Help for package someMTP

Type:

Package

Title:

Some Multiple Testing Procedures

Version:

1.4.1.1

Date:

2013-11-04

Author:

livio finos

Maintainer:

livio finos <livio@stat.unipd.it>

Depends:

methods

Description:

It's a collection of functions for Multiplicity Correction and Multiple Testing.

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

LazyLoad:

yes

Packaged:

2021-03-01 06:56:08 UTC; hornik

NeedsCompilation:

Repository:

CRAN

Date/Publication:

2021-03-01 07:10:10 UTC

Some Multiple Testing Procedures

Description

It is a collection of functions for Multiplicty Correction and Multiple Testing.

Details

Package:	someMTP
Type:	Package
Version:	1.2
Date:	2011-01-10
License:	GPL (>= 2)
LazyLoad:	yes

Author(s)

livio finos

Maintainer: <livio@stat.unipd.it>

References

For weighted methods:

Benjamini, Hochberg (1997). Multiple hypotheses testing with weights. Scand. J. Statist. 24, 407-418.

Finos, Salmaso (2007). FDR- and FWE-controlling methods using data-driven weights. Journal of Statistical Planning and Inference, 137,12, 3859-3870.

For LSD test:

J. Lauter, E. Glimm and S. Kropf (1998). Multivariate test based on Left-Spherically Distributed Linear Scores. The Annals of Statistics, Vol. 26, No. 5, 1972-1988

L. Finos (2011). A note on Left-Spherically Distributed Test with covariates, Statistics and Probabilty Letters, Volume 81, Issue 6, June 2011, Pages 639-641

Examples

set.seed(13)
y <- matrix(rnorm(5000),5,1000) #create toy data
y[,1:100] <- y[,1:100]+3 #create toy data

p <- apply(y,2,function(y) t.test(y)$p.value) #compute p-values
M2 <- apply(y^2,2,mean) #compute ordering criterion

fdr   <- p.adjust(p,method="BH") #(unweighted) procedure, fdr control
 sum(fdr<.05)
fdr.w <- p.adjust.w(p,method="BH",w=M2) #weighted procedure, weighted fdr control
 sum(fdr.w<.05)
 
fwer   <- p.adjust(p,method="holm") #(unweighted) procedure, fwer control
 sum(fwer<.05)
fwer.w <- p.adjust.w(p,method="BHfwe",w=M2) #weighted procedure, weighted fwer (=fwer) control
sum(fwer.w<.05)

plot(M2,-log10(p))

Class *OrNULL

Description

class * or Null

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "*OrNULL" in the signature.

Examples

showClass("callOrNULL")

Plots results of fdrOrd()

Description

Plots results of fdrOrd()

Usage

draw(object, what = c("all", "ordVsP", "stepVsR"), pdfName = NULL)

Arguments

object

a someMTP.object resulting from fdrOrd()

what

what to plot; "all" is the default

pdfName

it is the pdf filename where the plot will be saved. If pdfNane is null (the default) the plot will show as window.

Value

No value is returned

Author(s)

Livio Finos

Examples

set.seed(17)
	x=matrix(rnorm(60),3,20)
	x[,1:10]=x[,1:10]+2 ##variables 1:10 have tests under H1
	ts=apply(x,2,function(x) t.test(x)$statistic)
	ps=apply(x,2,function(x) t.test(x)$p.value)
	m2=apply(x^2,2,mean)
	pOrd <- fdrOrd(ps,q=.05,ord=m2)	
	draw(pOrd)

Controlling the False Discovery Rate and and the Generalized FWER in ordered Test

Description

Ordinal procedure controlling the FDR and the Generalized FWER

Usage

fdrOrd(p, q = .01, ord = NULL, GD=FALSE)
kfweOrd(p, k = 1, alpha = 0.01, ord = NULL, alpha.prime = alpha,
        J = qnbinom(alpha, k, alpha.prime), GD = FALSE)

Arguments

p

vector of p-values

ord

Values on the basis of which the procedure select the hypotheses (following decreasing order). The vector have the same length of p. If NULL the natural ordering is considered.

q

average FDR level

alpha

global significance level

k

number of allowed errors in kFWE controls

J

number of allowed jumps befor stopping

alpha.prime

univariate alpha for single step Guo and Romano procedure

GD

Logic value. Should the correction for general dependence be applied?

