Title: | Functions and Data for the Book "Applied Nonparametric Statistical Methods", 5th Edition |
Version: | 1.1.1 |
Description: | Functions and data to accompany the 5th edition of the book "Applied Nonparametric Statistical Methods" (4th edition: Sprent & Smeeton, 2024, ISBN:158488701X), the revisions from the 4th edition including a move from describing the output from a miscellany of statistical software packages to using R. While the output from many of the functions can also be obtained using a range of other R functions, this package provides functions in a unified setting and give output using both p-values and confidence intervals, exemplifying the book's approach of treating p-values as a guide to statistical importance and not an end product in their own right. Please note that in creating the ANSM5 package we do not claim to have produced software which is necessarily the most computationally efficient nor the most comprehensive. |
Encoding: | UTF-8 |
RoxygenNote: | 7.2.1 |
Suggests: | testthat (≥ 3.0.0) |
Config/testthat/edition: | 3 |
Imports: | stats |
Depends: | R (≥ 2.10) |
LazyData: | true |
License: | GPL (≥ 3) |
NeedsCompilation: | no |
Packaged: | 2024-08-31 15:30:04 UTC; soeqnhs |
Author: | Neil Spencer |
Maintainer: | Neil Spencer <neilhspencer@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2024-08-31 22:30:02 UTC |
Perform Ansari-Bradley test
Description
ansari.bradley()
performs the Ansari-Bradley test and is used in chapter 6 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
ansari.bradley(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 25,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Numeric vector |
y |
Numeric vector |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 6.12 from "Applied Nonparametric Statistical Methods" (5th edition)
ansari.bradley(ch6$typeA, ch6$typeB)
# Exercise 6.16 from "Applied Nonparametric Statistical Methods" (5th edition)
ansari.bradley(ch6$travel, ch6$politics)
Data in Appendix 1
Description
Data in Appendix 1 of "Applied Nonparametric Statistical Methods" (5th edition)
McAlpha (used in example 4.5)
McBeta (used in example 6.6)
McGamma (used in exercise 4.1, example 6.6)
McDelta (used in examples 10.4, 10.8, exercise 10.5)
Usage
app1
Format
app1
A list with 4 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Perform Binomial test
Description
binom()
performs the Binomial test and calculates the Binomial confidence interval and is used in chapters 4, 5 and 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
binom(
r,
n,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.cases = 1e+07,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = TRUE
)
Arguments
r |
Number of successes |
n |
Number of trials |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 4.6 from "Applied Nonparametric Statistical Methods" (5th edition)
binom(3, 20)
# Exercise 5.8 from "Applied Nonparametric Statistical Methods" (5th edition)
binom(24, 40, 0.5)
Calculate Blomqvist coefficient
Description
blomqvist()
calculates the Blomqvist coefficient and is used in chapter 10 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
blomqvist(
x,
y,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 1000,
nsims.mc = 1e+05,
seed = NULL,
do.exact = TRUE,
do.mc = FALSE
)
Arguments
x |
Numeric vector of same length as y |
y |
Numeric vector of same length as x |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Example 10.9 from "Applied Nonparametric Statistical Methods" (5th edition)
blomqvist(ch10$q1, ch10$q2, alternative = "greater")
# Exercise 10.7 from "Applied Nonparametric Statistical Methods" (5th edition)
blomqvist(ch10$ERA, ch10$SSS)
Perform Bowker's extension of McNemar's test
Description
bowker()
performs the Bowker's extension of McNemar's test and is used in chapter 12 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
bowker(x, y = NULL, do.asymp = TRUE)
Arguments
x |
Factor of same length as y, or two-dimensional square table |
y |
Factor of same length as x (or NULL if x is table) (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 12.12 from "Applied Nonparametric Statistical Methods" (5th edition)
bowker(ch12$side.effect.new, ch12$side.effect.old)
# Exercise 12.12 from "Applied Nonparametric Statistical Methods" (5th edition)
bowker(ch12$first.response, ch12$second.response)
Perform Breslow and Day test
Description
breslow.day()
performs the Breslow and Day test and is used in chapter 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
breslow.day(x, y, z, CI.width = 0.95, do.asymp = TRUE, do.CI = TRUE)
Arguments
x |
Binary factor of same length as y, z |
y |
Binary factor of same length as x, z |
z |
Factor of same length as x, y |
CI.width |
Confidence interval width (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 13.3 from "Applied Nonparametric Statistical Methods" (5th edition)
breslow.day(ch13$machine, ch13$output.status, ch13$material.source)
# Exercise 13.7 from "Applied Nonparametric Statistical Methods" (5th edition)
breslow.day(ch13$medicine, ch13$response, ch13$location)
Create bootstrap confidence interval
Description
bs()
creates a bootstrap confidence interval and is used in chapter 14 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
bs(x, y = NULL, CI.width = 0.95, nsims.bs = 10000, seed = NULL)
Arguments
x |
Numeric vector |
y |
Numeric vector or NULL (defaults to |
CI.width |
Confidence interval width (defaults to |
nsims.bs |
Number of bootstrap samples to be taken (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
Value
A list object object with the results from applying the function
Examples
# Example 14.5 from "Applied Nonparametric Statistical Methods" (5th edition)
bs(ch14$example14.2, nsims.bs = 2000, CI.width = 0.95, seed = 1)
bs(ch14$example14.2, nsims.bs = 2000, CI.width = 0.99, seed = 1)
Data used in Chapter 10
Description
Data used in Chapter 10 of "Applied Nonparametric Statistical Methods" (5th edition)
q1 (used in section 10.1.2, examples 10.2, 10.5, 10.9)
q2 (used in section 10.1.2, examples 10.2, 10.5, 10.9)
death.year (used in examples 10.4, 10.8)
diving.rank (used in example 10.10)
competitors (used in example 10.10)
judges (used in example 10.10)
dentistA (used in example 10.11)
dentistB (used in example 10.11)
questionnaire (used in example 10.12, exercise 10.13)
demonstration (used in example 10.12, exercise 10.13)
gender (used in exercise 10.13)
items (used in example 10.12)
ERA (used in exercises 10.1, 10.3, 10.6, 10.7)
ESMS (used in exercises 10.1, 10.3, 10.6)
SSS (used in exercise 10.7)
British (used in example 10.8, exercise 10.10)
American (used in example 10.8, exercise 10.10)
Canadian (used in example 10.9, exercise 10.10)
Australian (used in example 10.9, exercise 10.10)
design (used in exercise 10.10)
country (used in exercise 10.10)
marks (used in exercise 10.11)
script (used in exercise 10.11)
examiner (used in exercise 10.11)
observerA (used in exercise 10.12)
observerB (used in exercise 10.12)
Usage
ch10
Format
ch10
A list with 26 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 11
Description
Data used in Chapter 11 of "Applied Nonparametric Statistical Methods" (5th edition)
parentlimit (used in examples 11.2, 11.3, 11.4, 11.6)
reportedtime (used in examples 11.2, 11.3, 11.4, 11.6)
age (used in example 11.5)
length (used in example 11.5)
parentlimit.2 (used in example 11.7)
reportedtime.2 (used in example 11.7)
days.stored (used in exercise 11.3)
rotten (used in exercise 11.3)
ERA (used in exercise 11.6)
ESMS (used in exercise 11.6)
depth (used in exercise 11.8)
