Lowest Slope Line (LSL) method of Hochberg and Benjamini for estimating pi0

Description

The Lowest Slope Line (LSL) method of Hochberg and Benjamini for estimating pi0 is applied to pValues. This method for estimating pi0 is motivated by the graphical approach proposed by Schweder and Spjotvoll (1982), as developed and presented in Hochberg and Benjamini (1990).

Usage

ABH_pi0_est(pValues)

Arguments

Value

Author(s)

WerftWiebke

References

Hochberg, Y. and Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in Medicine 9, 811-818.

Schweder, T. and Spjotvoll, E. (1982). Plots of P-values to evaluate many tests simultaneously. Biometrika 69, 3, 493-502.

Examples

my.pvals <- c(runif(50), runif(50, 0, 0.01))
result <- ABH_pi0_est(my.pvals)

Benjamini-Hochberg (1995) linear step-up procedure

Description

Benjamini-Hochbergs Linear Step-Up Procedure. The procedure controls the FDR when the test statistics are stochastically independent or satisfy positive regression dependency (PRDS) (see Benjamini and Yekutieli 2001 for details). The Benjamini-Hochberg (BH) step-up procedure considers ordered pValues P_(i). It defines k as the largest i for which P_(i) <= i*alpha/m and then rejects all associated hypotheses H_(i) for i=1,...,k. In their seminal paper, Benjamini and Hochberg (1995) show that for 0 <= m_0 <= m independent pValues corresponding to true null hypotheses and for any joint distribution of the m_1 = m-m_0 p-values corresponding to the non null hypotheses, the FDR is controlled at level (m_0/m)*alpha. Under the assumption of the PRDS property, (for details see Benjamini and Yekutieli 2001). In Benjamini et al. (2006) the BH procedure is improved by adaptive procedures which use an estimate of m_0 and apply the BH method al level alpha'=alpha*m/m_0, to fully exhaust the desired level alpha (see Adaptive Benjamini Hochberg and Two Stage Banjamini Yekutieli).

Usage

BH(pValues, alpha, silent=FALSE)

Arguments

Value

A list containing:

Author(s)

Werft Wiebke

References

Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to mulitple testing. Journal of the Royal Statistical Society, Series B, 57:289-300.n

Benjamini, Y. and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4):1165-1188.n

Benjamini, Y., Krieger, A. and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93(3):491-507.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
result <- BH(p, alpha)
result <- BH(p, alpha, silent=TRUE)

Benjamini-Liu (1999) step-down procedure

Description

Benjamini-Liu's step-down procedure is applied to pValues. The procedure controls the FDR if the corresponding test statistics are stochastically independent.

Usage

BL(pValues, alpha, silent=FALSE)

Arguments

Details

The Benjamini-Liu (BL) step-down procedure neither dominates nor is dominated by the Benjamini-Hochberg (BH) step-up procedure. However, in Benjamini and Liu (1999) a large simulation study concerning the power of the two procedures reveals that the BL step-down procedure is more suitable when the number of hypotheses is small. Moreover, if most hypotheses are far from the null then the BL step-down procedure is more powerful than the BH step-up method. The BL step-down method calculates critical values according to Benjamin and Liu (1999), i.e., c_i = 1 - (1 - min(1, (m*alpha)/(m-i+1)))^(1/(m-i+1)) for i = 1,...,m, where m is the number of hypotheses tested. Then, let k be the smallest i for which P_(i) > c_i and reject the associated hypotheses H_(1),...,H_(k-1).

Value

A list containing:

Author(s)

Werft Wiebke

References

Bejamini, Y. and Liu, W. (1999). A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence. Journal of Statistical Planning and Inference Vol. 82(1-2): 163-170.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
result <- BL(p, alpha)
result <- BL(p, alpha, silent=TRUE)

Estimate of pi0 using the one-step Blanchard-Roquain procedure

Description

The proportion of true nulls is estimated using the Blanchard-Roquain 1-stage procedure with parameter (alpha,lambda) via the formula

Usage

BR_pi0_est(pValues, alpha, lambda=1, truncate=TRUE)

Arguments

Details

estimated pi_0 = ( m - R(alpha,lambda) + 1) / ( m*( 1 - lambda * alpha ) )

where R(alpha,lambda) is the number of hypotheses rejected by the BR 1-stage procedure, alpha is the FDR level for this procedure and lambda a parameter belonging to (0, 1/alpha) with default value 1. Independence of p-values is assumed. This estimate may in some cases be larger than 1; it is truncated to 1 if the parameter truncated=TRUE. The estimate is used in the Blanchard-Roquain 2-stage step-up (using the non-truncated version)

Value

Author(s)

GillesBlanchard

References

Blanchard

Benjamini-Yekutieli (2001) step-up procedure

Description

The Benjamini-Yekutieli step-up procedure is applied to pValues. The procedure ensures FDR control for any dependency structure.

Usage

BY(pValues, alpha, silent=FALSE)

Arguments

Details

The critical values of the Benjamini-Yekutieli (BY) procedure are calculated by replacing the alpha of the Benjamini-Hochberg procedure by alpha/sum(1/1:m)), i.e., c(i)=i*alpha/(m*(sum(1/1:m))) for i=1,...,m. For large number m of hypotheses the critical values of the BY procedure and the BH procedure differ by a factor log(m). Benjamini and Yekutieli (2001) showed that this step-up procedure controls the FDR at level alpha*m/m0 for any dependency structure among the test statistics.

Value

A list containing:

Author(s)

WerftWiebke

References

Benjamini, Y. and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4):1165-1188.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
result <- BY(p, alpha)
result <- BY(p, alpha, silent=TRUE)

Blanchard-Roquain (2008) step-up Procedure for arbitrary dependent p-Values...

Description

Blanchard-Roquain (2008) step-up Procedure for arbitrary dependent p-Values Also proposed independently by Sarkar (2008)

Usage

BlaRoq(pValues, alpha, pii, silent=FALSE)

Arguments

Details

A generalization of the Benjamini-Yekutieli procedure, taking as an additional parameter a distribution pii on [1..k] (k is the number of hypotheses) representing prior belief on the number of hypotheses that will be rejected.

It is a step-up Procedure with critical values C_i defined as alpha/k times the sum for j in [1..i] of j*pii[j]. For any fixed prior pii, the FDR is controlled at level alpha for arbitrary dependence structure of the p-Values. The particular case of the Benjamini-Yekutieli step-up is recovered by taking pii[i] proportional to 1/i.

If pii is missing, a default prior distribution proportional to exp( -i/(0.15*k) ) is taken. It should perform better than the BY procedure if more than about 0.05 to 0.1 of hypotheses are rejected, and worse otherwise.

Note: the procedure automatically normalizes the prior pii to sum to one if this is not the case.

Value

A list containing:

Author(s)

GillesBlanchard,HackNiklas

References

Blanchard, G. and Roquain, E. (2008). Two simple sufficient conditions for FDR control. Electronic Journal of Statistics, 2:963-992. Sarkar, S.K. (2008) On methods controlling the false discovery rate. Sankhya, Series A, 70:135-168.

Class ErrorControl

Description

This class holds the information related to the error rate

Slots

type:

a character specifying the error rate. For example FWER, FDR, ...

alpha:

the level at which the error rate shall be controlled

k:

an additional parameter for generalised FWER

q:

an additional parameter for FDX (false discovery exceedance)

Author(s)

MuToss-Coding Team

Class Mutoss

Description

A Mutoss object can store the input for a multiple test procedure and also the output.

Slots

data:

Raw data used in model

model:

link function,error family and design

description:

a general description

statistic:

for Z, T or F statistics

hypotheses:

of class ANY

hypNames:

identifiers for the hypotheses tested

criticalValues:

procedure-specific critical values

pValues:

raw p-values

adjPValues:

procedure-specific adjusted p-values

errorControl:

A Mutoss S4 class of type errorControl

rejected:

Logical vector of the output of a procedure at a given error rate

qValues:

Storey's estimates of the supremum of the pFDR

locFDR:

Efron's local fdr estimates

pi0:

Estimate of the proportion of null hypotheses

confIntervals:

Confidence intervals for selected parameters

commandHistory:

commandHistory

Author(s)

MuToss-Coding Team

Class MutossMethod

Description

A MutossMethod object describes a method that is applicable to Mutoss objects.