Value

The function returns an object of class someMTP.object.

rej:

a logical vector indicating whenever the related hypotesis have been rejected.

p:

the vector of p-values used in the call

ord:

The vector used to sort the p-values (decrasing).

MTP:

"fdrOrd" or "kfweOrd"

GD:

A logical value incating if the correction for General Dependence have been used or not.

q:

The level of controlled FDR.

alpha:

The level of controlled k-FWER

alphaprime:

The significance level of individual tests

k:

Number of allowed Errors

J:

Number of allowed Jumps

Author(s)

L. Finos and A. Farcomeni

References

L. Finos, A. Farcomeni (2011). k-FWER Control without p-value Adjustment, with Application to Detection of Genetic Determinants of Multiple Sclerosis in Italian Twins. Biometrics.

A. Farcomeni, L. Finos (2013). FDR Control with Pseudo-Gatekeeping Based on a Possibly Data Driven Order of the Hypotheses. Biometrics.

Examples



	set.seed(17)
	x=matrix(rnorm(60),3,20)
	x[,1:10]=x[,1:10]+2 ##variables 1:10 have tests under H1
	ts=apply(x,2,function(x) t.test(x)$statistic)
	ps=apply(x,2,function(x) t.test(x)$p.value) #compute p-values
	m2=apply(x^2,2,mean)           #compute ordering criterion

	pOrd <- fdrOrd(ps,q=.05,ord=m2)   #ordinal Procedure
	pOrd
	draw(pOrd)	
	sum(p.adjust(ps,method="BH")<=.05)  #rejections with BH
	
	kOrd <- kfweOrd(ps,k=5,ord=m2)#ordinal procedure
	kOrd
	kOrdGD <- kfweOrd(ps,k=5,ord=m2,GD=TRUE)#ord. proc. (any dependence)
	kOrdGD

Class "lsd.object" for storing the result of the function lsd

Description

The class lsd.object is the output of a call to lsd.test

Slots

F :: the test statistic
df :: the degrees of freedom of F
globalP:: the associated p-value
D:: the matrix used in the test (it provides the influence of columns in resp to the test statistic)
call:: The matched call to lsd.
MTP:: The procedure used ("fdrOrd", "kfweOrd" or others).

Methods

p.value: (lsd.object): Extracts the p-values.
show: lsd.object: Prints the test results: p-value, test statistic, expected value of the test statistic under the null hypothesis, standard deviation of the test statistic under the null hypothesis, and number of covariates tested.
summary: lsd.object: Prints the test results: p-value, test statistic, expected value of the test statistic under the null hypothesis, standard deviation of the test statistic under the null hypothesis, and number of covariates tested.
weights: lsd.object: diagonal of matrix D used in the test (i.e. the influence of columns in resp to the test statistic)

Author(s)

Livio Finos: livio@stat.unipd.it

Examples

    # Simple examples with random data here
    set.seed(1)
	#Standard multivariate LSD test for one sample case
	X=matrix(rnorm(50),5,10)+5
	res <- lsd.test(resp=X,alternative=~1)
	print(res)
	p.value(res)
    summary(res,showD=TRUE)

Multivariate Left Spherically Distributed (LSD) linear scores test.

Description

It performs the multivariate Left Spherically Distributed linear scores test of L\"auter et al. (The Annals of Statistics, 1998) (see also details below).

Usage

lsd.test(resp, alternative = 1, null = NULL, D = NULL, data=NULL)

Arguments

resp

The response vector of the regression model. May be supplied as a vector or as a formula object. In the latter case, the right hand side of Y is passed on to alternative if that argument is missing, or otherwise to null.

alternative

The part of the design matrix corresponding to the alternative hypothesis. The covariates of the null model do not have to be supplied again here. May be given as a half formula object (e.g. ~a+b). In that case the intercept is always suppressed.

null

The part of the design matrix corresponding to the null hypothesis. May be given as a design matrix or as a half formula object (e.g. ~a+b). The default for Z is ~1, i.e. only an intercept. This intercept may be suppressed, if desired, with Z = ~0.

data

Only used when Y, X, or Z is given in formula form. An optional data frame, list or environment containing the variables used in the formulae. If the variables in a formula are not found in data, the variables are taken from environment(formula), typically the environment from which gt is called.