ammonia (used in exercise 11.8)
food.weight.A (used in exercise 11.9)
weight.gain.A (used in exercise 11.9)
food.weight.B (used in exercise 11.9)
weight.gain.B (used in exercise 11.9)
SW.England (used in exercise 11.10)
N.Scotland (used in exercise 11.10)
Usage
ch11
Format
ch11
A list with 18 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 12
Description
Data used in Chapter 12 of "Applied Nonparametric Statistical Methods" (5th edition)
feedback.freq (used in example 12.1)
PPI.person (used in example 12.1)
infection.site (used in examples 12.2, 12.3)
district (used in examples 12.2, 12.3)
drugYZ (used in example 12.4)
side.effect (used in example 12.4)
drugAB (used in example 12.5)
side.effect.level (used in example 12.5)
time.to.failure (used in example 12.6)
cause (used in example 12.6)
dose (used in examples 12.7, 12.8)
dose.side.effect (used in example 12.7, 12.8)
platelet.count (used in examples 12.9)
spleen.size (used in example 12.9)
last.digits (used in example 12.10)
accidents (used in example 12.11)
accidents.reduced (used in example 12.11)
side.effect.new (used in example 12.12)
side.effect.old (used in example 12.12)
bronchitis (used in exercise 12.1)
otitis.media (used in exercise 12.1)
welsh.language (used in exercise 12.2)
opportunities (used in exercise 12.2)
diagnosis (used in exercise 12.3)
position.played (used in exercise 12.3)
PPI.person.2 (used in exercise 12.4)
feedback.satisfaction (used in exercise 12.4)
win.opinion (used in exercise 12.5)
supporter (used in exercise 12.5)
diabetes.status (used in exercise 12.6)
ethnic.group (used in exercise 12.6)
horse.wins (used in exercise 12.7)
F1.wins (used in exercise 12.8)
strokes (used in exercise 12.9)
recurrent.visits (used in exercise 12.10)
holes (used in exercise 12.11)
first.response (used in exercise 12.12)
second.response (used in exercise 12.12)
Usage
ch12
Format
ch12
A list with 38 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 13
Description
Data used in Chapter 13 of "Applied Nonparametric Statistical Methods" (5th edition)
physical.activity (used in examples 13.1, 13.2, exercise 13.2)
tv.viewing (used in examples 13.1, 13.2, exercise 13.2)
gender (used in examples 13.1, 13.2, exercise 13.2)
machine (used in example 13.3)
output.status (used in example 13.3)
material.source (used in example 13.3)
drug (used in example 13.4, section 13.2.5)
side.effects (used in example 13.4, section 13.2.5)
age.group (used in example 13.4, section 13.2.5)
dose (used in examples 13.7, 13.8)
dose.side.effect (used in examples 13.7, 13.8)
alcohol (used in example 13.9)
malformation (used in example 13.9)
frequency (used in example 13.10)
person (used in example 13.10)
medicine (used in exercise 13.7, section 13.3.1)
response (used in exercise 13.7, section 13.3.1)
location (used in exercise 13.7, section 13.3.1)
chemo.drug (used in example 13.12)
chemo.side.effect (used in example 13.12)
group (used in section 13.4)
promoted (used in section 13.4)
company (used in section 13.4)
breakfast.eaten (used in exercise 13.3)
VEL (used in exercise 13.3)
boys.girls (used in exercise 13.3)
cholesterol (used in exercise 13.4)
SBP (used in exercise 13.4)
schooling (used in exercise 13.5)
abortion.attitude (used in exercise 13.5)
PPI.ages (used in exercise 13.9)
PPI.people (used in exercise 13.9)
laid.off (used in exercises 13.10, 13.11)
employee.ages (used in exercise 13.10)
employee.ages.2 (used in exercise 13.11)
Usage
ch13
Format
ch13
A list with 35 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 14
Description
Data used in Chapter 14 of "Applied Nonparametric Statistical Methods" (5th edition)
example14.2 (used in examples 14.2, 14.5)
X14.4 (used in exercise 14.4)
Y14.4 (used in exercise 14.4)
Usage
ch14
Format
ch14
A list with 3 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 15
Description
Data used in Chapter 15 of "Applied Nonparametric Statistical Methods" (5th edition)
diet (used in section 15.3.5)
BMI (used in section 15.3.1)
wgt.VLCD (used in section 15.3.2)
wgt.norm (used in section 15.3.2)
opdiff (used in section 15.3.5)
optime.VLCD (used in sections 15.3.3, 15.3.6)
optime.norm (used in sections 15.3.3, 15.3.6)
los.VLCD (used in section 15.3.6)
los.norm (used in section 15.3.6)
optime (used in section 15.3.4)
los (used in section 15.3.4)
Usage
ch15
Format
ch15
A list with 11 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 3
Description
Data used in Chapter 3 of "Applied Nonparametric Statistical Methods" (5th edition)
sampleI (used in examples 3.1, 3.2, 3.3, exercise 3.17)
sampleII (used in examples 3.1, 3.2, 3.3, exercise 3.17)
heartrates1 (used in examples 3.4, 3.11)
heartrates2 (used in examples 3.5, 3.6, 3.7)
withties (used in example 3.8)
tiedifrounded1 (used in example 3.8)
tiedifrounded2 (used in example 3.8)
ages (used in example 3.8, exercise 3.9)
sampleA (used in example 3.12)
sampleB (used in examples 3.12, 3.13)
sampleA2 (used in example 3.12)
sampleA3 (used in example 3.12)
heartrates2a (used in example 3.14)
heartrates2b (used in example 3.14)
sampleIa (used in exercise 3.1)
parkingtime (used in exercise 3.3)
Svals (used in exercise 3.4)
children (used in exercise 3.6)
fishlengths (used in exercises 3.7, 3.11)
sleeptime (used in exercise 3.10)
weightloss (used in exercise 3.12)
plants (used in exercise 3.13)
birthprops (used in exercise 3.14)
assembly (used in exercise 3.15)
weightchange (used in exercise 3.16)
Usage
ch3
Format
ch3
A list with 25 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 4
Description
Data used in Chapter 4 of "Applied Nonparametric Statistical Methods" (5th edition)
breaks (used in example 4.2)
ages (used in example 4.4)
precipitation (used in example 4.13)
tosses1 (used in example 4.14)
tosses2 (used in example 4.14)
tosses3 (used in example 4.14)
births (used in example 4.15)
times.as.degrees (used in example 4.16)
dates.as.degrees (used in example 4.17)
waiting.time (used in exercise 4.2)
visiting.supporters (used in exercise 4.3)
days.waiting (used in exercise 4.8)
rainfall.by.latitude (used in exercise 4.9)
points (used in exercise 4.10)
rainfall.DRC (used in exercise 4.11)
piped.water.DRC (used in exercise 4.12)
accident.bearings (used in exercise 4.13)
board.angles (used in exercise 4.14)
arrow.angles (used in exercise 4.15)
football.results (used in exercise 4.17)
Usage
ch4
Format
ch4
A list with 20 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 5
Description
Data used in Chapter 5 of "Applied Nonparametric Statistical Methods" (5th edition)
LVF (used in example 5.1, exercise 6.2)
RVF (used in example 5.1, exercise 6.2)
arithmetic (used in example 5.2)
bp (used in example 5.3)
bp.incorrect (used in example 5.3)
yr0910 (used in example 5.10)
yr1314 (used in example 5.10)
bp.diff (used in exercise 5.1)
LabI (used in exercise 5.2)
LabII (used in exercise 5.2)
parent (used in exercise 5.4)
online (used in exercise 5.5)
lectures (used in exercise 5.5)
additiveA (used in exercise 5.9)
additiveB (used in exercise 5.9)
round2 (used in exercise 5.10)
round3 (used in exercise 5.10)
pollA (used in exercise 5.11)
pollB (used in exercise 5.11)
kHz0.125 (used in exercise 5.12)
kHz0.25 (used in exercise 5.12)
Usage
ch5
Format
ch5
A list with 21 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 6
Description
Data used in Chapter 6 of "Applied Nonparametric Statistical Methods" (5th edition)
groupA (used in examples 6.1, 6.2, 6.3, 6.10, 6.17)
groupB (used in examples 6.1, 6.2, 6.3, 6.10, 6.17)
groupA.sch2 (used in example 6.4)
groupB.sch2 (used in example 6.4)
groupA.sch2.grp (used in example 6.5)