Slots

label:

A character string that contains the label that will be shown in menus.

errorControl:

One of the following character strings: FWER, FWER.weak, FDR, FDX, gFWER, perComparison.

callFunction:

A character string that contains the name of the Mutoss-compatible function.

output:

A character vector of the possible output of the function.

info:

A character string with info text. Should contain small description, author, reference etc..

assumptions:

A character vector of assumptions for this method.

parameters:

A list of optional description of parameters - see MuToss developer handbook.

list:

For extensions a list where you can put all your miscellaneous stuff.

Storey's (2001) q-value Procedure...

Description

Storey's (2001) q-value Procedure

Usage

Qvalue(pValues, lambda=seq(0, 0.9, 0.05), pi0.method="smoother",
    fdr.level=NULL, robust=FALSE, smooth.df=3, smooth.log.pi0=FALSE,
    silent=FALSE)

Arguments

Details

The Qvalue procedure estimates the q-values for a given set of p-values. The q-value of a test measures the proportion of false positive incurred when that particular test is called sigificant. It gives the scientist a hypothesis testing error measure for each observed statistic with respect to the pFDR.

Note: If no options are selected, then the method used to estimate pi0 is the smoother method desribed in Storey and Tibshirani (2003). The bootstrap method is described in Storey, Taylor and Siegmund (2004).

Value

A list containing:

Author(s)

HackNiklas

References

Storey, John (2001). The Positive False Discovery Rate: A Baysian Interpretation and the Q-Value. The Annals of Statistics, Vol. 31, No. 6, 2013-2035.

Examples

pval <- c(runif(50), runif(50, 0, 0.01))
result <- Qvalue(pval)
result <- Qvalue(pval, lambda=0.5)

A general step-down procedure.

Description

A general step-down procedure.

Usage

SD(pValues, criticalValues)

Arguments

Details

Suppose we have n pValues and they are already sorted. The procedure starts with comparing pValues[1] with criticalValues[1]. If pValues[1] <= criticalValues[1], then the hypothsis associated with pValues[1] is rejected and the algorithm carries on with second smallest pValue and criticalValue in the same way. The algorithm stops rejecting at the first index i for which pValues[i] > criticalValues[i]. Thus pValues[j] is rejected if and only if pValues[i] <= criticalValues[i] for all i <= j.

Value

rejected logical vector indicating if hypotheses are rejected or retained.

Author(s)

MarselScheer

A general step-up procedure.

Description

A general step-up procedure.

Usage

SU(pValues, criticalValues)

Arguments

Details

Suppose we have n pValues and they are already sorted. The procedure starts with comparing pValues[n] with criticalValues[n]. If pValues[n] > criticalValues[n], then the hypothesis associated with pValues[n] is retained and the algorithm carries on with the next pValue and criticalValue, here for example pValues[n-1] and criticalValues[n-1]. The algorithm stops retaining at the first index i for which pValues[i] <= criticalValues[i]. Thus pValues[j] is rejected if and only if their exists an index i with j <= i and pValues[i] <= criticalValues[i].

Value

rejected logical vector indicating if hypotheses are rejected or retained.

Author(s)

MarselScheer

A general step-up-down procedure.

Description

A general step-up-down procedure.

Usage

SUD(pValues, criticalValues, startIDX_SUD)

Arguments

Details

Suppose we have n pValues and they are already sorted. The procedure compares pValues[startIDX_SUD] with criticalValues[startIDX_SUD] and then proceeds in step-up or step-down manner, depending on the result of this initial comparision.

If pValues[startIDX_SUD] <= criticalValues[startIDX_SUD], then the procedure rejects the hypotheses associated with pValues[1], ..., pValues[startIDX_SUD] and carries on in a step-down manner from startIDX_SUD to n to reject additional hypotheses.

If pValues[startIDX_SUD] > criticalValues[startIDX_SUD], then the procedure retains hypotheses associated with pValues[startIDX_SUD], ..., pValues[n] and carries on in a step-up manner with pValues[startIDX_SUD - 1], ..., pValues[1].

If startIDX_SUD equals n the algorithm behaves like a step-up procedure.

If startIDX_SUD equals 1 the algorithm behaves like a step-down procedure.

Value

rejected logical vector indicating if hypotheses are rejected or retained.

Author(s)

MuToss-Coding Team

Sidak-like (1987) step-down procedure

Description

The Sidak-like (1987) step-down procedure is applied to pValues The Sidak-like step-down procedure is an improvement over the Holm's (1979) step-down procedure. The improvement is analogous to Sidak's correction over the original Bonferroni procedure. This Sidak-like step-down procedure assumes positive orthant dependent test statistics.

Usage

SidakSD(pValues, alpha, silent=FALSE)

Arguments

Value

A list containing:

Author(s)

WerftWiebke

References

Hollander, B.S. and Covenhaver, M.D. (1987). An Improved Sequentially Rejective Bonferroni Test Procedure. Biometrics, 43(2):417-423, 1987.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9,max=1))
result <- SidakSD(p, alpha)
result <- SidakSD(p, alpha, silent=TRUE)

Two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0

Description

The two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0 is applied to pValues. It consists of the following two steps: Step 1. Use the linear step-up procedure at level alpha' =alpha/(1+alpha). Let r1 be the number of rejected hypotheses. If r1=0 do not reject any hypothesis and stop; if r1=m reject all m hypotheses and stop; otherwise continue. Step 2. Let \hat{m0} =(m - r1) and \hat{pi0} = \hat{m0} / m.

Usage

TSBKY_pi0_est(pValues, alpha)

Arguments

Value

Author(s)

WerftWiebke

References

Benjamini, Y., Krieger, A. and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate Biometrika 93, 3, page 495.

Examples

my.pvals <- c(runif(50), runif(50, 0, 0.01))
result <- TSBKY_pi0_est(my.pvals, 0.1)

Benjamini-Hochberg (2000) adaptive linear step-up procedure

Description

The adaptive Benjamini-Hochberg step-up procedure is applied to pValues. It controls the FDR at level alpha for independent or positive regression dependent test statistics.

Usage

adaptiveBH(pValues, alpha, silent=FALSE)

Arguments

Details

In the adaptive Benjamini-Hochberg step-up procedure the number of true null hypotheses is estimated first as in Hochberg and Benjamini (1990), and this estimate is used in the procedure of Benjamini and Hochberg (1995) with alpha'=alpha*m/m0. Please note that this method is not equivalent to multcomp's ABH. They revised the formular of the original paper.

Value

A list containing:

Author(s)

WerftWiebke

References

Benjamini, Y. and Hochberg, Y. (2000). On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics. Journal of Educational and Behavioral Statistics, 25(1): 60-83.n

Hochberg, Y. and Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in Medicine 9, 811-818.n

Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to mulitple testing. Journal of the Royal Statistical Society, Series B, 57:289-300.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
result <- adaptiveBH(p, alpha)
result <- adaptiveBH(p, alpha, silent=TRUE)

Storey-Taylor-Siegmund (2004) adaptive step-up procedure

Description

Storey-Taylor-Siegmund's (2004) adaptive step-up procedure

Usage

adaptiveSTS(pValues, alpha, lambda=0.5, silent=FALSE)

Arguments

Details

The adaptive STS procedure uses a conservative estimate of pi0 which is plugged in a linear step-up procedure. The estimation of pi0 requires a parameter (lambda) which is set to 0.5 by default. Note that the estimated pi0 is truncated at 1 as suggested by the author, so the implemetation of the procedure is not entirely supported by the proof in the reference.

