)
[1] -0.1565061
> sd(x)
[1] 1.175373
> var(x)
[1] 1.381502
> median(x)
[1] -0.3560654
> quantile(x)
0% 25% 50% 75% 100%
-2.1565603 -0.9689894 -0.3560654 0.6133640 2.9887241
> pvec <- seq(0,1,0.1)
> pvec
[1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
> quantile(x,pvec)
0% 10% 20% 30% 40%
-2.15656030 -1.54828008 -1.13398001 -0.79701920 -0.62973546
50% 60% 70% 80% 90%
-0.35606539 -0.05965905 0.36950570 0.85233024 1.29837806
100%
2.98872414
> attach(juul)
> mean(igf1)
[1] NA
> mean(igf1,na.rm=T)
[1] 340.168
> sum(!is.na(igf1))
[1] 1018
> summary(igf1)
Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
25.0 202.2 313.5 340.2 462.8 915.0 321
> summary(juul)
age menarche sex igf1
Min. : 0.170 Min. :1.000 Min. :1.000 Min. : 25.0
1st Qu.: 9.053 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:202.2
Median :12.560 Median :1.000 Median :2.000 Median :313.5
Mean :15.095 Mean :1.476 Mean :1.534 Mean :340.2
3rd Qu.:16.855 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:462.8
Max. :83.000 Max. :2.000 Max. :2.000 Max. :915.0
NAs :5 NAs :635 NAs :5 NAs :321
tanner testvol
Min. :1.00 Min. : 1.000
1st Qu.:1.00 1st Qu.: 1.000
Median :2.00 Median : 3.000
Mean :2.64 Mean : 7.896
3rd Qu.:5.00 3rd Qu.:15.000
Max. :5.00 Max. :30.000
NAs :240 NAs :859
> detach(juul)
> juul$sex <- factor(juul$sex,labels=c("M","F"))
> juul$menarche <- factor(juul$menarche,labels=c("No","Yes"))
> juul$tanner <- factor(juul$tanner,
+ labels=c("I","II","III","IV","V"))
> attach(juul)
> summary(juul)
age menarche sex igf1 tanner
Min. : 0.170 No :369 M :621 Min. : 25.0 I :515
1st Qu.: 9.053 Yes:335 F :713 1st Qu.:202.2 II :103
Median :12.560 NAs:635 NAs: 5 Median :313.5 III: 72
Mean :15.095 Mean :340.2 IV : 81
3rd Qu.:16.855 3rd Qu.:462.8 V :328
Max. :83.000 Max. :915.0 NAs:240
NAs :5 NAs :321
testvol
Min. : 1.000
1st Qu.: 1.000
Median : 3.000
Mean : 7.896
3rd Qu.:15.000
Max. :30.000
NAs :859
> hist(x)
> if (.make.epsf) dev.copy2eps(file="hist.ps")
> mid.age <- c(2.5,7.5,13,16.5,17.5,19,22.5,44.5,70.5)
> acc.count <- c(28,46,58,20,31,64,149,316,103)
> age.acc <- rep(mid.age,acc.count)
> brk <- c(0,5,10,16,17,18,20,25,60,80)
> hist(age.acc,breaks=brk)
> if (.make.epsf) dev.copy2eps(file="hist-acc-right.ps")
> n <- length(x)
> plot(sort(x),(1:n)/n,type="s",ylim=c(0,1))
> if (.make.epsf) dev.copy2eps(file="empdist.ps")
> qqnorm(x)
> if (.make.epsf) dev.copy2eps(file="qqnorm.ps")
> par(mfrow=c(1,2))
> boxplot(IgM)
> boxplot(log(IgM))
> par(mfrow=c(1,1))
> if (.make.epsf) dev.copy2eps(file="boxplot-IgM.ps")
> attach(red.cell.folate)
> tapply(folate,ventilation,mean)
N2O+O2,24h N2O+O2,op O2,24h
316.6250 256.4444 278.0000
> tapply(folate,ventilation,sd)
N2O+O2,24h N2O+O2,op O2,24h
58.71709 37.12180 33.75648
> tapply(folate,ventilation,length)
N2O+O2,24h N2O+O2,op O2,24h
8 9 5
> xbar <- tapply(folate, ventilation, mean)