groupB.sch2.grp (used in example 6.5)
males (used in examples 6.7, 6.8)
females (used in examples 6.7, 6.8)
sampleI (used in example 6.9)
sampleII (used in example 6.9)
typeA (used in examples 6.11, 6.12, 6.13, exercises 6.11, 6.12)
typeB (used in examples 6.11, 6.12, 6.13, exercises 6.11, 6.12)
groupI (used in example 6.14)
groupII (used in example 6.14)
groupI.trimmed (used in example 6.14)
groupI.amended (used in example 6.14)
salivaF (used in examples 6.15, 6.16)
salivaM (used in examples 6.15, 6.16)
sex (used in example 6.18)
temp.H (used in exercise 6.1)
temp.L (used in exercise 6.1)
DMF.M (used in exercise 6.3)
DMF.F (used in exercise 6.3)
weight.diabetic (used in exercise 6.4)
weight.normal (used in exercise 6.4)
cooling.time.standard (used in exercise 6.5)
cooling.time.cheap (used in exercise 6.5)
wait.1979 (used in exercise 6.6)
wait.1983 (used in exercise 6.6)
activity.boys (used in exercise 6.7)
activity.girls (used in exercise 6.7)
time.withoutLD (used in exercises 6.13, 6.14)
time.withLD (used in exercises 6.13, 6.14)
doseI (used in exercise 6.15)
doseII (used in exercise 6.15)
doseI.2 (used in exercise 6.15)
travel (used in exercise 6.16)
politics (used in exercise 6.16)
twins (used in exercise 6.17)
Usage
ch6
Format
ch6
A list with 39 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 7
Description
Data used in Chapter 7 of "Applied Nonparametric Statistical Methods" (5th edition)
affordability (used in example 7.1, exercise 7.16)
regions (used in example 7.1, exercise 7.16)
age (used in example 7.2)
positions (used in example 7.2)
dementia.age (used in examples 7.3, 7.9)
features (used in examples 7.3, 7.9)
time (used in examples 7.4, 7.5)
surgeon (used in examples 7.4, 7.5)
pulse (used in example 7.6)
student (used in example 7.6)
time.period (used in example 7.6)
nodes (used in example 7.7)
treatment (used in example 7.7)
block (used in example 7.7)
outcome (used in example 7.8)
member (used in example 7.8)
climb (used in example 7.8)
procedure.time (used in example 7.10)
team.member (used in example 7.10)
sentences (used in exercise 7.2)
author (used in exercise 7.2)
head.width (used in exercise 7.4)
species (used in exercise 7.4)
braking.distance (used in exercise 7.5)
speed (used in exercise 7.5)
platelet.count (used in exercise 7.6)
spleen.size (used in exercise 7.6)
liver.weight (used in exercise 7.7)
dose (used in exercise 7.7)
house (used in exercise 7.7)
mark (used in exercise 7.8)
scheme (used in exercise 7.8)
candidate (used in exercise 7.8)
prem.contractions (used in exercise 7.9)
drug (used in exercise 7.9)
patient (used in exercise 7.9)
births (used in exercise 7.11)
week (used in exercise 7.11)
weekday (used in exercise 7.11)
names.recalled (used in exercise 7.12)
group (used in exercise 7.12)
medical.student (used in exercise 7.12)
soc.media.use (used in exercise 7.14)
participant (used in exercise 7.14)
day (used in exercise 7.14)
braking.distance.2 (used in exercise 7.15)
initial.speed (used in exercise 7.15)
Usage
ch7
Format
ch7
A list with 47 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 8
Description
Data used in Chapter 8 of "Applied Nonparametric Statistical Methods" (5th edition)
plant.weight (used in example 8.2)
growth.hormone (used in examples 8.6, 8.7)
undersoil.heating (used in examples 8.6, 8.7)
plant.weight.2 (used in example 8.6)
plant.weight.3 (used in examples 8.4, 8.5)
plant.weight.4 (used in example 8.7)
sequence (used in example 8.9)
periodI (used in example 8.9)
periodII (used in example 8.9)
sentences (used in example 8.10)
authors (used in example 8.10)
prey.preference (used in example 8.11)
prey (used in example 8.11)
larva (used in example 8.11)
game.time (used in exercise 8.3)
experience (used in exercise 8.3)
game (used in exercise 8.3)
periodI.mistakes.AB (used in exercise 8.6)
periodII.mistakes.AB (used in exercise 8.6)
periodI.mistakes.BA (used in exercise 8.6)
periodII.mistakes.BA (used in exercise 8.6)
periodI.time.AB (used in exercise 8.7)
periodII.time.AB (used in exercise 8.7)
periodI.time.BA (used in exercise 8.7)
periodII.time.BA (used in exercise 8.7)
seizure.score (used in exercises 8.8, 8.9)
hospital (used in exercises 8.8, 8.9)
silver.content (used in exercise 8.10)
dynasty (used in exercise 8.10)
Usage
ch8
Format
ch8
A list with 29 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Data used in Chapter 9
Description
Data used in Chapter 9 of "Applied Nonparametric Statistical Methods" (5th edition)
symp.survtime (used in examples 9.1, 9.3)
symp.censor (used in examples 9.1, 9.3)
asymp.survtime (used in examples 9.1, 9.3)
asymp.censor (used in examples 9.1, 9.3)
sampleI.survtime (used in following example 9.3, example 9.4)
sampleI.censor (used in example 9.4)
sampleII.survtime (used in example 9.4)
sampleII.survtime.2 (used in following example 9.3)
sampleII.censor (used in example 9.4)
samplesAB.survtime (used in example 9.6)
samplesAB.censor (used in example 9.6)
samplesAB (used in example 9.6)
samplesXYZ.survtime (used in example 9.7)
samplesXYZ.censor (used in example 9.7)
samplesXYZ (used in example 9.7)
boys.toothtime (used in exercise 9.2)
girls.toothtime (used in exercise 9.2)
regimeA.survtime (used in exercises 9.5, 9.6)
regimeA.censor (used in exercises 9.5, 9.6)
regimeB.survtime (used in exercises 9.5, 9.6)
regimeB.censor (used in exercises 9.5, 9.6)
bulbA (used in exercise 9.8)
bulbB (used in exercise 9.8)
Usage
ch9
Format
ch9
A list with 23 data vectors
Source
"Applied Nonparametric Statistical Methods" (5th edition)
Perform Chi-squared test
Description
chisqtest.ANSM()
is a wrapper for chisq.test() from the stats
package - performs the Chi-squared test and is used in chapters 12 and 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
chisqtest.ANSM(
x,
y = NULL,
p = NULL,
cont.corr = TRUE,
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.exact = TRUE,
do.asymp = FALSE,
do.mc = FALSE
)
Arguments
x |
Factor of same length as y, or table |
y |
Factor of same length as x (or NULL if x is table) (defaults to |
p |
Vector of probabilities (expressed as numbers between 0 and 1 and summing to 1) of same length as x or NULL (defaults to |
cont.corr |
Boolean indicating whether or not to use continuity correction (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 12.1 from "Applied Nonparametric Statistical Methods" (5th edition)
chisqtest.ANSM(ch12$feedback.freq, ch12$PPI.person, do.exact = FALSE, do.asymp = TRUE)
# Exercise 13.7 from "Applied Nonparametric Statistical Methods" (5th edition)
chisqtest.ANSM(ch13$medicine[ch13$location == "Rural"],
ch13$response[ch13$location == "Rural"], seed = 1)
Perform Cochran Q test
Description
cochran.q()
performs the Cochran Q test and is used in chapter 7 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
cochran.q(
y,
groups,
blocks,
max.exact.perms = 1e+05,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
y |
Binary vector of same length as groups, blocks |
groups |
Factor of same length as y, blocks with levels such that length(y) == nlevels(groups) * nlevels(blocks) |
blocks |
Factor of same length as y, groups with levels such that length(y) == nlevels(groups) * nlevels(blocks) |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 7.8 from "Applied Nonparametric Statistical Methods" (5th edition)
cochran.q(ch7$outcome, ch7$climb, ch7$member, do.exact = FALSE, do.asymp = TRUE)
# Exercise 7.14 from "Applied Nonparametric Statistical Methods" (5th edition)
cochran.q(ch7$soc.media.use, ch7$participant, ch7$day, do.exact = FALSE, do.asymp = TRUE)
Calculate Cohen's kappa
Description
