These functions perform either: two-sample permutation tests (permTS),
k-sample permutation tests (permKS), or trend permutation tests (permTREND).
The test function can be transformed to a linear function of the scores times the covariate, where the covariate
may be either a factor or character vector with two (permTS) or more (permKS) levels or a
numeric vector (permTREND). By using suitable scores one can create
for example, the permutation t-test (general scores), the Wilcoxon rank sum test (rank scores),
the logrank test (need to use other functions to create these scores). It performs either
exact (network algorithm, complete enumeration, or Monte Carlo) asymptotic calculations (using permutational
central limit theorem).
permTS(x, …)# S3 method for default
permTS(x, y, alternative = c("two.sided", "less", "greater"),
exact = NULL, method = NULL, methodRule = methodRuleTS1,
control=permControl(), …)
# S3 method for formula
permTS(formula, data, subset, na.action, …)
permKS(x,…)
# S3 method for default
permKS(x, g, exact = NULL, method = NULL, methodRule = methodRuleKS1, control=permControl(), ...)
# S3 method for formula
permKS(formula,data,subset, na.action,…)
permTREND(x,…)
# S3 method for default
permTREND(x, y, alternative = c("two.sided", "less", "greater"), exact = NULL, method = NULL, methodRule = methodRuleTREND1, control=permControl(),...)
# S3 method for formula
permTREND(formula,data,subset,na.action,…)
numeric vector of respose scores for the first group
numeric vector of either response scores for the second group (for permTS) or trend scores for each observation (for permTREND)
a factor or character vector denoting group membership
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater","less" (see details)
a logical value, TRUE denotes exact test, ignored if method is not NULL
a character value, one of 'pclt','exact.network','exact.ce','exact.mc'. If NULL method chosen by methodRule
a function used to choose the method (see details)
a list with arguments that control the algortihms, see permControl
a formula of the form lhs~rhs where lhs is a numeric variable giving the response scores and rhs a factor with two levels giving the corresponding groups.
an optional matrix or data frame containing the variables in the formula
an optional vector specifying a subset of observations to be used.
a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action").
further arguments to be passed to or from methods.
An object of class htest or for 'exact.mc' of class mchtest,
a list with the following elements:
p value associated with alternative
description of alternative hypothesis
a vector giving lower, upper, and two-sided p-values as well as p.equal which is the proportion equal to the observed test statistic
a character vector describing the test
an estimate of the test statistic
statistic used for asymptotics, either Z statistics or chi square statistic, output if method="pclt"
degrees of freedom for chi square statistic, output if 'statistic' is the chi square statistic
character vector describing the response and group variables
a confidence interval on the p-value if method='exact.mc'
(see calcPvalsMC)
number of Monte Carlo replications if method='exact.mc', NULL otherwise
There are 4 different methods for deciding how to determine the p-value by defining which test statistics are extreme.
For alternative there are 3 choices, "two.sided", "less" or "greater", but within alternative="two.sided" there are 2 methods defined by the
tsmethod given within control, see permControl. If Ti is a vector of test statistics, and T0 is the observed test statistic, then
alternative="less" gives p.lte=Pr[Ti<=T0], alternative="greater" gives p.gte=Pr[Ti>=T0],
alternative="two.sided" with tsmethod="central" (default) gives p.twosided=max(1, 2*min(p.lte,p.gte)), and alternative="two.sided"
with tsmethod="abs" gives p.twosidedAbs=Pr[abs(Ti - mean(Ti) ) >=abs(T0-mean(Ti))]. For permTS the test statistic
is equivalent to the mean of one group minus the mean of the other group. For permTREND the test
statistic is equivalent to the correlation between the response (x) and the trend scores (y).
For permKS only a twosided pvalue based on Pr[Ti>=T0] is allowed, where the test statistic, Ti, is the
weighted sum of the square of the mean within group, where the weights are the sample size for each group. This will
give for example, the usual Kruskal-Wallis test when the ranks are used on the responses.
Many standard statistical tests may be put into the form of the permutation test (see Graubard and Korn, 1987).
There is a choice of four different methods to calculate the p-values (the last two are only available for
permTS):
(1) pclt: using permutational central limit theorem (see e.g., Sen, 1985).
(2) exact.mc:exact using Monte Carlo.
(3) exact.network: exact method using a network algorithm (see e.g., Agresti, Mehta, and Patel, 1990). Currently the network
method does
not implement many of the time saving suggestions such as clubbing.
(4) exact.ce: exact using complete enumeration. This is good for very small sample sizes and when doing simulations, since the cm need only
be calculated once for the simulation.
These associated functions for the above methods (e.g., twosample.pclt, twosample.exact.network, etc),
are internal and are not to be called directly.
The methodRule is a function which takes the first two objects of the default implementation, and returns the
method. This function can be used to appropriately choose the method based on the size of the data.
For explanation of the default method rules see methodRuleTS1, methodRuleKS1, or
methodRuleTREND1.
For more details see Fay and Shaw (2010, Section 5).
Agresti, A, Mehta, CR, Patel, NR (1990). JASA 85: 453-458.
Fay, MP and Shaw, PA (2010). Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R package. Journal of Statistical Software. http://www.jstatsoft.org/v36/i02/. 36 (2):1-34.
Graubard, BI, and Korn, EL (1987). Biometrics 43: 471-476.
Sen, PK (1985) `Permutational central limit theorems' in Encyclopedia of Statistics, Vol 6.
# NOT RUN {
## Example from StatExact manual
dBP<-c(94,108,110,90,80,94,85,90,90,90,108,94,78,105,88)
treatment<-c(rep("treated",4),rep("control",11))
permTS(dBP~treatment,alternative="less",method="pclt")
result<-permTS(dBP[treatment=="treated"],dBP[treatment=="control"],alternative="greater")
result
result$p.values
# }
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