> s <- tapply(folate, ventilation, sd)
> n <- tapply(folate, ventilation, length)
> cbind(mean=xbar, std.dev=s, n=n)
mean std.dev n
N2O+O2,24h 316.6250 58.71709 8
N2O+O2,op 256.4444 37.12180 9
O2,24h 278.0000 33.75648 5
> tapply(igf1, tanner, mean)
I II III IV V
NA NA NA NA NA
> tapply(igf1, tanner, mean, na.rm=T)
I II III IV V
207.4727 352.6714 483.2222 513.0172 465.3344
> aggregate(juul[c("age","igf1")],
+ list(sex=juul$sex), mean, na.rm=T)
sex age igf1
1 M 15.38436 310.8866
2 F 14.84363 368.1006
> aggregate(juul[c("age","igf1")], juul["sex"], mean, na.rm=T)
sex age igf1
1 M 15.38436 310.8866
2 F 14.84363 368.1006
> by(juul, juul["sex"], summary)
sex: M
age menarche sex igf1 tanner
Min. : 0.17 No : 0 M:621 Min. : 29.0 I :291
1st Qu.: 8.85 Yes: 0 F: 0 1st Qu.:176.0 II : 55
Median :12.38 NAs:621 Median :280.0 III: 34
Mean :15.38 Mean :310.9 IV : 41
3rd Qu.:16.77 3rd Qu.:430.2 V :124
Max. :83.00 Max. :915.0 NAs: 76
NAs :145
testvol
Min. : 1.000
1st Qu.: 1.000
Median : 3.000
Mean : 7.896
3rd Qu.:15.000
Max. :30.000
NAs :141
-------------------------------------------------
sex: F
age menarche sex igf1 tanner
Min. : 0.25 No :369 M: 0 Min. : 25.0 I :224
1st Qu.: 9.30 Yes:335 F:713 1st Qu.:233.0 II : 48
Median :12.80 NAs: 9 Median :352.0 III: 38
Mean :14.84 Mean :368.1 IV : 40
3rd Qu.:16.93 3rd Qu.:483.0 V :204
Max. :75.12 Max. :914.0 NAs:159
NAs :176
testvol
Min. : NA
1st Qu.: NA
Median : NA
Mean :NaN
3rd Qu.: NA
Max. : NA
NAs :713
> attach(energy)
> expend.lean <- expend[stature=="lean"]
> expend.obese <- expend[stature=="obese"]
> par(mfrow=c(2,1))
> hist(expend.lean,breaks=10,xlim=c(5,13),ylim=c(0,4),col="white")
> hist(expend.obese,breaks=10,xlim=c(5,13),ylim=c(0,4),col="grey")
> par(mfrow=c(1,1))
> if (.make.epsf) dev.copy2eps(file="expend-hist-2on1.ps")
> boxplot(expend ~ stature)
> if (.make.epsf) dev.copy2eps(file="boxplots-expend-stat.ps")
> boxplot(expend.lean,expend.obese)
> opar <- par(mfrow=c(2,2), mex=0.8, mar=c(3,3,2,1)+.1)
> stripchart(expend ~ stature)
> stripchart(expend ~ stature, method="stack")
> stripchart(expend ~ stature, method="jitter")
> stripchart(expend ~ stature, method="jitter", jitter=.03)
> par(opar)
> if (.make.epsf) dev.copy2eps(file="stripcharts-expend-stat.ps")
> caff.marital <- matrix(c(652,1537,598,242,36,46,38,21,218
+ ,327,106,67),
+ nrow=3,byrow=T)
> caff.marital
[,1] [,2] [,3] [,4]
[1,] 652 1537 598 242
[2,] 36 46 38 21
[3,] 218 327 106 67
> colnames(caff.marital) <- c("0","1-150","151-300",">300")
> rownames(caff.marital) <- c("Married","Prev.married","Single")
> caff.marital
0 1-150 151-300 >300
Married 652 1537 598 242
Prev.married 36 46 38 21
Single 218 327 106 67
> names(dimnames(caff.marital)) <- c("marital","consumption")
> caff.marital
consumption
marital 0 1-150 151-300 >300
Married 652 1537 598 242
Prev.married 36 46 38 21