cohen.kappa()
calculates Cohen's kappa and is used in chapter 10 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
cohen.kappa(
y1,
y2,
blocks = NULL,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = FALSE,
do.mc = FALSE
)
Arguments
y1 |
Factor of same length as y2, blocks and same levels as y2 and (if blocks not NULL) with 2 levels |
y2 |
Factor of same length as y1, blocks and same levels as y1 and (if blocks not NULL) with 2 levels |
blocks |
Factor of same length as y1, y2 or NULL (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Example 10.11 from "Applied Nonparametric Statistical Methods" (5th edition)
cohen.kappa(ch10$dentistA, ch10$dentistB, do.asymp = TRUE, do.exact = FALSE,
alternative = "greater")
# Example 10.12 from "Applied Nonparametric Statistical Methods" (5th edition)
cohen.kappa(ch10$questionnaire, ch10$demonstration, ch10$items)
Perform Conover test using standard or squared ranks
Description
conover()
performs the Conover test using standard or squared ranks and is used in chapters 6 and 7 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
conover(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
abs.ranks = FALSE,
max.exact.perms = 5e+06,
nsims.mc = 10000,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.mc = FALSE
)
Arguments
x |
Numeric vector of same length as y |
y |
Factor of same length as x |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
abs.ranks |
Boolean indicating whether absolute ranks to be used instead of squared ranks (defaults to |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 6.13 from "Applied Nonparametric Statistical Methods" (5th edition)
conover(ch6$typeA, ch6$typeB, do.exact = FALSE, do.asymp = TRUE)
# Exercise 7.15 from "Applied Nonparametric Statistical Methods" (5th edition)
conover(ch7$braking.distance.2, ch7$initial.speed, do.exact = FALSE, do.asymp = TRUE)
Perform Control median test
Description
control.median()
performs the Control median test and is used in chapters 6 and 9 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
control.median(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.cases = 1000,
nsims.mc = 10000,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = TRUE
)
Arguments
x |
Numeric vector |
y |
Numeric vector |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 6.9 from "Applied Nonparametric Statistical Methods" (5th edition)
control.median(ch6$sampleI, ch6$sampleII, alternative = "greater")
# Exercise 9.8 from "Applied Nonparametric Statistical Methods" (5th edition)
control.median(ch9$bulbA, ch9$bulbB, alternative = "greater", nsims = 1000)
Perform Cox-Stuart test
Description
cox.stuart()
performs the Cox-Stuart test and is used in chapters 4 and 10 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
cox.stuart(
x,
alternative = c("two.sided", "less", "greater"),
cont.corr = TRUE,
max.exact.cases = 1e+07,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Numeric vector |
alternative |
Type of alternative hypothesis (defaults to |
cont.corr |
Boolean indicating whether or not to use continuity correction (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 4.13 from "Applied Nonparametric Statistical Methods" (5th edition)
cox.stuart(ch4$precipitation)
# Exercise 10.5 from "Applied Nonparametric Statistical Methods" (5th edition)
cox.stuart(app1$McDelta[order(ch10$death.year)], alternative = "less")
Perform Cramer-von Mises test
Description
cramer.von.mises()
performs the Cramer-von Mises test and is used in chapter 6 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
cramer.von.mises(x, y, alternative = c("two.sided", "less", "greater"))
Arguments
x |
Numeric vector |
y |
Numeric vector |
alternative |
Type of alternative hypothesis (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 6.16 from "Applied Nonparametric Statistical Methods" (5th edition)
cramer.von.mises(ch6$salivaF, ch6$salivaM)
cramer.von.mises(ch6$salivaF, ch6$salivaM, alternative = "greater")
Perform Fisher exact test
Description
fishertest.ANSM()
is a wrapper for fisher.test() from the stats
package - performs the Fisher exact test and is used in chapters 6, 12 and 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
fishertest.ANSM(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 10000,
do.exact = TRUE
)
Arguments
x |
Numeric vector or factor |
y |
Numeric vector or factor |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 6.7 from "Applied Nonparametric Statistical Methods" (5th edition)
fishertest.ANSM(ch6$males, ch6$females)
# Exercise 13.10 from "Applied Nonparametric Statistical Methods" (5th edition)
fishertest.ANSM(ch13$laid.off, ch13$employee.ages)
Perform Friedman test
Description
friedman()
performs the Friedman test and is used in chapter 7 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
friedman(
y,
groups,
blocks,
use.Iman.Davenport = FALSE,
max.exact.perms = 1e+05,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
y |
Numeric vector of same length as groups, blocks |
groups |
Factor of same length as y, blocks with levels such that length(y) == nlevels(groups) * nlevels(blocks) |
blocks |
Factor of same length as y, groups with levels such that length(y) == nlevels(groups) * nlevels(blocks) |
use.Iman.Davenport |
Boolean indicating whether or not to use Iman and Davenport approximation (defaults to |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 7.6 from "Applied Nonparametric Statistical Methods" (5th edition)
friedman(ch7$pulse, ch7$time.period, ch7$student, do.exact = FALSE, do.asymp = TRUE)
# Exercise 7.12 from "Applied Nonparametric Statistical Methods" (5th edition)
friedman(ch7$names.recalled, ch7$group, ch7$medical.student, use.Iman.Davenport = TRUE,
do.exact = FALSE, do.asymp = TRUE)
Perform Least Significant Differences test after the Friedman test
Description
friedman.lsd()
performs the Least Significant Differences test after the Friedman test and is used in chapter 8 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
friedman.lsd(y, groups, blocks, ids)
Arguments
y |
Numeric vector of same length as groups, blocks |
groups |
Factor of same length as y, blocks with levels such that length(y) == nlevels(groups) * nlevels(blocks) |
blocks |
Factor of same length as y, groups with levels such that length(y) == nlevels(groups) * nlevels(blocks) |
ids |
Vector of length 2 with elements both levels of groups |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 8.11 from "Applied Nonparametric Statistical Methods" (5th edition)
friedman.lsd(ch8$prey.preference, ch8$prey, ch8$larva, c("Cyclops", "Anopheles"))
# from "Applied Nonparametric Statistical Methods" (5th edition)
Perform Gehan-Wilcoxon test
Description
gehan.wilcoxon()
performs the Gehan-Wilcoxon test and is used in chapter 9 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
gehan.wilcoxon(
x,
y,
x.c,
y.c,
alternative = c("two.sided", "less", "greater"),
max.exact.perms = 1e+05,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Numeric vector of same length as y, x.c, y.c |
y |
Numeric vector of same length as x, x.c, y.c |
x.c |
Binary vector of same length as x, y, x.c |
y.c |
Binary vector of same length as x, y, y.c |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 9.1 from "Applied Nonparametric Statistical Methods" (5th edition)
gehan.wilcoxon(ch9$symp.survtime, ch9$asymp.survtime,
ch9$symp.censor, ch9$asymp.censor, alternative = "less",
do.exact = FALSE, do.asymp = TRUE)
# Exercise 9.5 from "Applied Nonparametric Statistical Methods" (5th edition)
gehan.wilcoxon(ch9$regimeA.survtime, ch9$regimeB.survtime,