Value

A list containing:

Author(s)

Werft Wiebke

References

Storey, J.D., Taylor, J.E. and Siegmund, D. (2004). Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society, B 66(1):187-205.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9,max=1))
result <- adaptiveSTS(p, alpha, lambda=0.5)
result <- adaptiveSTS(p, alpha, lambda=0.5, silent=TRUE)

A function plotting adjusted p-values...

Description

A function plotting adjusted p-values

Usage

adjPValuesPlot(adjPValues, alpha)

Arguments

Author(s)

MuToss-Coding Team

Step-up-down test based on the asymptotically optimal rejection curve.

Description

Performs a step-up-down test on pValues. The critical values are based on the asymptotically optimal rejection curve. To have finite FDR control, an automatic adjustment of the critical values is done (see details for more).

Usage

aorc(pValues, alpha, startIDX_SUD=length(pValues), betaAdjustment,
    silent=FALSE)

Arguments

Details

The graph of the function f(t) = t / (t * (1 - alpha) + alpha) is called the asymptotically optimal rejection curve. Denote by finv(t) the inverse of f(t). Using the critical values finv(i/n) for i = 1, ..., n yields asymptotic FDR control. To ensure finite control it is possible to adjust f(t) by a factor. The function calculateBetaAdjustment() calculates a beta such that (1 + beta / n) * f(t) can be used to control the FDR for a given finite sample size. If the parameter betaAdjustment is not provided, calculateBetaAdjustment() will be called automatically.

Value

A list containing:

Author(s)

MarselScheer

References

Finner, H., Dickhaus, T. & Roters, M. (2009). On the false discovery rate and an asymptotically optimal rejection curve. The Annals of Statistics 37, 596-618.

Examples

  
## Not run:  # Takes more than 6 seconds therefor CRAN should not check it:
r <- c(runif(10), runif(10, 0, 0.01))
result <- aorc(r, 0.05)
result <- aorc(r, 0.05, startIDX_SUD = 1)  ## step-down
result <- aorc(r, 0.05, startIDX_SUD = length(r))  ## step-up
## End(Not run)

Wrapper function to the augmentation methods of the multtest package.

Description

Wrapper function to the augmentation methods of the multtest package.

Usage

augmentation(adjPValues, newErrorControl, newK, newQ, silent=FALSE)

Arguments

Details

The augmentation method turns a vector of p-values which are already adjusted for FWER control into p-values that are adjusted for gFWER, FDX or FDR. The underlying idea (for gFWER and FDX) is that the set of hypotheses rejected at a given level alpha under FWER can be "augmented" by rejecting some additional hypotheses while still ensuring (strong) control of the desired weaker type I criterion. For FDR, it uses the fact that FDX control for q=alpha=1-sqrt(1-beta) entails FDR control at level beta.

Use of these augmentation methods is recommended only in the situation where FWER-controlled p-values are directly available from the data (using some specific method). When only marginal p-values are available, it is generally prerefable to use other adjustment methods directly aimed at the intended criterion (as opposed to first adjust for FWER, then augment)

Note: in the multtest package, two methods ("restricted" and "conservative") are available for FDR augmentation. Here the 'restricted' method is forced for FDR augmentation since it is in fact always valid and better than "conservative" (M. van der Laan, personal communication) with respect to power.

Value

A list containing:

Author(s)

KornRohm, GillesBlanchard

References

Dudoit, S. and van der Laan, M.J. (2008) Multiple Testing Procedures with Applications to Genomics, Springer. (chapter 6)

Examples

## Not run:  TODO NH (MS) EXAMPLE PROBLEM
p <- c(runif(50), runif(50, 0, 0.01))
fwer_adj <- bonferroni(p)    # adjust for FWER using Bonferroni correction
fdx_adj  <- augmentation(fwer_adj$adjPValues , "FDX", q=0.1, silent = TRUE) #augment to FDX (q=0.1)

## End(Not run)

Bonferroni correction...

Description

Bonferroni correction

Usage

bonferroni(pValues, alpha, silent=FALSE)

Arguments

Details

The classical Bonferroni correction outputs adjusted p-values, ensuring strong FWER control under arbitrary dependence of the input p-values. It simply multiplies each input p-value by the total number of hypotheses (and ceils at value 1).

It is recommended to use Holm's step-down instead, which is valid under the exact same assumptions and more powerful.

Value

A list containing:

Author(s)

MuToss-Coding Team

References

Bonferroni, C. E. (1935) Il calcolo delle assicurazioni su gruppi di teste. 'In Studi in Onore del Professore Salvatore Ortu Carboni. Rome: Italy, pp. 13-60.

Bonferroni, C. E. (1936) Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze 8, 3-62, 1936.

Calculating the beta adjustment factor for the asymptotically optimal rejection curve.

Description

Calculates the beta to adjust the asymptotically optimal rejection curve used by the function aorc() for a finite sample size. Then aorc(..., betaAdjustment = beta) controls the FDR also in the finite sample situation.

Usage

calculateBetaAdjustment(n, startIDX_SUD, alpha, silent=FALSE,
    initialBeta=1, maxBinarySteps=50, tolerance=1e-04)

Arguments

Details

The asymptotically optimal rejection curve, denoted by f(t), does not provide finite control of the FDR. calculateBetaAdjustment() calculates a factor, denoted by beta, such that (1 + beta/n) * f(t) provides finite control of the FDR.

The beta is calculated with the bisection approach. Assume there are beta1 and beta2 such that the choosing beta1 controls the FDR and beta2 not, then the optimal beta lies in [beta2, beta1]. If the choice (beta2 + beta1)/2 controls the FDR, the optimal FDR lies in [(beta2 + beta1)/2, beta1] and so on.

Value

The adjustment factor that is needed to ensure control of the FDR with the adjusted asymptotically optimal rejection curve at the specified level and sample size.

Author(s)

MarselScheer

Functions for comparing outputs of different procedures.

Description

Functions for comparing outputs of different procedures.

Usage

compareMutoss(...)
mu.compare.adjusted(comparison.list, identify.check=F)
mu.compare.critical(comparison.list, identify.check=F)
mu.compare.summary(comparison.list)

Arguments

Details

These functions are used to compare the results of different multiple comparisons procedures stored as Mutoss class objects. compareMutoss takes as input an arbitrary number of Mutoss objects and arranges them in a simple list objects (non S4). mu.compare.adjuted, mu.compare.critical and mu.compare.summary take the output of the compareMutoss and plots or summerize the results textually or graphically.

Value

Author(s)

Jonathan Rosenblatt

Examples

# TODO: EXAMPLE PROBLEMS
## Not run: 
\dontrun{Creating several Mutoss class objects}
mu.test.obj.1 <- mutoss.apply(new(Class="Mutoss", 
                                  pValues=runif(10)), 
                                  f=bonferroni, 
                                  label="Bonferroni Correction", 
                                  alpha=0.05, 
                                  silent=T)
mu.test.obj.2 <- mutoss.apply(new(Class="Mutoss", 
                                  pValues=runif(10)), 
                                  f=holm, 
                                  label="Holm's step-down-procedure", 
                                  alpha=0.05,
                                  silent=T)
mu.test.obj.3 <- mutoss.apply(new(Class="Mutoss", 
                                  pValues=runif(10)), 
                                  f=aorc, 
                                  label="Asymtotically optimal rejection curve", 
                                  alpha=0.05, 
                                  startIDX_SUD = 1, 
                                  silent=T)
\dontrun{Trying to coercing a non-Mutoss object}
compareMutoss(1)
\dontrun{ Coercing several objects into a list}
compare.1<- compareMutoss(mu.test.obj.1, mu.test.obj.2)
compare.2<- compareMutoss(mu.test.obj.1, mu.test.obj.2, mu.test.obj.3)
\dontrun{Plotting the adjusted pvalues. Identification available.}
mu.compare.adjusted(compare.1, T)
mu.compare.adjusted(compare.2, T)
\dontrun{Plotting the critical values. Identification available.}
mu.compare.critical(compare.1, T)
mu.compare.critical(compare.2, T)
\dontrun{Showing a textual sumary}
mu.compare.summary(compare.1)
mu.compare.summary(compare.2)

## End(Not run)

Fisher (2x2) table association analysis for calculating all marginal p-values...