Single 218 327 106 67
> as.data.frame(as.table(caff.marital))
marital consumption Freq
1 Married 0 652
2 Prev.married 0 36
3 Single 0 218
4 Married 1-150 1537
5 Prev.married 1-150 46
6 Single 1-150 327
7 Married 151-300 598
8 Prev.married 151-300 38
9 Single 151-300 106
10 Married >300 242
11 Prev.married >300 21
12 Single >300 67
> table(sex)
sex
M F
621 713
> table(sex,menarche)
menarche
sex No Yes
M 0 0
F 369 335
> table(menarche,tanner)
tanner
menarche I II III IV V
No 221 43 32 14 2
Yes 1 1 5 26 202
> xtabs(~ tanner + sex, data=juul)
sex
tanner M F
I 291 224
II 55 48
III 34 38
IV 41 40
V 124 204
> xtabs(~ dgn + diab + coma, data=stroke)
, , coma = No
diab
dgn No Yes
ICH 53 6
ID 143 21
INF 411 64
SAH 38 0
, , coma = Yes
diab
dgn No Yes
ICH 19 1
ID 23 3
INF 23 2
SAH 9 0
> ftable(coma + diab ~ dgn, data=stroke)
coma No Yes
diab No Yes No Yes
dgn
ICH 53 6 19 1
ID 143 21 23 3
INF 411 64 23 2
SAH 38 0 9 0
> t(caff.marital)
marital
consumption Married Prev.married Single
0 652 36 218
1-150 1537 46 327
151-300 598 38 106
>300 242 21 67
> tanner.sex <- table(tanner,sex)
> tanner.sex
sex
tanner M F
I 291 224
II 55 48
III 34 38
IV 41 40
V 124 204
> margin.table(tanner.sex,1)
tanner
I II III IV V
515 103 72 81 328
> margin.table(tanner.sex,2)
sex
M F
545 554
> prop.table(tanner.sex,1)
sex
tanner M F
I 0.5650485 0.4349515
II 0.5339806 0.4660194
III 0.4722222 0.5277778
IV 0.5061728 0.4938272
V 0.3780488 0.6219512
> tanner.sex/sum(tanner.sex)
sex
tanner M F
I 0.26478617 0.20382166
II 0.05004550 0.04367607
III 0.03093722 0.03457689
IV 0.03730664 0.03639672
V 0.11282985 0.18562329
> total.caff <- margin.table(caff.marital,2)
> total.caff
consumption
0 1-150 151-300 >300
906 1910 742 330
> barplot(total.caff, col="white")
> if (.make.epsf) dev.copy2eps(file="simple-bar.ps")
> par(mfrow=c(2,2))
> barplot(caff.marital, col="white")
> barplot(t(caff.marital), col="white")
> barplot(t(caff.marital), col="white", beside=T)
> barplot(prop.table(t(caff.marital),2), col="white", beside=T)
> par(mfrow=c(1,1))
> if (.make.epsf) dev.copy2eps(file="mat-4-bar.ps")
> barplot(prop.table(t(caff.marital),2),beside=T,
+ legend.text=colnames(caff.marital),
+ col=c("white","grey80","grey50","black"))
> if (.make.epsf) dev.copy2eps(file="pretty-bar.ps")
> dotchart(t(caff.marital), lcolor="black")
> if (.make.epsf) dev.copy2eps(file="dotchart.ps")
> opar <- par(mfrow=c(2,2),mex=0.8, mar=c(1,1,2,1))
> slices <- c("white","grey80","grey50","black")
> pie(caff.marital["Married",], main="Married", col=slices)
> pie(caff.marital["Prev.married",],
+ main="Previously married", col=slices)
> pie(caff.marital["Single",], main="Single", col=slices)
> par(opar)
> if (.make.epsf) dev.copy2eps(file="pie.ps")
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> daily.intake <- c(5260,5470,5640,6180,6390,6515,
+ 6805,7515,7515,8230,8770)
> mean(daily.intake)
[1] 6753.636
> sd(daily.intake)
[1] 1142.123
> quantile(daily.intake)
0% 25% 50% 75% 100%
5260 5910 6515 7515 8770
> t.test(daily.intake,mu=7725)
One Sample t-test
data: daily.intake
t = -2.8208, df = 10, p-value = 0.01814
alternative hypothesis: true mean is not equal to 7725
95 percent confidence interval:
5986.348 7520.925
sample estimates:
mean of x
6753.636
> t.test(daily.intake,mu=7725)
One Sample t-test
data: daily.intake
t = -2.8208, df = 10, p-value = 0.01814
alternative hypothesis: true mean is not equal to 7725
95 percent confidence interval:
5986.348 7520.925
sample estimates:
mean of x
6753.636
> wilcox.test(daily.intake, mu=7725, exact=FALSE)
Wilcoxon signed rank test with continuity correction
data: daily.intake
V = 8, p-value = 0.0293
alternative hypothesis: true location is not equal to 7725
> attach(energy)
> energy
expend stature
1 9.21 obese
2 7.53 lean
3 7.48 lean
4 8.08 lean
5 8.09 lean
6 10.15 lean
7 8.40 lean
8 10.88 lean
9 6.13 lean
10 7.90 lean
11 11.51 obese
12 12.79 obese
13 7.05 lean
14 11.85 obese
15 9.97 obese
16 7.48 lean
17 8.79 obese
18 9.69 obese
19 9.68 obese
20 7.58 lean
21 9.19 obese
22 8.11 lean
> t.test(expend~stature)
Welch Two Sample t-test
data: expend by stature
t = -3.8555, df = 15.919, p-value = 0.001411
alternative hypothesis: true difference in means between group lean and group obese is not equal to 0
95 percent confidence interval:
-3.459167 -1.004081
sample estimates:
mean in group lean mean in group obese
8.066154 10.297778
> t.test(expend~stature, var.equal=T)
Two Sample t-test
data: expend by stature
t = -3.9456, df = 20, p-value = 0.000799
alternative hypothesis: true difference in means between group lean and group obese is not equal to 0
95 percent confidence interval:
-3.411451 -1.051796
sample estimates:
mean in group lean mean in group obese
8.066154 10.297778
> var.test(expend~stature)
F test to compare two variances
data: expend by stature
F = 0.78445, num df = 12, denom df = 8, p-value = 0.6797
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.1867876 2.7547991
sample estimates:
ratio of variances
0.784446
> wilcox.test(expend~stature, exact=FALSE)
Wilcoxon rank sum test with continuity correction
data: expend by stature
W = 12, p-value = 0.002122
alternative hypothesis: true location shift is not equal to 0
> attach(intake)
> intake
pre post
1 5260 3910
2 5470 4220
3 5640 3885
4 6180 5160
5 6390 5645
6 6515 4680
7 6805 5265
8 7515 5975
9 7515 6790
10 8230 6900
11 8770 7335
> post - pre
[1] -1350 -1250 -1755 -1020 -745 -1835 -1540 -1540 -725 -1330
[11] -1435
> t.test(pre, post, paired=T)
Paired t-test
data: pre and post
t = 11.941, df = 10, p-value = 3.059e-07
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
1074.072 1566.838
sample estimates:
mean difference
1320.455
> t.test(pre, post) #WRONG!
Welch Two Sample t-test
data: pre and post
t = 2.6242, df = 19.92, p-value = 0.01629
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
270.5633 2370.3458
sample estimates:
mean of x mean of y
6753.636 5433.182
> wilcox.test(pre, post, paired=T, exact=FALSE)
Wilcoxon signed rank test with continuity correction
data: pre and post
V = 66, p-value = 0.00384
alternative hypothesis: true location shift is not equal to 0
> rm(list=ls())
> while(search()[2] != "package:ISwR") detach()
> attach(thuesen)
> lm(short.velocity~blood.glucose)
Call:
lm(formula = short.velocity ~ blood.glucose)
Coefficients:
(Intercept) blood.glucose
1.09781 0.02196
> summary(lm(short.velocity~blood.glucose))
Call:
lm(formula = short.velocity ~ blood.glucose)
Residuals:
Min 1Q Median 3Q Max
-0.40141 -0.14760 -0.02202 0.03001 0.43490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.09781 0.11748 9.345 6.26e-09 ***
blood.glucose 0.02196 0.01045 2.101 0.0479 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.2167 on 21 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1737, Adjusted R-squared: 0.1343
F-statistic: 4.414 on 1 and 21 DF, p-value: 0.0479
> summary(lm(short.velocity~blood.glucose))
Call:
lm(formula = short.velocity ~ blood.glucose)
Residuals:
Min 1Q Median 3Q Max
-0.40141 -0.14760 -0.02202 0.03001 0.43490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.09781 0.11748 9.345 6.26e-09 ***
blood.glucose 0.02196 0.01045 2.101 0.0479 *
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.2167 on 21 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1737, Adjusted R-squared: 0.1343
F-statistic: 4.414 on 1 and 21 DF, p-value: 0.0479
> plot(blood.glucose,short.velocity)
> abline(lm(short.velocity~blood.glucose))
> if (.make.epsf) dev.copy2eps(file="velo-gluc-line.ps")
> lm.velo <- lm(short.velocity~blood.glucose)
> fitted(lm.velo)
1 2 3 4 5 6 7
1.433841 1.335010 1.275711 1.526084 1.255945 1.214216 1.302066
8 9 10 11 12 13 14
1.341599 1.262534 1.365758 1.244964 1.212020 1.515103 1.429449
15 17 18 19 20 21 22
1.244964 1.190057 1.324029 1.372346 1.451411 1.389916 1.205431
23 24
1.291085 1.306459
> resid(lm.velo)
1 2 3 4 5
0.326158532 0.004989882 -0.005711308 -0.056084062 0.014054962
6 7 8 9 10
0.275783754 0.007933665 -0.251598875 -0.082533795 -0.145757649
11 12 13 14 15
0.005036223 -0.022019994 0.434897199 -0.149448964 0.275036223
17 18 19 20 21
-0.070057471 0.045971143 -0.182346406 -0.401411486 -0.069916424
22 23 24
-0.175431237 -0.171085074 0.393541161
> options(error=expression(NULL))
> plot(blood.glucose,short.velocity)
> lines(blood.glucose,fitted(lm.velo))
Error in xy.coords(x, y) : 'x' and 'y' lengths differ
Calls: lines -> lines.default -> plot.xy -> xy.coords
> options(error=NULL)
> lines(blood.glucose[!is.na(short.velocity)],fitted(lm.velo))
> cc <- complete.cases(thuesen)
> options(na.action=na.exclude)
> lm.velo <- lm(short.velocity~blood.glucose)
> fitted(lm.velo)
1 2 3 4 5 6 7
1.433841 1.335010 1.275711 1.526084 1.255945 1.214216 1.302066
8 9 10 11 12 13 14
1.341599 1.262534 1.365758 1.244964 1.212020 1.515103 1.429449
15 16 17 18 19 20 21
1.244964 NA 1.190057 1.324029 1.372346 1.451411 1.389916
22 23 24
1.205431 1.291085 1.306459
> segments(blood.glucose,fitted(lm.velo),
+ blood.glucose,short.velocity)