ch9$regimeA.censor, ch9$regimeB.censor, do.exact = FALSE, do.asymp = TRUE)
Perform Hettmansperger and Elmore interaction test
Description
hettmansperger.elmore()
performs the Hettmansperger and Elmore interaction test and is used in chapter 8 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
hettmansperger.elmore(
y,
factor.a,
factor.b,
nsims.mc = 1000,
seed = NULL,
do.asymp = TRUE,
do.mc = FALSE,
median.polish = FALSE
)
Arguments
y |
Numeric vector of same length as factor.a, factor.b |
factor.a |
Factor of same length as y, factor.b |
factor.b |
Factor of same length as y, factor.a |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
median.polish |
Boolean indicating whether or not to use median polish (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 8.6 from "Applied Nonparametric Statistical Methods" (5th edition)
hettmansperger.elmore(ch8$plant.weight.2, ch8$growth.hormone, ch8$undersoil.heating)
# Exercise 8.3 from "Applied Nonparametric Statistical Methods" (5th edition)
hettmansperger.elmore(ch8$game.time, ch8$experience, ch8$game)
Perform Hodges-Ajne test
Description
hodges.ajne()
performs the Hodges-Ajne test and is used in chapter 4 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
hodges.ajne(x, alternative = c("two.sided"), minx = 0, maxx = 360)
Arguments
x |
Numeric vector |
alternative |
Type of alternative hypothesis (defaults to |
minx |
Minimum value for x (defaults to |
maxx |
Maximum value for x (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 4.16 from "Applied Nonparametric Statistical Methods" (5th edition)
hodges.ajne(ch4$times.as.degrees)
# Exercise 4.14 from "Applied Nonparametric Statistical Methods" (5th edition)
hodges.ajne(ch4$board.angles)
Perform Jonckheere-Terpstra test
Description
jonckheere.terpstra()
performs the Jonckheere-Terpstra test and is used in chapters 7, 8 and 12 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
jonckheere.terpstra(
x,
g,
alternative = c("less", "greater"),
max.exact.cases = 15,
nsims.mc = 10000,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.mc = FALSE,
do.asymp.ties.adjust = TRUE
)
Arguments
x |
Numeric vector or factor of same length as g |
g |
Factor of same length as x |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
do.asymp.ties.adjust |
Boolean indicating whether or not to use adjustment for ties in data (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 7.3 from "Applied Nonparametric Statistical Methods" (5th edition)
jonckheere.terpstra(ch7$dementia.age, ch7$features, alternative = "greater",
do.exact = FALSE, do.asymp = TRUE, do.asymp.ties.adjust = FALSE)
# Exercise 12.6 from "Applied Nonparametric Statistical Methods" (5th edition)
jonckheere.terpstra(ch12$ethnic.group, ch12$diabetes.status, do.exact = FALSE, do.asymp = TRUE)
Calculate Kendall's concordance
Description
kendall.concordance()
calculates Kendall's concordance and is used in chapter 10 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
kendall.concordance(
y,
groups,
blocks,
max.exact.perms = 1e+05,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
y |
Numeric vector of same length as groups, blocks |
groups |
Factor of same length as y, blocks with levels such that length(y) == nlevels(groups) * nlevels(blocks) |
blocks |
Factor of same length as y, groups with levels such that length(y) == nlevels(groups) * nlevels(blocks) |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Exercise 10.11 from "Applied Nonparametric Statistical Methods" (5th edition)
kendall.concordance(ch10$marks, ch10$script, ch10$examiner, do.exact = FALSE, do.asymp = TRUE)
kendall.concordance(ch10$marks, ch10$examiner, ch10$script, do.exact = FALSE, do.asymp = TRUE)
Perform Kendall's tau
Description
kendall.tau()
performs the Kendall's tau and is used in chapter 10 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
kendall.tau(
x,
y,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.mc = FALSE
)
Arguments
x |
Numeric vector of same length as y |
y |
Numeric vector of same length as x |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Example 10.8 from "Applied Nonparametric Statistical Methods" (5th edition)
kendall.tau(ch10$death.year, app1$McDelta, alternative = "greater",
do.asymp = TRUE, do.exact = FALSE)
# Example 10.9 from "Applied Nonparametric Statistical Methods" (5th edition)
kendall.tau(ch10$Canadian, ch10$Australian)
Perform Kruskal-Wallis test
Description
kruskal.wallis()
performs the Kruskal-Wallis test and is used in chapters 7 and 12 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
kruskal.wallis(
x,
g,
max.exact.cases = 15,
nsims.mc = 10000,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.mc = FALSE
)
Arguments
x |
Numeric vector or factor of same length as g |
g |
Factor of same length as x |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 7.1 from "Applied Nonparametric Statistical Methods" (5th edition)
kruskal.wallis(ch7$affordability, ch7$regions, do.exact = FALSE, do.asymp = TRUE)
# Exercise 7.16 from "Applied Nonparametric Statistical Methods" (5th edition)
kruskal.wallis(ch7$affordability, ch7$regions)
Perform Least Significant Differences test after the Kruskal-Wallis test
Description
kruskal.wallis.lsd()
performs the Least Significant Differences test after the Kruskal-Wallis test and is used in chapter 8 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
kruskal.wallis.lsd(x, g, ids)
Arguments
x |
Numeric vector of same length as g |
g |
Factor of same length as x |
ids |
Vector of length 2 with elements both levels of g |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 8.10 from "Applied Nonparametric Statistical Methods" (5th edition)
kruskal.wallis.lsd(ch8$sentences, ch8$authors, c("Vulliamy", "Queen"))
# Exercise 8.8 from "Applied Nonparametric Statistical Methods" (5th edition)
kruskal.wallis.lsd(ch8$seizure.score, ch8$hospital, c("HospitalA", "HospitalC"))
Perform Kruskal-Wallis test with van der Waerden scores
Description
kruskal.wallis.vdW()
performs the Kruskal-Wallis test with van der Waerden scores and is used in chapter 7 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
kruskal.wallis.vdW(
x,
g,
max.exact.cases = 15,
nsims.mc = 10000,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Numeric vector of same length as g |
g |
Factor of same length as x |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 7.2 from "Applied Nonparametric Statistical Methods" (5th edition)
kruskal.wallis.vdW(ch7$age, ch7$positions)
kruskal.wallis.vdW(ch7$age, ch7$positions, do.exact = FALSE, do.asymp = TRUE)
Perform Smirnov test and Kolgomorov test
Description
kstest.ANSM()
is a wrapper for ks.test() from the stats
package - performs the Smirnov test and Kolgomorov test and is used in chapters 4, 6 and 9 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
kstest.ANSM(
x,
y,
...,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 1000,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Numeric vector |
y |
Numeric vector or a character string naming a cumulative distribution function or an actual cumulative distribution function |
... |
For the default method of |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Exercise 4.3 from "Applied Nonparametric Statistical Methods" (5th edition)
kstest.ANSM(ch4$visiting.supporters, "pexp", rate = 2600)
# Exercise 9.2 from "Applied Nonparametric Statistical Methods" (5th edition)
kstest.ANSM(ch9$boys.toothtime, ch9$girls.toothtime)
Perform Likelihood ratio test
Description
lik.ratio()
performs the Likelihood ratio test and is used in chapters 12 and 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
lik.ratio(
x,
y,
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.exact = TRUE,
do.asymp = FALSE,
do.mc = FALSE
)
Arguments
x |
Factor of same length as y |
y |
Factor of same length as x |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 12.2 from "Applied Nonparametric Statistical Methods" (5th edition)
lik.ratio(ch12$infection.site, ch12$district, do.exact = FALSE, do.asymp = TRUE)
# Example 13.12 from "Applied Nonparametric Statistical Methods" (5th edition)
chemo.side.effect.3 <- ch13$chemo.side.effect
levels(chemo.side.effect.3) <- list("Side-effect" = c("Hair loss",
"Visual impairment", "Hair loss & Visual impairment"), "None" = "None")
lik.ratio(ch13$chemo.drug, chemo.side.effect.3, seed = 1)
Performs Lilliefors test of Normality
Description
lilliefors()
performs Lilliefors test of Normality and is used in chapters 4, 5 and 6 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
lilliefors(x, alternative = c("two.sided"), nsims.mc = 10000, seed = NULL)
Arguments
x |
Numeric vector |
alternative |
Type of alternative hypothesis (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 4.4 from "Applied Nonparametric Statistical Methods" (5th edition)
lilliefors(ch4$ages, seed = 1)
# Exercise 6.15 from "Applied Nonparametric Statistical Methods" (5th edition)
lilliefors(ch6$doseI.2, seed = 1, nsims = 1000)
Perform Linear by linear association test
Description
linear.by.linear()
performs the Linear by linear association test and is used in chapter 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
linear.by.linear(
x,
y,
u = NULL,
v = NULL,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.mc = TRUE
)
Arguments
x |
Factor of same length as y |
y |
Factor of same length as x |
u |
Numeric vector of length equal to number of levels of x or NULL (defaults to |
v |
Numeric vector of length equal to number of levels of y or NULL (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 13.8 from "Applied Nonparametric Statistical Methods" (5th edition)
linear.by.linear(ch13$dose, ch13$dose.side.effect, do.mc = FALSE, do.asymp = TRUE)
# Exercise 13.4 from "Applied Nonparametric Statistical Methods" (5th edition)
linear.by.linear(ch13$SBP, ch13$cholesterol, seed = 1)
Perform Log odds ratio test
Description
logoddsratio.2x2()
performs the Log odds ratio test and is used in chapter 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
logoddsratio.2x2(
x,
y,
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.exact = TRUE,
do.asymp = FALSE,
do.mc = FALSE
)
Arguments
x |
Binary factor of same length as y |
y |
Binary factor of same length as x |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Exercise 13.2 from "Applied Nonparametric Statistical Methods" (5th edition)
#logoddsratio.2x2(ch13$physical.activity[ch13$gender == "Boy"],
# ch13$tv.viewing[ch13$gender == "Boy"], do.exact = FALSE, do.asymp = TRUE)
#logoddsratio.2x2(ch13$physical.activity[ch13$gender == "Girl"],
# ch13$tv.viewing[ch13$gender == "Girl"], do.exact = FALSE, do.asymp = TRUE)
Perform logrank test
Description
logrank()
performs the logrank test and is used in chapter 9 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
logrank(
x,
censored,
groups,
score.censored = TRUE,
max.exact.perms = 1e+05,
nsims.mc = 10000,
seed = NULL
)
Arguments
x |
Numeric vector of same length as censored, groups |
censored |
Binary vector of same length as x, groups |
groups |
Factor of same length as x, censored |
score.censored |
Boolean indicating whether or not to score censored values (defaults to |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 9.6 from "Applied Nonparametric Statistical Methods" (5th edition)
logrank(ch9$samplesAB.survtime, ch9$samplesAB.censor, ch9$samplesAB, score.censored = FALSE)
# Exercise 9.7 from "Applied Nonparametric Statistical Methods" (5th edition)
logrank(ch9$samplesXYZ.survtime, ch9$samplesXYZ.censor, ch9$samplesXYZ)
Perform Mantel-Haenszel test
Description
mantel.haenszel()
performs the Mantel-Haenszel test and is used in chapter 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
mantel.haenszel(x, y, z, do.asymp = TRUE)
Arguments
x |
Binary factor of same length as y, z |
y |
Binary factor of same length as x, z |
z |
Factor of same length as x, y |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 13.4 from "Applied Nonparametric Statistical Methods" (5th edition)
mantel.haenszel(ch13$drug, ch13$side.effects, ch13$age.group)
# from "Applied Nonparametric Statistical Methods" (5th edition)
Perform Median test
Description
med.test()
performs the Median test and is used in chapters 6 and 7 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
med.test(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.cases = 1000,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = TRUE
)
Arguments
x |
Numeric vector of same length as y |
y |
Numeric vector, or factor of same length as x |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 6.7 from "Applied Nonparametric Statistical Methods" (5th edition)
med.test(ch6$males, ch6$females)
# Example 7.5 from "Applied Nonparametric Statistical Methods" (5th edition)
med.test(ch7$time, ch7$surgeon, do.exact = FALSE, do.asymp = TRUE)
Perform Mood test
Description
mood()
performs the Mood test and is used in chapter 6 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
mood(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 25,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Numeric vector |
y |
Numeric vector |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 6.12 from "Applied Nonparametric Statistical Methods" (5th edition)
mood(ch6$typeA, ch6$typeB)
mood(ch6$typeA, ch6$typeB, do.exact = FALSE, do.asymp = TRUE)
Perform Moses test for extreme reactions
Description
moses.extreme.reactions()
performs the Moses test for extreme reactions and is used in chapter 6 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
moses.extreme.reactions(
x,
y,
H0 = NULL,
max.exact.cases = 1000,
do.exact = TRUE
)
Arguments
x |
Numeric vector |
y |
Numeric vector |
H0 |
Null hypothesis value (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 6.14 from "Applied Nonparametric Statistical Methods" (5th edition)
moses.extreme.reactions(ch6$groupI.amended, ch6$groupII)
moses.extreme.reactions(ch6$groupI.amended, ch6$groupII)
Calculate Noether approximation
Description
noether()
calculates the Noether approximation and is used in chapter 5 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
noether(p1, alpha = 0.05, power = 0.9)
Arguments
p1 |
Probability (expressed as a number between 0 and 1) |
alpha |
Level of significance (expressed as number between 0 and 1) (defaults to |
power |
Power (expressed as number between 0 and 1) (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Exercise 5.8 from "Applied Nonparametric Statistical Methods" (5th edition)
noether(p1 = 0.7534, alpha = 0.05, power = 0.9)
# Exercise 5.16 from "Applied Nonparametric Statistical Methods" (5th edition)
noether(p1 = 0.8, alpha = 0.025, power = 0.9)
Perform Normal Scores test
Description
normal.scores.test()
performs the Normal Scores test and is used in chapters 6 and 8 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
normal.scores.test(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 25,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Numeric vector |
y |
Numeric vector |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 5.8 from "Applied Nonparametric Statistical Methods" (5th edition)
normal.scores.test(ch6$groupA, ch6$groupB, do.exact = FALSE, do.asymp = TRUE)
# Exercise 6.15 from "Applied Nonparametric Statistical Methods" (5th edition)
normal.scores.test(ch6$doseI, ch6$doseII)
Perform test for difference in odds ratios
Description
oddsratio.2x2diff()
performs the test for difference in odds ratios and is used in chapter 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
oddsratio.2x2diff(
x,
y,
z,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.perms = 1e+06,
nsims.mc = 1e+05,
seed = NULL,
do.exact = TRUE,
do.asymp = FALSE,
do.mc = FALSE,
do.CI = TRUE
)
Arguments
x |
Binary factor of same length as y, z |
y |
Binary factor of same length as x, z |
z |
Binary factor of same length as x, y |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 13.2 from "Applied Nonparametric Statistical Methods" (5th edition)
oddsratio.2x2diff(ch13$physical.activity, ch13$tv.viewing, ch13$gender,
do.exact = FALSE, do.asymp = TRUE)
oddsratio.2x2diff(ch13$physical.activity, ch13$tv.viewing, ch13$gender,
do.exact = FALSE, do.mc = TRUE, seed = 1, nsims = 10000)
Calculate Pearson correlation
Description
pearson()
calculates the Pearson correlation and is used in chapters 10 and 11 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
pearson(
x,
y,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.mc = FALSE
)
Arguments
x |
Numeric vector of same length as y |
y |
Numeric vector of same length as x |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Section 10.1.2 from "Applied Nonparametric Statistical Methods" (5th edition)
pearson(ch10$q1, ch10$q2, alternative = "greater", do.asymp = TRUE, do.exact = FALSE)
# Example 11.2 from "Applied Nonparametric Statistical Methods" (5th edition)
pearson(ch11$parentlimit, ch11$reportedtime - 1 * ch11$parentlimit, alternative = "two.sided")
Calculate Pearson beta
Description
pearson.beta()
calculates the Pearson beta and is used in chapter 11 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
pearson.beta(
y,
x,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = FALSE,
do.mc = FALSE
)
Arguments
y |
Numeric vector of same length as x |
x |
Numeric vector of same length as y |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Example 11.2 from "Applied Nonparametric Statistical Methods" (5th edition)
pearson.beta(ch11$reportedtime, ch11$parentlimit, H0 = 1)
pearson.beta(ch11$reportedtime[1:6], ch11$parentlimit[1:6], H0 = 1)
Perform Peto-Wilcoxon test
Description
peto.wilcoxon()
performs the Peto-Wilcoxon test and is used in chapter 9 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
peto.wilcoxon(
x,
y,
x.c,
y.c,
alternative = c("two.sided", "less", "greater"),
max.exact.perms = 1e+05,
nsims.mc = 10000,
seed = NULL
)
Arguments
x |
Numeric vector of same length as y, x.c, y.c |
y |
Numeric vector of same length as x, x.c, y.c |
x.c |
Binary vector of same length as x, y, x.c |
y.c |
Binary vector of same length as x, y, y.c |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 9.4 from "Applied Nonparametric Statistical Methods" (5th edition)
peto.wilcoxon(ch9$sampleI.survtime, ch9$sampleII.survtime,
ch9$sampleI.censor, ch9$sampleII.censor, alternative = "less")
Perform Pitman test
Description
pitman()
performs the Pitman test and is used in chapter 3 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
pitman(
x,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.cases = 1000,
nsims.mc = 10000,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = TRUE
)
Arguments
x |
Numeric vector |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 3.11 from "Applied Nonparametric Statistical Methods" (5th edition)
pitman(ch3$heartrates1, 70, "greater", do.exact = FALSE, do.asymp = TRUE)
# Exercise 3.17 from "Applied Nonparametric Statistical Methods" (5th edition)
pitman(ch3$sampleII, 110, do.exact = FALSE, do.asymp = TRUE)
Prints an ANSMstat object
Description
print.ANSMstat()
prints the output contained in an ANSMstat object
Usage
## S3 method for class 'ANSMstat'
print(x, ...)
Arguments
x |
An ANSMstat object |
... |
Further arguments relevant to the default |
Value
No return value, called to display results
Prints an ANSMtest object
Description
print.ANSMtest()
prints the output contained in an ANSMtest object
Usage
## S3 method for class 'ANSMtest'
print(x, ...)
Arguments
x |
An ANSMtest object |
... |
Further arguments relevant to the default |
Value
No return value, called to display results
Perform Range test
Description
rng.test()
performs the Range test and is used in chapter 4 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
rng.test(x, alternative = c("two.sided"), minx = 0, maxx = 360)
Arguments
x |
Numeric vector |
alternative |
Type of alternative hypothesis (defaults to |
minx |
Minimum value for x (defaults to |
maxx |
Maximum value for x (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 4.17 from "Applied Nonparametric Statistical Methods" (5th edition)
rng.test(ch4$dates.as.degrees)
# Exercise 4.13 from "Applied Nonparametric Statistical Methods" (5th edition)
rng.test(ch4$accident.bearings)
Perform Runs test for two categories
Description
runs.2cat()
performs the Runs test for two categories and is used in chapters 4, 5 and 6 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
runs.2cat(
x,
alternative = c("two.sided", "less", "greater"),
cont.corr = TRUE,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Vector with two unique values |
alternative |
Type of alternative hypothesis (defaults to |
cont.corr |
Boolean indicating whether or not to use continuity correction (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 4.14 from "Applied Nonparametric Statistical Methods" (5th edition)
runs.2cat(ch4$tosses1, do.exact = FALSE, do.asymp = TRUE)
# Exercise 6.17 from "Applied Nonparametric Statistical Methods" (5th edition)
runs.2cat(ch6$twins, alternative = "greater")
Perform Runs test for three or more categories
Description
runs.ncat()
performs the Runs test for three or more categories and is used in chapters 4 and 7 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
runs.ncat(
x,
alternative = c("two.sided", "less", "greater"),
cont.corr = TRUE,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = TRUE,
do.mc = FALSE
)
Arguments
x |
Vector or factor |
alternative |
Type of alternative hypothesis (defaults to |
cont.corr |
Boolean indicating whether or not to use continuity correction (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 4.15 from "Applied Nonparametric Statistical Methods" (5th edition)
runs.ncat(ch4$births, alternative = "less")
# Exercise 7.16 from "Applied Nonparametric Statistical Methods" (5th edition)
runs.ncat(ch7$regions[order(ch7$affordability)], alternative = "less")
Perform Sign test
Description
sgn.test()
performs the Sign test and is used in chapters 3, 4, 5 and 6 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
sgn.test(
x,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
cont.corr = TRUE,
CI.width = 0.95,
max.exact.cases = 1e+06,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = TRUE
)
Arguments
x |
Numeric vector, or binary factor and H0 is NULL |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
cont.corr |
Boolean indicating whether or not to use continuity correction (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 3.1 from "Applied Nonparametric Statistical Methods" (5th edition)
sgn.test(ch3$sampleI, 110)
# Exercise 6.2 from "Applied Nonparametric Statistical Methods" (5th edition)
sgn.test(ch5$LVF - ch5$RVF, 0)
Perform Shapiro-Wilk test of Normality
Description
shapirotest.ANSM()
is a wrapper for shapiro.test() from the stats
package - performs the Shapiro-Wilk test of Normality and is used in chapters 4 and 5 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
shapirotest.ANSM(x, alternative = c("two.sided"))
Arguments
x |
Numeric vector |
alternative |
Type of alternative hypothesis (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 4.4 from "Applied Nonparametric Statistical Methods" (5th edition)
shapirotest.ANSM(ch4$ages)
# Example 5.3 from "Applied Nonparametric Statistical Methods" (5th edition)
shapirotest.ANSM(ch5$bp.incorrect)
Perform Siegel-Tukey test
Description
siegel.tukey()
performs the Siegel-Tukey test using mean or median shift and is used in chapter 6 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
siegel.tukey(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
mean.shift = FALSE,
cont.corr = TRUE,
max.exact.cases = 1000,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE
)
Arguments
x |
Numeric vector |
y |
Numeric vector |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
mean.shift |
Boolean indicating whether mean shift to be used instead of median shift (defaults to |
cont.corr |
Boolean indicating whether or not to use continuity correction (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Exercise 6.11 from "Applied Nonparametric Statistical Methods" (5th edition)
siegel.tukey(ch6$typeA, ch6$typeB, mean.shift = TRUE)
# Exercise 6.16 from "Applied Nonparametric Statistical Methods" (5th edition)
siegel.tukey(ch6$travel, ch6$politics)
Calculate Spearman correlation
Description
spearman()
calculates the Spearman correlation and is used in chapter 10 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
spearman(
x,
y,
alternative = c("two.sided", "less", "greater"),
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.mc = FALSE
)
Arguments
x |
Numeric vector of same length as y |
y |
Numeric vector of same length as x |
alternative |
Type of alternative hypothesis (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Example 10.2 from "Applied Nonparametric Statistical Methods" (5th edition)
spearman(ch10$q1, ch10$q2, alternative = "greater", do.asymp = TRUE, do.exact = FALSE)
# Exercise 10.1 from "Applied Nonparametric Statistical Methods" (5th edition)
spearman(ch10$ERA, ch10$ESMS, do.exact = FALSE)
Calculate Spearman beta
Description
spearman.beta()
calculates the Spearman beta and is used in chapter 11 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
spearman.beta(
y,
x,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = FALSE,
do.mc = FALSE
)
Arguments
y |
Numeric vector of same length as x |
x |
Numeric vector of same length as y |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Example 11.3 from "Applied Nonparametric Statistical Methods" (5th edition)
spearman.beta(ch11$reportedtime, ch11$parentlimit, H0 = 1)
spearman.beta(ch11$reportedtime, ch11$parentlimit, H0 = 1, do.CI = TRUE)
Calculate Theil-Kendall beta
Description
theil.kendall()
calculates the Theil-Kendall beta and is used in chapter 11 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
theil.kendall(
y,
x,
H0 = NULL,
do.abbreviated = FALSE,
do.alpha = FALSE,
alternative = c("two.sided", "less", "greater"),
CI.width = 0.95,
max.exact.cases = 10,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = FALSE,
do.mc = FALSE
)
Arguments
y |
Numeric vector of same length as x |
x |
Numeric vector of same length as y |
H0 |
Null hypothesis value (defaults to |
do.abbreviated |
Boolean indicating whether or not to use abbreviated Theil procedure (defaults to |
do.alpha |
Boolean indicating whether or not to report estimate of alpha (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
Value
An ANSMstat object with the results from applying the function
Examples
# Example 11.6 from "Applied Nonparametric Statistical Methods" (5th edition)
theil.kendall(ch11$reportedtime, ch11$parentlimit, do.alpha = TRUE)
# Exercise 11.10 from "Applied Nonparametric Statistical Methods" (5th edition)
theil.kendall(ch11$N.Scotland, ch11$SW.England)
Perform Wilcoxon-Mann-Whitney test
Description
wilcoxon.mann.whitney()
performs the Wilcoxon-Mann-Whitney test and is used in chapters 6, 8, 9 and 12 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
wilcoxon.mann.whitney(
x,
y,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
cont.corr = TRUE,
CI.width = 0.95,
max.exact.cases = 1000,
nsims.mc = 1e+05,
seed = NULL,
do.asymp = FALSE,
do.exact = TRUE,
do.mc = FALSE,
do.CI = TRUE
)
Arguments
x |
Numeric vector, or factor with same levels as y |
y |
Numeric vector, or factor with same levels as x |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
cont.corr |
Boolean indicating whether or not to use continuity correction (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
nsims.mc |
Number of Monte Carlo simulations to be performed (defaults to |
seed |
Random number seed to be used for Monte Carlo simulations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.mc |
Boolean indicating whether or not to perform Monte Carlo calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Examples 6.1 and 6.2 from "Applied Nonparametric Statistical Methods" (5th edition)
wilcoxon.mann.whitney(ch6$groupA, ch6$groupB)
# Exercise 12.4 from "Applied Nonparametric Statistical Methods" (5th edition)
wilcoxon.mann.whitney(ch12$feedback.satisfaction[ch12$PPI.person.2 == "Representative"],
ch12$feedback.satisfaction[ch12$PPI.person.2 == "Researcher"],
do.exact = FALSE, do.asymp = TRUE)
Perform Wilcoxon signed-rank test
Description
wilcoxon.signedrank()
performs the Wilcoxon signed-rank test and is used in chapters 3, 4 and 5 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
wilcoxon.signedrank(
x,
H0 = NULL,
alternative = c("two.sided", "less", "greater"),
cont.corr = TRUE,
CI.width = 0.95,
max.exact.cases = 1000,
do.asymp = FALSE,
do.exact = TRUE,
do.CI = TRUE
)
Arguments
x |
Numeric vector |
H0 |
Null hypothesis value (defaults to |
alternative |
Type of alternative hypothesis (defaults to |
cont.corr |
Boolean indicating whether or not to use continuity correction (defaults to |
CI.width |
Confidence interval width (defaults to |
max.exact.cases |
Maximum number of cases allowed for exact calculations (defaults to |
do.asymp |
Boolean indicating whether or not to perform asymptotic calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
do.CI |
Boolean indicating whether or not to perform confidence interval calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Example 3.4 from "Applied Nonparametric Statistical Methods" (5th edition)
wilcoxon.signedrank(ch3$heartrates1, 70, "greater")
# Exercise 5.12 from "Applied Nonparametric Statistical Methods" (5th edition)
wilcoxon.signedrank(ch5$kHz0.125 - ch5$kHz0.25, 0)
Perform Zelen test
Description
zelen()
performs the Zelen test and is used in chapter 13 of "Applied Nonparametric Statistical Methods" (5th edition)
Usage
zelen(x, y, z, max.exact.perms = 1e+06, do.exact = TRUE)
Arguments
x |
Binary factor of same length as y, z |
y |
Binary factor of same length as x, z |
z |
Factor of same length as x, y |
max.exact.perms |
Maximum number of permutations allowed for exact calculations (defaults to |
do.exact |
Boolean indicating whether or not to perform exact calculations (defaults to |
Value
An ANSMtest object with the results from applying the function
Examples
# Section 13.2.5 from "Applied Nonparametric Statistical Methods" (5th edition)
zelen(ch13$drug, ch13$side.effects, ch13$age.group)
# Example 13.3 from "Applied Nonparametric Statistical Methods" (5th edition)
zelen(ch13$machine, ch13$output.status, ch13$material.source)