D

is q x p matrix or it is a function with arguments resp and null returning the q x p transformation matrix. When D = NULL, then D = diag(t(resp)%*%IP0%*%resp) with IP0 = diag(n) - null%*%solve(t(null)%*%null)%*%t(null)

Value

The function returns an object of class lsd.object.

F

the test statistic

df

the degrees of freedom of F

p

the associated p-value

D

the matrix used in the test (it provide information on the influence of columns in resp to the test)

call:

The matched call to lsd.test.

Author(s)

Livio Finos

References

J. Laeuter, E. Glimm and S. Kropf (1998) Multivariate test based on Left-Spherically Distributed Linear Scores. The Annals of Statistics, Vol. 26, No. 5, 1972-1988

L. Finos (2011). A note on Left-Spherically Distributed Test with covariates, Statistics and Probabilty Letters, Volume 81, Issue 6, June 2011, Pages 639-641

Examples

set.seed(1)
#Standard multivariate LSD test for one sample case
X=matrix(rnorm(50),5,10)+2
lsd.test(resp=X,alternative=~1)

#Standard multivariate LSD test for two sample case
X2=X+matrix(c(0,0,1,1,1),5,10)*10
lsd.test(resp=X2,null=~1,alternative=c(0,0,1,1,1))

#General multivariate LSD test for linear predictor with covariates
lsd.test(resp=X2,null=cbind(rep(1,5),c(0,0,1,1,1)),alternative=1:5)

Adjust P-values for Multiple Comparisons

Description

Given a set of p-values, returns p-values adjusted using one of several (weighted) methods. It extends the method of p.adjust{stats}

Usage

p.adjust.w(p, method = c("bonferroni","holm","BHfwe","BH","BY"), n = length(p),w=NULL)

Arguments

p

vector of p-values (possibly with NAs)

method

correction method

n

number of comparisons, must be at least length(p); only set this (to non-default) when you know what you are doing!

w

weigths to be used. p.adjust.w(..., rep(1,length(p))) produces the same results as in p.adjust(...) (i.e. the unweighted counterpart).

Value

A vector of corrected p-values (same length as p) having two attributes: attributes(...)$w is the vecotr of used weights and attributes(...)$method is the method used.

Author(s)

Livio Finos

References

Benjamini, Hochberg (1997). Multiple hypotheses testing with weights. Scand. J. Statist. 24, 407-418.

Finos, Salmaso (2007). FDR- and FWE-controlling methods using data-driven weights. Journal of Statistical Planning and Inference, 137,12, 3859-3870.

Examples

set.seed(13)
y <- matrix(rnorm(5000),5,1000) #create toy data
y[,1:100] <- y[,1:100]+3 #create toy data

p <- apply(y,2,function(y) t.test(y)$p.value) #compute p-values
M2 <- apply(y^2,2,mean) #compute ordering criterion

fdr   <- p.adjust(p,method="BH") #(unweighted) procedure, fdr control
 sum(fdr<.05)
fdr.w <- p.adjust.w(p,method="BH",w=M2) #weighted procedure, weighted fdr control
 sum(fdr.w<.05)
 
fwer   <- p.adjust(p,method="holm") #(unweighted) procedure, fwer control
 sum(fwer<.05)
fwer.w <- p.adjust.w(p,method="BHfwe",w=M2) #weighted procedure, weighted fwer (=fwer) control
 sum(fwer.w<.05)

plot(M2,-log10(p))

Class "someMTP.object" for storing the result of the function fdrOrd

Description

The class someMTP.object is the output of a call to fdrOrd. It also stores the information needed for related plots.