> if (.make.epsf) dev.copy2eps(file="velo-gluc-seg.ps")
> plot(fitted(lm.velo),resid(lm.velo))
> if (.make.epsf) dev.copy2eps(file="velo-gluc-resid.ps")
> qqnorm(resid(lm.velo))
> if (.make.epsf) dev.copy2eps(file="velo-gluc-qqnorm.ps")
> predict(lm.velo)
1 2 3 4 5 6 7
1.433841 1.335010 1.275711 1.526084 1.255945 1.214216 1.302066
8 9 10 11 12 13 14
1.341599 1.262534 1.365758 1.244964 1.212020 1.515103 1.429449
15 16 17 18 19 20 21
1.244964 NA 1.190057 1.324029 1.372346 1.451411 1.389916
22 23 24
1.205431 1.291085 1.306459
> predict(lm.velo,int="c")
fit lwr upr
1 1.433841 1.291371 1.576312
2 1.335010 1.240589 1.429431
3 1.275711 1.169536 1.381887
4 1.526084 1.306561 1.745607
5 1.255945 1.139367 1.372523
6 1.214216 1.069315 1.359118
7 1.302066 1.205244 1.398889
8 1.341599 1.246317 1.436881
9 1.262534 1.149694 1.375374
10 1.365758 1.263750 1.467765
11 1.244964 1.121641 1.368287
12 1.212020 1.065457 1.358583
13 1.515103 1.305352 1.724854
14 1.429449 1.290217 1.568681
15 1.244964 1.121641 1.368287
16 NA NA NA
17 1.190057 1.026217 1.353898
18 1.324029 1.230050 1.418008
19 1.372346 1.267629 1.477064
20 1.451411 1.295446 1.607377
21 1.389916 1.276444 1.503389
22 1.205431 1.053805 1.357057
23 1.291085 1.191084 1.391086
24 1.306459 1.210592 1.402326
> predict(lm.velo,int="p")
fit lwr upr
1 1.433841 0.9612137 1.906469
2 1.335010 0.8745815 1.795439
3 1.275711 0.8127292 1.738693
4 1.526084 1.0248161 2.027352
5 1.255945 0.7904672 1.721423
6 1.214216 0.7408499 1.687583
7 1.302066 0.8411393 1.762993
8 1.341599 0.8809929 1.802205
9 1.262534 0.7979780 1.727090
10 1.365758 0.9037136 1.827802
11 1.244964 0.7777510 1.712177
12 1.212020 0.7381424 1.685898
13 1.515103 1.0180367 2.012169
14 1.429449 0.9577873 1.901111
15 1.244964 0.7777510 1.712177
16 NA NA NA
17 1.190057 0.7105546 1.669560
18 1.324029 0.8636906 1.784367
19 1.372346 0.9096964 1.834996
20 1.451411 0.9745421 1.928281
21 1.389916 0.9252067 1.854626
22 1.205431 0.7299634 1.680899
23 1.291085 0.8294798 1.752690
24 1.306459 0.8457315 1.767186
Warning message:
In predict.lm(lm.velo, int = "p") :*** buffer overflow detected ***: terminated
Aborted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 2.0-12
Check: tests
Result: NOTE
Running ‘allexercises.R’
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...
180c180
< V = 13858, p-value = 4.225e-14
---
> V = 9283.5, p-value = 2.075e-13
471c471
< V = 0, p-value = 0.0452
---
> V = 0, p-value = 0.05906
Running ‘allscripts.R’
Comparing ‘allscripts.Rout’ to ‘allscripts.Rout.save’ ... OK
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 2.0-12
Check: tests
Result: NOTE
Running 'allexercises.R' [4s]
Comparing 'allexercises.Rout' to 'allexercises.Rout.save' ...
180c180
< V = 13858, p-value = 4.225e-14
---
> V = 9283.5, p-value = 2.075e-13
471c471
< V = 0, p-value = 0.0452
---
> V = 0, p-value = 0.05906
Running 'allscripts.R' [4s]
Comparing 'allscripts.Rout' to 'allscripts.Rout.save' ... OK
Flavor: r-devel-windows-x86_64
Version: 2.0-12
Check: tests
Result: NOTE
Running ‘allexercises.R’ [1s/1s]
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...26c26
< NA's :5 NA's :11 NA's :5 NA's :119 NA's :66
---
> NAs :5 NAs :11 NAs :5 NAs :119 NAs :66
34c34
< NA's :351
---
> NAs :351
Running ‘allscripts.R’ [1s/1s]
Comparing ‘allscripts.Rout’ to ‘allscripts.Rout.save’ ...597c597
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
607c607
< NA's :5 NA's :635 NA's :5 NA's :321
---
> NAs :5 NAs :635 NAs :5 NAs :321
615c615
< NA's :240 NA's :859
---
> NAs :240 NAs :859
623,638c623,638
< age menarche sex igf1
< Min. : 0.170 No :369 M :621 Min. : 25.0
< 1st Qu.: 9.053 Yes :335 F :713 1st Qu.:202.2
< Median :12.560 NA's:635 NA's: 5 Median :313.5
< Mean :15.095 Mean :340.2
< 3rd Qu.:16.855 3rd Qu.:462.8
< Max. :83.000 Max. :915.0
< NA's :5 NA's :321
< tanner testvol
< I :515 Min. : 1.000
< II :103 1st Qu.: 1.000
< III : 72 Median : 3.000
< IV : 81 Mean : 7.896
< V :328 3rd Qu.:15.000
< NA's:240 Max. :30.000
< NA's :859
---
> age menarche sex igf1 tanner
> Min. : 0.170 No :369 M :621 Min. : 25.0 I :515
> 1st Qu.: 9.053 Yes:335 F :713 1st Qu.:202.2 II :103
> Median :12.560 NAs:635 NAs: 5 Median :313.5 III: 72
> Mean :15.095 Mean :340.2 IV : 81
> 3rd Qu.:16.855 3rd Qu.:462.8 V :328
> Max. :83.000 Max. :915.0 NAs:240
> NAs :5 NAs :321
> testvol
> Min. : 1.000
> 1st Qu.: 1.000
> Median : 3.000
> Mean : 7.896
> 3rd Qu.:15.000
> Max. :30.000
> NAs :859
695c695
< Median :12.38 NA's:621 Median :280.0 III : 34
---
> Median :12.38 NAs:621 Median :280.0 III: 34
698,699c698,699
< Max. :83.00 Max. :915.0 NA's: 76
< NA's :145
---
> Max. :83.00 Max. :915.0 NAs: 76
> NAs :145
707c707
< NA's :141
---
> NAs :141
713c713
< Median :12.80 NA's: 9 Median :352.0 III : 38
---
> Median :12.80 NAs: 9 Median :352.0 III: 38
716,717c716,717
< Max. :75.12 Max. :914.0 NA's:159
< NA's :176
---
> Max. :75.12 Max. :914.0 NAs:159
> NAs :176
725c725
< NA's :713
---
> NAs :713
1279c1279
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1293c1293
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1329c1329
< I II III IV V NA's
---
> I II III IV V NAs
1798c1798
< Max. NA's
---
> Max. NAs
1806c1806
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
Flavor: r-oldrel-macos-arm64
Version: 2.0-12
Check: tests
Result: NOTE
Running ‘allexercises.R’ [3s/4s]
Comparing ‘allexercises.Rout’ to ‘allexercises.Rout.save’ ...26c26
< NA's :5 NA's :11 NA's :5 NA's :119 NA's :66
---
> NAs :5 NAs :11 NAs :5 NAs :119 NAs :66
34c34
< NA's :351
---
> NAs :351
Running ‘allscripts.R’ [3s/4s]
Comparing ‘allscripts.Rout’ to ‘allscripts.Rout.save’ ...597c597
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
607c607
< NA's :5 NA's :635 NA's :5 NA's :321
---
> NAs :5 NAs :635 NAs :5 NAs :321
615c615
< NA's :240 NA's :859
---
> NAs :240 NAs :859
623,638c623,638
< age menarche sex igf1
< Min. : 0.170 No :369 M :621 Min. : 25.0
< 1st Qu.: 9.053 Yes :335 F :713 1st Qu.:202.2
< Median :12.560 NA's:635 NA's: 5 Median :313.5
< Mean :15.095 Mean :340.2
< 3rd Qu.:16.855 3rd Qu.:462.8
< Max. :83.000 Max. :915.0
< NA's :5 NA's :321
< tanner testvol
< I :515 Min. : 1.000
< II :103 1st Qu.: 1.000
< III : 72 Median : 3.000
< IV : 81 Mean : 7.896
< V :328 3rd Qu.:15.000
< NA's:240 Max. :30.000
< NA's :859
---
> age menarche sex igf1 tanner
> Min. : 0.170 No :369 M :621 Min. : 25.0 I :515
> 1st Qu.: 9.053 Yes:335 F :713 1st Qu.:202.2 II :103
> Median :12.560 NAs:635 NAs: 5 Median :313.5 III: 72
> Mean :15.095 Mean :340.2 IV : 81
> 3rd Qu.:16.855 3rd Qu.:462.8 V :328
> Max. :83.000 Max. :915.0 NAs:240
> NAs :5 NAs :321
> testvol
> Min. : 1.000
> 1st Qu.: 1.000
> Median : 3.000
> Mean : 7.896
> 3rd Qu.:15.000
> Max. :30.000
> NAs :859
695c695
< Median :12.38 NA's:621 Median :280.0 III : 34
---
> Median :12.38 NAs:621 Median :280.0 III: 34
698,699c698,699
< Max. :83.00 Max. :915.0 NA's: 76
< NA's :145
---
> Max. :83.00 Max. :915.0 NAs: 76
> NAs :145
707c707
< NA's :141
---
> NAs :141
713c713
< Median :12.80 NA's: 9 Median :352.0 III : 38
---
> Median :12.80 NAs: 9 Median :352.0 III: 38
716,717c716,717
< Max. :75.12 Max. :914.0 NA's:159
< NA's :176
---
> Max. :75.12 Max. :914.0 NAs:159
> NAs :176
725c725
< NA's :713
---
> NAs :713
1279c1279
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1293c1293
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1329c1329
< I II III IV V NA's
---
> I II III IV V NAs
1798c1798
< Max. NA's
---
> Max. NAs
1806c1806
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
Flavor: r-oldrel-macos-x86_64
Version: 2.0-12
Check: tests
Result: NOTE
Running 'allexercises.R' [6s]
Comparing 'allexercises.Rout' to 'allexercises.Rout.save' ...26c26
< NA's :5 NA's :11 NA's :5 NA's :119 NA's :66
---
> NAs :5 NAs :11 NAs :5 NAs :119 NAs :66
34c34
< NA's :351
---
> NAs :351
Running 'allscripts.R' [6s]
Comparing 'allscripts.Rout' to 'allscripts.Rout.save' ...597c597
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
607c607
< NA's :5 NA's :635 NA's :5 NA's :321
---
> NAs :5 NAs :635 NAs :5 NAs :321
615c615
< NA's :240 NA's :859
---
> NAs :240 NAs :859
623,638c623,638
< age menarche sex igf1
< Min. : 0.170 No :369 M :621 Min. : 25.0
< 1st Qu.: 9.053 Yes :335 F :713 1st Qu.:202.2
< Median :12.560 NA's:635 NA's: 5 Median :313.5
< Mean :15.095 Mean :340.2
< 3rd Qu.:16.855 3rd Qu.:462.8
< Max. :83.000 Max. :915.0
< NA's :5 NA's :321
< tanner testvol
< I :515 Min. : 1.000
< II :103 1st Qu.: 1.000
< III : 72 Median : 3.000
< IV : 81 Mean : 7.896
< V :328 3rd Qu.:15.000
< NA's:240 Max. :30.000
< NA's :859
---
> age menarche sex igf1 tanner
> Min. : 0.170 No :369 M :621 Min. : 25.0 I :515
> 1st Qu.: 9.053 Yes:335 F :713 1st Qu.:202.2 II :103
> Median :12.560 NAs:635 NAs: 5 Median :313.5 III: 72
> Mean :15.095 Mean :340.2 IV : 81
> 3rd Qu.:16.855 3rd Qu.:462.8 V :328
> Max. :83.000 Max. :915.0 NAs:240
> NAs :5 NAs :321
> testvol
> Min. : 1.000
> 1st Qu.: 1.000
> Median : 3.000
> Mean : 7.896
> 3rd Qu.:15.000
> Max. :30.000
> NAs :859
695c695
< Median :12.38 NA's:621 Median :280.0 III : 34
---
> Median :12.38 NAs:621 Median :280.0 III: 34
698,699c698,699
< Max. :83.00 Max. :915.0 NA's: 76
< NA's :145
---
> Max. :83.00 Max. :915.0 NAs: 76
> NAs :145
707c707
< NA's :141
---
> NAs :141
713c713
< Median :12.80 NA's: 9 Median :352.0 III : 38
---
> Median :12.80 NAs: 9 Median :352.0 III: 38
716,717c716,717
< Max. :75.12 Max. :914.0 NA's:159
< NA's :176
---
> Max. :75.12 Max. :914.0 NAs:159
> NAs :176
725c725
< NA's :713
---
> NAs :713
1279c1279
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1293c1293
< Cannot compute exact p-value with ties
---
> cannot compute exact p-value with ties
1329c1329
< I II III IV V NA's
---
> I II III IV V NAs
1798c1798
< Max. NA's
---
> Max. NAs
1806c1806
< Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
---
> Min. 1st Qu. Median Mean 3rd Qu. Max. NAs
Flavor: r-oldrel-windows-x86_64