Description

Fisher (2x2) table association analysis for calculating all marginal p-values

Usage

fisher22.marginal(data, model)

Arguments

Value

A list containing the m marginal p-values

Author(s)

ThorstenDickhaus

Fisher (2x2) table association analysis for calculating one (marginal) p-value...

Description

Fisher (2x2) table association analysis for calculating one (marginal) p-value

Usage

fisher22_fast(obs, epsilon)

Arguments

Value

A list containing:

Author(s)

ThorstenDickhaus

Fisher-type (2x3) table association analysis for calculating all marginal p-values...

Description

Fisher-type (2x3) table association analysis for calculating all marginal p-values

Usage

fisher23.marginal(data, model)

Arguments

Value

A list containing the m marginal p-values

Author(s)

ThorstenDickhaus

Fisher-type (2x3) table association analysis for calculating one (marginal) p-value...

Description

Fisher-type (2x3) table association analysis for calculating one (marginal) p-value

Usage

fisher23_fast(obs, epsilon)

Arguments

Value

A list containing:

Author(s)

ThorstenDickhaus

Marginal F test

Description

The robust version uses the Kruskal-Wallis test, otherwise a F-test will be performed.

Usage

ftest.marginal(data, model, robust)

Arguments

Details

A classlabel needs to be provided to distinguish k sample groups.

Value

Author(s)

MuToss-Coding Team

References

Kruskal, W.H. und Wallis, W.A. (1952). Use of ranks in one-criterion variance analysis. JASA, 47:583-621

Xin Gao's non-parametric multiple test procedure is applied to Data.

Description

Xin Gao's non-parametric multiple test procedure is applied to Data. The procedure controls the FWER in the strong sense. Here, only the Many-To-One comparisons are computed.

Usage

gao(formula, data, alpha=0.05, control=NULL, silent=FALSE)
gao.wrapper(model, data, alpha, control)

Arguments

Details

This function computes Xin Gao's nonparametric multiple test procedures in an unbalanced one way layout. It is based upon the following purely nonparametric effects: Let F_i denote the distribution function of sample i, i=1,\ldots,a, and let G denote the mean distribution function of all distribution functions (G=1/a\sum_i F_i). The effects p_i=\int GdF_i are called unweighted relative effects. If p_i>1/2, the random variables from sample i tend (stochastically) to larger values than any randomly chosen number from the whole experiment. If p_i = 1/2, there is no tendency to smaller nor larger values. However, this approach tests the hypothesis H_0^F: F_1=F_j, j=2,\ldots,a formulated in terms of the distribution functions, simultaneously.

Value

A list containing:

Author(s)

Frank Konietschke

References

Gao, X. et al. (2008). Nonparametric multiple comparison procedures for unbalanced one-way factorial designs. Journal of Statistical Planning and Inference 77, 2574-2591. n The FWER is controlled by using the Hochberg adjustment (Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75, 800-802.)

Examples

x=c(rnorm(40))
f1=c(rep(1,10),rep(2,10),rep(3,10),rep(4,10))
my.data <- data.frame(x,f1)
result <- gao(x~f1,data=my.data, alpha=0.05,control=2, silent=FALSE)
result <- gao(x~f1,data=my.data, alpha=0.05,control=2, silent=TRUE)
result <- gao(x~f1,data=my.data, alpha=0.05)

Extracts the parameters from the simulation()-Object

Description

Basically, it is a helper function for gatherStatistics(). It extracts the parameters from the simObject$results and creates a data.frame from this. Every used parameter gets its own column. Every row corresponds to one object in simObject$results. If a object A from simObject$results does not contain a parameter P that another object B does, then the row for object A will have "" in the column for the parameter P.

Usage

gatherParameters(simObject)

Arguments

Value

A data.frame with rows for every object in simObject$results and columns for the used parameter.

Author(s)

MarselScheer

Examples

#' # this function generates pValues 
myGen <- function(n, n0) {
  list(procInput=list(pValues = c(runif(n-n0, 0, 0.01), 
       runif(n0))), groundTruth = c(rep(FALSE, times=n-n0), rep(TRUE, times=n0)))
}

# need some simulation()-Object I can work with 
sim <- simulation(replications = 3, list(funName="myGen", fun=myGen, n=200, n0=c(50,100)), 
list(list(funName="BH", fun=function(pValues, alpha) BH(pValues, alpha, silent=TRUE), 
  alpha=c(0.25, 0.5)),
list(funName="holm", fun=holm, alpha=c(0.25, 0.5),silent=TRUE)))

gatherParameters(sim)

Gathering statistics from simulation()-Object

Description

This function facilitates gathering statistics from the object returned by simulation().

Usage

gatherStatistics(simObject, listOfStatisticFunctions,
    listOfAvgFunctions)

Arguments

Details

For every simulation()-Object in simObject$results all statistics in listOfStatisticFunctions are calculated. If in addition listOfAvgFunctions is provided then the statistics of the objects that have the same parameter constellation are averaged by the functions in listOfAvgFunctions. The resulting data.frame will then keep only one row for every parameter constellation.

Value

Author(s)

MarselScheer

Examples

#' # this function generates pValues 
myGen <- function(n, n0) {
  list(procInput=list(pValues = c(runif(n-n0, 0, 0.01), runif(n0))), 
       groundTruth = c(rep(FALSE, times=n-n0), rep(TRUE, times=n0)))
}

# need some simulation()-Object I can work with 
sim <- simulation(replications = 10, list(funName="myGen", fun=myGen, n=200, n0=c(50,100)), 
list(list(funName="BH", fun=function(pValues, alpha) BH(pValues, alpha, silent=TRUE), 
  alpha=c(0.25, 0.5)),
list(funName="holm", fun=holm, alpha=c(0.25, 0.5),silent=TRUE)))

# Make my own statistic function
NumberOfType1Error <- function(data, result) sum(data$groundTruth * result$rejected)

# Get now for every object in sim$results one row with the number of Type 1 Errors
result.all <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error))

# Average over all sim$results-Objects with common parameters 
result1 <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error), list(MEAN = mean))
print(result1)  
result2 <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error), 
             list(q05 = function(x) quantile(x, probs=0.05), 
             MEAN = mean, q95 = function(x) quantile(x, probs=0.95)))
print(result2)

# create some plots
require(lattice)
histogram(~NumOfType1Err | method*alpha, data = result.all$statisticDF)
barchart(NumOfType1Err.MEAN ~ method | alpha, data = result2$statisticDF)

Hochberg (1988) step-up procedure

Description

The Hochberg step-up procedure is based on marginal p-values. It controls the FWER in the strong sense under joint null distributions of the test statistics that satisfy Simes' inequality.

Usage

hochberg(pValues, alpha, silent=FALSE)

Arguments

Details

The Hochberg procedure is more powerful than Holm's (1979) procedure, but the test statistics need to be independent or have a distribution with multivariate total positivity of order two or a scale mixture thereof for its validity (Sarkar, 1998). Both procedures use the same set of critical values c(i)=alpha/(m-i+1). Whereas Holm's procedure is a step-down version of the Bonferroni test, and Hochberg's is a step-up version of the Bonferroni test. Note that Holm's method is based on the Bonferroni inequality and is valid regardless of the joint distribution of the test statistics.

Value

A list containing:

Author(s)

WerftWiebke

References

Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75:800-802.n

Huang, Y. and Hsu, J. (2007). Hochberg's step-up method: cutting corners off Holm's step-down method. Biometrika, 94(4):965-975.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9,max=1))
result <- hochberg(p, alpha)
result <- hochberg(p, alpha, silent=TRUE)

Holm's (1979) step-down-procedure

Description

Holm's step-down-procedure is applied to pValues. It controls the FWER in the strong sense under arbitrary dependency.

Usage

holm(pValues, alpha, silent=FALSE)

Arguments

Details

Holm's procedure uses the same critical values as Hochberg's procedure, namely c(i)=alpha/(m-i+1), but is a step-down version while Hochberg's method is a step-up version of the Bonferroni test. Holm's method is based on the Bonferroni inequality and is valid regardless of the joint distribution of the test statistics, whereas Hochberg's method relies on the assumption that Simes' inequality holds for the joint null distribution of the test statistics. If this assumption is met, Hochberg's step-up procedure is more powerful than Holm's step-down procedure.

Value

A list containing:

Author(s)

MarselScheer

References

S. Holm (1979). A simple sequentially rejective multiple test procedure. Scand. J. Statist. Vol. 6, 65-70. n

Huang, Y. and Hsu, J. (2007). Hochberg's step-up method: cutting corners off Holm's step-down method. Biometrika, 94(4):965-975.

Examples

r <- c(runif(50), runif(50, 0, 0.01))
result 	<- holm(r, 0.05)
result 	<- holm(r, 0.05, silent = TRUE)

Hommel's (1988) step-up-procedure

Description

Hommel's step-up-procedure.

Usage

hommel(pValues, alpha, silent=FALSE)

Arguments

Details

The method is applied to p-values. It controls the FWER in the strong sense when the hypothesis tests are independent or when they are non-negatively associated.

The method is based upon the closure principle and the Simes test.

Value

A list containing:

Author(s)

HackNiklas

References

G. Hommel (1988). A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75, pp. 383-386

Examples

pval <- c(runif(50), runif(50, 0, 0.01))
result 	<- hommel(pval, 0.05)
result 	<- hommel(pval, 0.05, silent = TRUE)

Blanchard-Roquain (2009) 1-stage adaptive step-up

Description

Blanchard-Roquain (2009) 1-stage adaptive step-up

Usage

indepBR(pValues, alpha, lambda=1, silent=FALSE)

Arguments

Details

This is a step-up procedure with critical values

C_i = alpha * min( i * ( 1 - lambda * alpha) / (m - i + 1) , lambda )

where alpha is the level at which FDR should be controlled and lambda an arbitrary parameter belonging to (0, 1/alpha) with default value 1. This procedure controls FDR at the desired level when the p-values are independent.

Value

A list containing:

Author(s)

GillesBlanchard

References

Blanchard, G. and Roquain, E. (2009) Adaptive False Discovery Rate Control under Independence and Dependence Journal of Machine Learning Research 10:2837-2871.

Joint cumulative distribution function of order statistics of n iid. U(0,1)-distributed random variables

Description

Calculates the joint cumulative distribution function of order statistics of n iid. U(0,1)-distributed random variables at argument vec. Because of numerical issues n should not be greater than 100.

Usage

jointCDF.orderedUnif(vec)

Arguments

Details

Following Shorack, Wellner (1986) or Finner, Roters (2002) by applying Bolshev's recursion the joint distribution is calculated.

Value

The return value is the following probability P(U_(1:n) <= vec[1], ..., U_(n:n) <= vec[n]), where U_1, ..., U_n are assumed to be iid. uniformly distributed on [0,1]. The i-th ordered value is denoted by U_(i:n) and n equals length(vec)

Author(s)

MarselScheer

References

Shorack, G. R. and Wellner, J. A. (1986). Empirical Processes with Applications to Statistics. Wiley, New York.

Finner, H. and Roters, M. (2002). Multiple hypotheses testing and expected type I errors. Ann. Statist. 30, 220-238.

Linear Step Up Service Function...

Description

Linear Step Up Service Function

Usage

linearStepUp(sorted, q, m, adjust=FALSE, m0=m, pi0, constant=1)

Arguments

Details

A Mutoss service function called by other procedures.

Value

A list containing the following objects:

Author(s)

JonathanRosenblatt

Test for compatible classes for comparison...

Description

Test for compatible classes for comparison

Usage

mu.test.class(classes)

Arguments

Details

Internal function of muToss package. Takes list of classes and tests if they are all muToss.

Value

True if all classes are muToss. False otherwise.

Author(s)

MuToss-Coding Team.

Tests if all hypotheses have the same names.

Description

Tests if all hypotheses have the same names.

Usage

mu.test.name(hyp.names)

Arguments

Details

Internal muToss function.

Value

Gives a notice when hypotheses names in different procedures are found different.

Author(s)

MuToss-Coding Team

Tests that different procedures used the same error rates.

Description

Tests that different procedures used the same error rates.

Usage

mu.test.rates(rates)

Arguments

Details

Internal muToss function.

Value

A notice if error rates differ.

Author(s)

MuToss-Coding Team.

Tests if the same pvalues were used by different procedures.

Description

Tests if the same pvalues were used by different procedures.

Usage

mu.test.same.data(pvals)

Arguments

Details

Internal muToss function.

Value

Stops if different data was used by different procedures.

Author(s)

MuToss-Coding Team

Tests that different procedures use the same error types.

Description

Tests that different procedures use the same error types.

Usage

mu.test.type(types)

Arguments

Details

Internal muToss function.

Value

Returns a notice if error types differ.

Author(s)

MuToss-Coding Team.

Simultaneous confidence intervals for arbitrary parametric contrasts in unbalanced one-way layouts.

Description

Simultaneous confidence intervals for arbitrary parametric contrasts in unbalanced one-way layouts. The procedure controls the FWER in the strong sense.

Usage

multcomp.wrapper(model, hypotheses, alternative, rhs=0, alpha, factorC)

Arguments

Details

this function, it is possible to compute simultaneous confidence for arbitrary parametric contrasts in the unbalanced one way layout. Moreover, it computes p-values. The simultaneous confidence intervals are computed using multivariate t-distribution.

Value

A list containing:

Author(s)

MuToss-Coding Team

Examples

data(warpbreaks)
# Tukey contrast on the levels of the factor 'Tension'

multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), 
  hypotheses = "Tukey", alternative="two.sided", factorC="tension",alpha=0.05)

# Williams contrast on 'Tension'
multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), 
  hypotheses= "Williams", alternative="two.sided",alpha=0.05,factorC="tension")

# Userdefined contrast matrix
K <-matrix(c(-1,0,1,-1,1,0, -1,0.5,0.5),ncol=3,nrow=3,byrow=TRUE)
multcomp.wrapper(aov(breaks ~ tension, data = warpbreaks), 
  hypotheses=K, alternative="two.sided",alpha=0.05,factorC="tension")

# Two-way anova
multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), 
  hypotheses="Tukey", alternative="two.sided",alpha=0.05,factorC="wool")
multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), 
  hypotheses="Tukey", alternative="two.sided",alpha=0.05,factorC="tension")
multcomp.wrapper(aov(breaks ~ tension*wool, data = warpbreaks), 
  hypotheses=K, alternative="two.sided",alpha=0.05, factorC="tension")
data(iris)
multcomp.wrapper(model=lm(Sepal.Length ~ Species, data=iris), 
  hypotheses="Tukey","two.sided",alpha=0.05, factorC="Species")
K <-matrix(c(-1,0,1,-1,1,0, -1,0.5,0.5),ncol=3,nrow=3,byrow=TRUE)
multcomp.wrapper(model=lm(Sepal.Length ~ Species, data=iris),
  hypotheses=K,"two.sided",alpha=0.05, factorC="Species")

Benjamini-Krieger-Yekutieli (2006) Multi-Stage Step-Down

Description

A p-value procedure which controls the FDR for independent test statistics.

Usage

multiple.down(pValues, alpha)

Arguments

Details

A non-linear step-down p-value procedure which control the FDR for independent test statistics and enjoys more power then other non-adaptive procedure such as the linear step-up (BH). For the case of non-independent test statistics, non-adaptive procedures such as the linear step-up (BH) or the all-purpose conservative Benjamini-Yekutieli (2001) are recommended.

Value

A list containing:

Author(s)

Jonathan Rosenblatt

Examples

pvals<- runif(100)^2
alpha<- 0.2
result<- multiple.down(pvals, alpha)
result 
plot(result[['criticalValues']]~pvals)
plot(result[['adjPValues']]~pvals)
abline(v=alpha)

A service function used by multiple...

Description

A service function used by multiple.down

Usage

multiple.down.adjust(sorted, m)

Arguments

Author(s)

JonathanRosenblatt

Applies a function to a Mutoss object.

Description

Applies a function to a Mutoss object.

Usage

mutoss.apply(mutossObj, f, label = deparse(substitute(f)) , recordHistory = TRUE , ...)

Arguments

Details

This functions is intended for applying functions for multiplicity control on Mutoss class objects using the console and not the Mutoss GUI.

Value

A Mutoss object after applying the given function

Author(s)

Kornelius Rohmeyer

Examples

newObjectBonf <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), 
  f=bonferroni, label="Bonferroni Correction", alpha=0.05)
## Not run:  TODO: EXAMPLE PROBLEM
newObjectHolm <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), 
  f=holm, label="Holm's step-down-procedure", alpha=0.05, silent=T)

newObjectAORC <- mutoss.apply(new(Class="Mutoss", pValues=runif(10)), 
  f=aorc, label="Asymptotically optimal rejection curve", alpha=0.05, startIDX_SUD = 1, silent=T)

## End(Not run)

Mutoss Models

Description

Fisher-type (2x3) table as model for marginal hypotheses testing problems, Fisher (2x2) table as model for marginal hypotheses testing problems and others...

Value

A list containing the model description

mutoss.plotCI

Description

Plots the confidence intervals

Usage

mutoss.plotCI(mat)

Arguments

Notterman data set

Description

The notterman data set is a data.frame containing 18 paired samples of 7457 gene expression values from Notterman et al. (2001).

The vector notterman.grpLabel contains 36 labels of type Tumor or Normal specifying the type of tissue for each column of the notterman data set.

The vector T.Test.tumor.vs.normal contains the resulting 7457 numeric p-values from the 7457 t-tests applied to each row of the data set.

Usage

notterman
notterman.grpLabel
T.Test.tumor.vs.normal

Format

• notterman - A data.frame containing 36 columns with 7457 observations

• notterman.grpLabel - A vector containing 36 labels of type Tumor or Normal

• T.Test.tumor.vs.normal - A vector containing 7457 numeric p-values

Source

D.A. Notterman, U. Alon, A.J. Sierk, and A.J. Levine: Transcriptional Gene Expression Profiles of Colorectal Adenoma, Adenocarcinoma, and Normal Tissue Examined by Oligonucleotide Arrays, Cancer Research, 2001, vol. 61, pp. 3124-3130.

Simultaneous confidence intervals for relative contrast effects...

Description

Simultaneous confidence intervals for relative contrast effects The procedure controls the FWER in the strong sense.

Usage

nparcomp(formula, data, type=c("UserDefined", "Tukey", "Dunnett",
    "Sequen", "Williams", "Changepoint", "AVE", "McDermott", "Marcus",
    "UmbrellaWilliams"), control=NULL, conflevel=0.95,
    alternative=c("two.sided", "less", "greater"), rounds=3,
    correlation=FALSE, asy.method=c("logit", "probit", "normal",
    "mult.t"), plot.simci=FALSE, info=TRUE, contrastMatrix=NULL)

Arguments

Details

With this function, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes adjusted p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers

Value

A list containing:

Author(s)

FrankKonietschke

Examples

## Not run: # TODO Check this example and set a seed!
grp <- rep(1:5,10)
x <- rnorm(50, grp)
dataframe <- data.frame(x,grp)
# Williams Contrast
nparcomp(x ~grp, data=dataframe, asy.method = "probit",
type = "Williams", alternative = "two.sided", plot.simci = TRUE, info = TRUE)

# Dunnett Contrast
nparcomp(x ~grp, data=dataframe, asy.method = "probit",control=1,
type = "Dunnett", alternative = "two.sided", plot.simci = TRUE, info = TRUE)

# Dunnett dose 3 is baseline
nparcomp(x ~grp, data=dataframe, asy.method = "probit",
type = "Dunnett", control = "3",alternative = "two.sided",
plot.simci = TRUE, info = TRUE)

## End(Not run)

Simultaneous confidence intervals for relative contrast effects...

Description

Simultaneous confidence intervals for relative contrast effects The procedure controls the FWER in the strong sense.

Usage

nparcomp.wrapper(model, data, hypotheses, alpha, alternative,
    asy.method)

Arguments

Details

With this function, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes adjusted p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers

Value

A list containing:

Author(s)

FrankKonietschke

Marginal one sample test

Description

The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.

Usage

onesamp.marginal(data, robust, alternative, psi0)

Arguments

Value

Author(s)

MuToss-Coding Team

References

Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin 1:80-83. Mann, H. and Whitney, D. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics 18:50-60 Student (1908). The probable error of a mean. Biometrika, 6(1):1-25.

Bejamini-Hochberg (2000) oracle linear step-up Procedure...

Description

Bejamini-Hochberg (2000) oracle linear step-up Procedure

Usage

oracleBH(pValues, alpha, pi0, silent=FALSE)

Arguments

Details

Knowledge of the number of true null hypotheses (m0) can be very useful to improve upon the performance of the FDR controlling procedure. For the oracle linear step-up procedure we assume that m0 were given to us by an 'oracle', the linear step-up procedure with q0 = q*m/m0 would control the FDR at precisely the desired level q in the independent and continuous case, and would then be more powerful in rejecting hypotheses for which the alternative holds.

Value

A list containing:

Author(s)

HackNiklas

Examples

pval <- c(runif(50), runif(50, 0, 0.01))
result <- oracleBH(pValues=pval,alpha=0.05,pi0=0.85)

A function plotting p-values...

Description

A function plotting p-values

Usage

pValuesPlot(pValues)

Arguments

Author(s)

MarselScheer

Marginal paired two sample test

Description

The robust version uses the Wilcoxon signed rank test, otherwise a paired t-test will be performed.

Usage

paired.marginal(data, model, robust, alternative, psi0, equalvar)

Arguments

Details

A vector of classlabels needs to be provided to distinguish the two paired groups. The arrangement of group indices does not matter, as long as the columns are arranged in the same corresponding order between groups. For example, if group 1 is code as 0 and group 2 is coded as 1, for 3 pairs of data, it does not matter if the classlabel is coded as (0,0,0,1,1,1) or (1,1,1,0,0,0) or (0,1,0,1,0,1) or (1,0,1,0,1,0), the paired differences between groups will be calculated as group2 - group1.

Value

Author(s)

MuToss-Coding Team

References

Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin 1:80-83.

Internal MuTossProjekt-Function

Description

Generates standard output for pValues, rejected and adjustedPValues.

Usage

printRejected(rejected, pValues=NULL, adjPValues=NULL)

Arguments

Details

It generates an output on the console with the number of hypotheses (number of pValues) and the number of rejected hypotheses (number of rejected pValues). Further a data.frame is constructed, one column containing the rejected pValues, one the index number of the rejected pValues and if given one column with the corresponding adjusted pValues.

Author(s)

MarselScheer

Strimmer et al.'s fdrtool-based local fdr

Description

The function pval2locfdr takes a vector of p-values and estimates for each case the local fdr.

Usage

pval2locfdr(pValues, cutoff)

Arguments

Value

A list containing:

Author(s)

JonathanRosenblatt

References

Strimmer, K. (2008). fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24: 1461-1462.
Efron B., Tibshirani R., Storey J. D. and Tusher, V. (2001). Empirical Bayes Analysis of a Microarray Experiment. Journal of the American Statistical Association 96(456):1151-1160.

Examples

pvals<- runif(1000)^2
pval2locfdr(pvals)
pval2locfdr(pValues=pvals, cutoff=0.4)

Strimmer et al.'s fdrtool-based q-values

Description

The function pval2qval takes a vector of p-values and estimates for each case the tail area-based FDR, which can be regarded as a p-value corrected for multiplicity. This is done by calling the fdrtool function. If a cutoff is supplied, a vector of rejected hypotheses will be returned as well.

Usage

pval2qval(pValues, cutoff)

Arguments

Value

A list containing:

Author(s)

JonathanRosenblatt

References

Strimmer, K. (2008). fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24: 1461-1462.
Storey, J. D. (2003) The Positive False Discovery Rate: A Bayesian Interpretation and the q-Value. The Annals of Statistics 31(6): 2013-2035

Examples

pvals<- runif(1000)^2
pval2qval(pvals)
pval2qval(pValues=pvals, cutoff=0.1)

Rank truncated p-Value procedure...

Description

Rank truncated p-Value procedure The program computes the exact distribution and with it the p-Value

Usage

ranktruncated(pValues, K, silent=FALSE)

Arguments

Details

This function computes the exact distribution of the product of at most K significant p-values of L>K observed p-values. Thus, one gets the pvalue from the exact distribution. This has certain advantages for genomewide association scans: K can be chosen on the basis of a hypothesised disease model, and is independent of sample size. Furthermore, the alternative hypothesis corresponds more closely to the experimental situation where all loci have fixed effects.

Please note that this method is implemented with factorials and binomial coefficients and the computation becomes numerical instable for large number of p-values.

Value

Used.pValue: List information about the used pValues; RTP: Test statistic and pValue

Author(s)

Frank Konietschke

References

Dubridge, F., Koeleman, B.P.C. (2003). Rank truncated product of P-values, with application to genomewide association scans. Genet Epidemiol. 2003 Dec;25(4):360-6

Examples

pvalues<-runif(10)
result <- ranktruncated(pvalues,K=2,silent=FALSE) # take the K=2 smallest pvalues
result <- ranktruncated(pvalues,K=2,silent=TRUE) # take the K=2 smallest pvalues
result <- ranktruncated(pvalues,K=5,silent=TRUE) # take the K=5 smallest pvalues

REGWQ - Ryan / Einot and Gabriel / Welsch test procedure...

Description

REGWQ - Ryan / Einot and Gabriel / Welsch test procedure This function computes REGWQ test for given data including p samples. It is based on a stepwise or layer approach to significance testing. Sample means are ordered from the smallest to the largest. The largest difference, which involves means that are r = p steps apart, is tested first at \alpha level of significance; if significant, means that are r <p steps apart are tested at a different \alpha level of significance and so on. Compare to the Student- Newman-Keuls test, the \alpha levels are adjusted for the p-1 different layers by the formula \alpha_p=\alpha, if p=k or p=k-1, \alpha_p = 1-(1-\alpha)^{p/k} otherwise. It might happen that the quantiles are not descending in p. In this case, they are adapted by c_k = max_{2\leq r \leq k} c_r, k=2,\ldots,p. The REGWQ procedure, like Tukey's procedure, requires equal sample n's. However, in this algorithm, the procedure is adapted to unequal sample sized which can lead to still conservative test decisions.

Usage

regwq(formula, data, alpha, MSE=NULL, df=NULL, silent=FALSE)

Arguments

Value

A list containing:

Author(s)

Frank Konietschke

References

Hochberg, Y. & Tamhane, A. C. (1987). Multiple Comparison Procedures, Wiley.

Examples

x = rnorm(50)
grp = c(rep(1:5,10))
dataframe <- data.frame(x,grp)
result <- regwq(x~grp, data=dataframe, alpha=0.05,MSE=NULL, df=NULL, silent = TRUE)
result <- regwq(x~grp, data=dataframe, alpha=0.05,MSE=NULL, df=NULL, silent = FALSE)
result <- regwq(x~grp, data=dataframe, alpha=0.05,MSE=1, df=Inf, silent = FALSE) # known variance
result <- regwq(x~grp, data=dataframe, alpha=0.05,MSE=1, df=1000, silent = FALSE) # known variance

reject

Description

Returns the highest rejected p-value and its index given some critical values.

Usage

reject(sorted, criticals)

Arguments

Value

A list with elements

Tries to load a package.

Description

Tries to load a package. If this package does not exist, it will ask the user whether the package should be installed and loaded. If the user negates, we will raise an error via stop.

Usage

requireLibrary(package)

Arguments

Value

NULL

Author(s)

MuToss-Coding Team

Rom's (1990) step-up-procedure.

Description

Rom's step-up-procedure is applied to pValues. The procedure controls the FWER in the strong sense if the pValues are stochastically independent.

Usage

rom(pValues, alpha, silent=FALSE)

Arguments

Details

This function calculates the critical values by the formula given in Finner, H. and Roters, M. (2002) based on the joint distribution of order statistics. After that a step-up test is performed to reject hypotheses associated with pValues.

Since the formula for the critical values is recursive, the calculation of adjusted pValues is far from obvious and is not implemented here.

Value

A list containing:

Author(s)

Marsel Scheer

References

Rom, D. M. (1990). A sequentially rejective test procedure based on a modified Bonferroni inequality. Biometrika 77, 663-665.

Finner, H. and Roters, M. (2002). Multiple hypotheses testing and expected type I errors. Ann. Statist. 30, 220-238.

Examples

r <- c(runif(50), runif(50, 0, 0.01))
result <- rom(r, 0.05)
result <- rom(r, 0.05, silent = TRUE)

Sidak correction

Description

The classical Sidak correction returns adjusted p-values, ensuring strong FWER control under the assumption of independence of the input p-values. It only uses the fact that the probability of no incorrect rejection is the product over true nulls of those marginal probabilities (using the assumed independence of p-values).

Usage

sidak(pValues, alpha, silent=FALSE)

Arguments

Details

The procedure is more generally valid for positive orthant dependent test statistics.

It is recommended to use the step-down version of the Sidak correction instead (see SidakSD), which is valid under the exact same assumptions but is more powerful.

Value

A list containing:

Author(s)

MuToss-Coding Team

References

Sidak, Z. (1967). Rectangular confidence regions for the means of multivariate normal distributions. Journal of the American Statistical Association, 62:626-633.

Examples

alpha <- 0.05
p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
result <- sidak(p)
result <- sidak(p, alpha)
result <- sidak(p, alpha, silent=TRUE)

Simulation studies

Description

This function generates data according to a specified function and parameters and then applies specified procedures to the generated data. The generated data is stored in $data and the results are stored in $results. In order to recognize which results and generated data belong together every result contains an element $data.set.number. The data generating function must return a list which contains a list $procInput. Every element in $procInput will be used as an input parameter for the procedures provided by listOfProcedures.

Usage

simulation(replications, DataGen, listOfProcedures, discardProcInput=FALSE)

Arguments

Value

A list with 2 elements. $data contains the objects generated by DataGen. $results contains the objects generated by the procedures augmented by the data set number and the parameter constellation.

Author(s)

MarselScheer

Examples

# this function generates pValues 
myGen <- function(n, n0) {
  list(procInput=list(pValues = c(runif(n-n0, 0, 0.01), runif(n0))), 
  groundTruth = c(rep(FALSE, times=n-n0), rep(TRUE, times=n0)))
}

sim <- simulation(replications = 10, list(funName="myGen", fun=myGen, n=200, n0=c(50,100)), 
list(list(funName="BH", 
  fun=function(pValues, alpha) BH(pValues, alpha, silent=TRUE), alpha=c(0.25, 0.5)),
list(funName="holm", fun=holm, alpha=c(0.25, 0.5),silent=TRUE)))

# the following happend:
# Call myGen(200,50) and append result to sim$data
# Apply bonferroni and holm each with alpha 0.25 and 0.5 to this data set
# Append the results to sim$restults
# Repeat this 10 times.
# Call myGen(200, 100) and append restult to sim$data
# Apply bonferroni and holm each with alpha 0.25 and 0.5 to this data set
# Append the results to sim$restults
# Repeat this 10 times.

length(sim$data)
length(sim$results)

# we now reproduce the 6th item in results
print(sim$results[[6]]$data.set.number)
print(sim$results[[6]]$parameters)

all(BH(sim$data[[2]]$procInput$pValues, 0.5, silent=TRUE)$adjPValues == sim$results[[6]]$adjPValues)

#
# Just calculating some statistics and making some plots
NumberOfType1Error <- function(data, result) sum(data$groundTruth * result$rejected)
result.all <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error))
result <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error), 
  list(median=median, mean=mean, sd=sd))
print(result)
require(lattice)
histogram(~NumOfType1Err | method*alpha, data = result.all$statisticDF)
barchart(NumOfType1Err.median + NumOfType1Err.mean ~ method | alpha, data = result$statisticDF)

Student - Newman - Keuls rejective test procedure.

Description

Student - Newman - Keuls rejective test procedure. The procedure controls the FWER in the WEAK sense.

Usage

snk(formula, data, alpha, MSE=NULL, df=NULL, silent=FALSE)
snk.wrapper(model, data, alpha, silent=FALSE)

Arguments

Details

This function computes the Student-Newman-Keuls test for given data including p samples. The Newman-Keuls procedure is based on a stepwise or layer approach to significance testing. Sample means are ordered from the smallest to the largest. The largest difference, which involves means that are r = p steps apart, is tested first at \alpha level of significance; if significant, means that are r = p - 1 steps apart are tested at \alpha level of significance and so on. The Newman-Keuls procedure provides an r-mean significance level equal to \alpha for each group of r ordered means, that is, the probability of falsely rejecting the hypothesis that all means in an ordered group are equal to \alpha. It follows that the concept of error rate applies neither on an experimentwise nor on a per comparison basis-the actual error rate falls somewhere between the two. The Newman-Keuls procedure, like Tukey's procedure, requires equal sample n's. However, in this algorithm, the procedure is adapted to unequal sample sized which can lead to still conservative test decisions.

It should be noted that the Newman-Keuls and Tukey procedures require the same critical difference for the first comparison that is tested. The Tukey procedure uses this critical difference for all the remaining tests, whereas the Newman-Keuls procedure reduces the size of the critical difference, depending on the number of steps separating the ordered means. As a result, the Newman-Keuls test is more powerful than Tukey's test. Remember, however, that the Newman-Keuls procedure does not control the experimentwise error rate at \alpha.

Value

A list containing:

Author(s)

Frank Konietschke

References

Keuls M (1952). "The use of the studentized range in connection with an analysis of variance". Euphytica 1: 112-122

Examples

x = rnorm(50)
grp = c(rep(1:5,10))
dataframe <- data.frame(x,grp)
result <- snk(x~grp, data=dataframe, alpha=0.05,MSE=NULL, df=NULL, silent = TRUE)
result <- snk(x~grp, data=dataframe,alpha=0.05,MSE=NULL, df=NULL, silent = FALSE)
result <- snk(x~grp, data=dataframe,alpha=0.05,MSE=1, df=Inf, silent = FALSE) # known variance
result <- snk(x~grp, data=dataframe,alpha=0.05,MSE=1, df=1000, silent = FALSE) # known variance

Storey-Taylor-Siegmund estimation of pi0 (finite sample version)

Description

The Storey-Taylor-Siegmund procedure for estimating pi0 is applied to pValues. The formula is equivalent to that in Schweder and Spjotvoll (1982), page 497, except the additional '+1' in the nominator that introduces a conservative bias which is proven to be sufficiently large for FDR control in finite families of hypotheses if the estimation is used for adjusting the nominal level of a linear step-up test.

Usage

storey_pi0_est(pValues, lambda)

Arguments

Value

A list containing:

Author(s)

MarselScheer

References

Schweder, T. and Spjotvoll, E. (1982). Plots of P-values to evaluate many tests simultaneously. Biometrika 69, 3, 493-502.

Storey, J. D., Taylor, J. E. and Siegmund, D. (2004). Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. JRSS B 66, 1, 187-205.

Examples

my.pvals <- c(runif(50), runif(50, 0, 0.01))
result <- storey_pi0_est(my.pvals, 0.5)

Tukey HSD test and simultaneous confidence intervals for all pairs comparisons...

Description

Tukey HSD test and simultaneous confidence intervals for all pairs comparisons in factorial designs. The procedure controls the FWER in the strong sense.

Usage

tukey.wrapper(model, alpha, factorC)

Arguments

Details

this function, it is possible to compute all pairs comparisons for expectations and simultaneous confidence intervals in factorial linear models. Hereby, the all-pairs comparisons can be performed for user given effects. The overall variance is estimated by the linear model as well as the degree of freedom used by the studentized range distribution.

Value

A list containing:

Author(s)

Frank Konietschke et al.

Examples

data(warpbreaks)
# Tukey contrast on the levels of the factor "Tension"

tukey.wrapper(aov(breaks ~ tension, data = warpbreaks), factorC="tension",alpha=0.05)


# Two-way anova with interaction
tukey.wrapper(aov(breaks ~ tension*wool, data = warpbreaks),alpha=0.05,factorC="tension")
# Two-way anova without interaction

tukey.wrapper(aov(breaks ~ tension+wool, data = warpbreaks),alpha=0.05,factorC="tension")
tukey.wrapper(aov(breaks ~ tension, data = warpbreaks),alpha=0.05,factorC="tension")


data(iris)
tukey.wrapper(lm(Sepal.Length ~ Species, data=iris),alpha=0.05, factorC="Species")

A p-value procedure which controls the FDR for independent test statistics.

Description

A p-value procedure which controls the FDR for independent test statistics.

Usage

two.stage(pValues, alpha)

Arguments

Details

In the Benjamini-Krieger-Yekutieli two-stage procedure the linear step-up procedure is used in stage one to estimate m0 which is re-plugged in a linear step-up. This procedure is more powerful then non-adaptive procedures, while still controlling the FDR. On the other hand, error control is not guaranteed under dependence in which case more conservative procedures should be used (e.g. BH).

Value

A list containing:

Author(s)

JonathanRosenblatt

Examples

pvals<- runif(100)^2
two.stage(pvals, 0.1)

Marginal two sample test

Description

The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a two-sample t-test will be performed.

Usage

twosamp.marginal(data, model, robust, alternative, psi0, equalvar)

Arguments

Details

A vector of classlabels needs to be provided to distinguish the two groups.

Value

Author(s)

MuToss-Coding Team

References

Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin 1:80-83. Mann, H. and Whitney, D. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics 18:50-60 Student (1908). The probable error of a mean. Biometrika, 6(1):1-25.

Blanchard-Roquain (2009) 2-stage adaptive step-up...

Description

Blanchard-Roquain (2009) 2-stage adaptive step-up

Usage

twostageBR(pValues, alpha, lambda=1, silent=FALSE)

Arguments

Details

This is an adaptive linear step-up procedure where the proportion of true nulls is estimated using the Blanchard-Roquain 1-stage procedure with parameter lambda, via the formula

estimated pi_0 = ( m - R(alpha,lambda) + 1) / ( m*( 1 - lambda * alpha ) )

where R(alpha,lambda) is the number of hypotheses rejected by the BR 1-stage procedure, alpha is the level at which FDR should be controlled and lambda an arbitrary parameter belonging to (0, 1/alpha) with default value 1. This procedure controls FDR at the desired level when the p-values are independent.

Value

A list containing:

Author(s)

GillesBlanchard

References

Blanchard, G. and Roquain, E. (2009) Adaptive False Discovery Rate Control under Independence and Dependence Journal of Machine Learning Research 10:2837-2871.