Slots

rej:: a logical vector indicating whenever the related hypotesis have been rejected.
p:: The vector of (raw) p-values used in the procedure.
ord:: The vector used to sort the p-values (decreasing).
idOrd:: The vector of indices used in sorting.
MTP:: The type of procedure used.
GD:: A logical value incating if the correction for General Dependence have been used or not.
q:: The level of contrelled FDR when MTP=="fdrOrd".
k:: The number of false rejection when MTP=="kfweOrd"
J:: The number of allowed Jumps when MTP=="kfweOrd"
alpha:: The significance level when MTP=="kfweOrd"
alphaprime:: The significance level of individual tests.
call:: The cal that generates the object.

Methods

show: someMTP.object: Prints the test results.
summary: someMTP.object: Prints the test results (as show).
draw: someMTP.object: Plots results; what = c("all","ordVsP", "stepVsR")
sort: signature(x = "someMTP.object"): Sorts the p-values to decreasing order of ord.
length: signature(x = "someMTP.object"): The number of tests performed.
names: signature(x = "someMTP.object"): Extracts the row names of the results matrix.
names<-: signature(x = "someMTP.object"): Changes the row names of the results matrix. Duplicate names are not allowed, but see alias.

Author(s)

Livio Finos: livio@stat.unipd.it

Examples

    # Simple examples with random data
    set.seed(17)
	x=matrix(rnorm(60),3,20)
	x[,1:10]=x[,1:10]+2 ##variables 1:10 have tests under H1
	ts=apply(x,2,function(x) t.test(x)$statistic)
	ps=apply(x,2,function(x) t.test(x)$p.value)
	m2=apply(x^2,2,mean)
	pOrd <- fdrOrd(ps,q=.05,ord=m2)
	pOrd
    length(pOrd)
	names(pOrd) <- paste("V",1:20,sep="")
	names(pOrd)

Multipicity correction for Stepwise Selected models

Description

Corrects the p-value due to model selection. It works with models of class glm and selected with function step {stats\).

Usage

step.adj(object, MC = 1000, scope = NULL, scale = 0, 
         direction = c("both", "backward", "forward"), 
         trace = 0, keep = NULL, steps = 1000, k = 2)

Arguments

object

object of class glm. Note that formula have to write by variables name like y~var1+var2+var3, data is a data.frame (see example below), offset is not yet implemented, avoid its use, glm(formula, data, family=gaussian) produce the same result of lm(formula, data), then linear model can be allways performed

MC

number of random permutations for the dependent variable

scope

as in function step

scale

as in function step

direction

as in function step

trace

as in function step

keep

as in function step

steps

as in function step

k

as in function step, other arguments are not implemented yet.

Details

It performs anova function (stats library) on the model selected by function step vs the null model with the only intercept and it corrects for multiplicity. For lm models and gaussian glm models it computes a F-test, form other models it uses Chisquare-test (see also anova.glm and anova.lm help).

Value

An anova table with an extra column reporting the corrected p-value

Author(s)

Livio Finos and Chiara Brombin

References

L. Finos, C. Brombin, L. Salmaso (2010). Adjusting stepwise p-values in generalized linear models. Communications in Statistics - Theory and Methods.

Examples

set.seed(17)
y=rnorm(10)
x=matrix(rnorm(50),10,5)
#define a data.frame to be used in the glm function
DATA=data.frame(y,x)
#fit the model on a toy dataset
mod=glm(y~X1+X2+X3+X4+X5,data=DATA)

#select the model using function step
mod.step=step(mod, trace=0)
#test the selected model vs the null model
anova(glm(y~1, data=DATA),mod.step,test="F")

#step.adj do the same, but it also provides multiplicity control
step.adj(mod,MC=101, trace=0)

Some Multiple Testing Procedures

Description

Details

Author(s)

References

Examples

Class *OrNULL

Description

Objects from the Class

Methods

Examples

Plots results of fdrOrd()

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Controlling the False Discovery Rate and and the Generalized FWER in ordered Test

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Class "lsd.object" for storing the result of the function lsd

Description

Slots

Methods

Author(s)

See Also

Examples

Multivariate Left Spherically Distributed (LSD) linear scores test.

Description

Usage

Arguments

Value

Author(s)

References

Examples

Adjust P-values for Multiple Comparisons

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Class "someMTP.object" for storing the result of the function fdrOrd

Description

Slots

Methods

Author(s)

See Also

Examples

Multipicity correction for Stepwise Selected models

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples