diffregr_multisplit: Differential Regression (multi-split version).

In FrankD/nethet: A bioconductor package for high-dimensional exploration of biological network heterogeneity

Description

Differential Regression (multi-split version).

Usage

diffregr_multisplit(y1, y2, x1, x2, b.splits = 50, frac.split = 1/2,
  screen.meth = "screen_cvtrunc.lasso", gamma.min = 0.05,
  compute.evals = "est2.my.ev3.diffregr",
  method.compquadform = "imhof", acc = 1e-04, epsabs = 1e-10,
  epsrel = 1e-10, show.warn = FALSE, n.perm = NULL,
  mc.flag = FALSE, mc.set.seed = TRUE, mc.preschedule = TRUE,
  mc.cores = getOption("mc.cores", 2L), ...)

Arguments

y1	Response vector condition 1.
y2	Response vector condition 2.
x1	Predictor matrix condition 1.
x2	Predictor matrix condition 2.
b.splits	Number of splits (default=50).
frac.split	Fraction train-data (screening) / test-data (cleaning) (default=0.5).
screen.meth	Screening method (default='screen_cvtrunc.lasso').
gamma.min	Tuning parameter in p-value aggregation of Meinshausen et al (2009) (default=0.05).
compute.evals	Method to estimate the weights in the weighted-sum-of-chi2s distribution. The default and (currently) the only available option is the method 'est2.my.ev3.diffregr'.
method.compquadform	Algorithm for computing distribution function of weighted-sum-of-chi2 (default='imhof').
acc	See ?davies (default=1e-4).
epsabs	See ?imhof (default=1e-10).
epsrel	See ?imhof (default=1e-10).
show.warn	Show warnings (default=FALSE)?
n.perm	Number of permutation for "split-perm" p-value. Default=NULL, which means that the asymptotic approximation is used.
mc.flag	If TRUE use parallel execution for each b.splits via function mclapply of package parallel.
mc.set.seed	See mclapply. Default=TRUE
mc.preschedule	See mclapply. Default=TRUE
mc.cores	Number of cores to use in parallel execution. Defaults to mc.cores option if set, or 2 otherwise.
...	Other arguments specific to screen.meth.

Details

Intercepts in regression models are assumed to be zero (mu1=mu2=0). You might need to center the input data prior to running Differential Regression.

Value

List consisting of

ms.pval	p-values for all b.splits
ss.pval	single-split p-value
medagg.pval	median aggregated p-value
meinshagg.pval	meinshausen aggregated p-value (meinshausen et al 2009)
teststat	test statistics for b.splits
weights.nulldistr	estimated weights
active.last	active-sets obtained in last screening-step
beta.last	constrained mle (regression coefficients) obtained in last cleaning-step

Author(s)

n.stadler

Examples

###############################################################
##This example illustrates the use of Differential Regression##
###############################################################

##set seed
set.seed(1)

## Number of predictors and sample size
p <- 100
n <- 80

## Predictor matrices
x1 <- matrix(rnorm(n*p),n,p)
x2 <- matrix(rnorm(n*p),n,p)

## Active-sets and regression coefficients
act1 <- sample(1:p,5)
act2 <- c(act1[1:3],sample(setdiff(1:p,act1),2))
beta1 <- beta2 <- rep(0,p)
beta1[act1] <- 0.5
beta2[act2] <- 0.5

## Response vectors under null-hypothesis
y1 <- x1%*%as.matrix(beta1)+rnorm(n,sd=1)
y2 <- x2%*%as.matrix(beta1)+rnorm(n,sd=1)

## Diffregr (asymptotic p-values)
fit.null <- diffregr_multisplit(y1,y2,x1,x2,b.splits=5)
fit.null$ms.pval#multi-split p-values
fit.null$medagg.pval#median aggregated p-values

## Response vectors under alternative-hypothesis
y1 <- x1%*%as.matrix(beta1)+rnorm(n,sd=1)
y2 <- x2%*%as.matrix(beta2)+rnorm(n,sd=1)

## Diffregr (asymptotic p-values)
fit.alt <- diffregr_multisplit(y1,y2,x1,x2,b.splits=5)
fit.alt$ms.pval
fit.alt$medagg.pval

## Diffregr (permutation-based p-values; 100 permutations)
fit.alt.perm <- diffregr_multisplit(y1,y2,x1,x2,b.splits=5,n.perm=100)
fit.alt.perm$ms.pval
fit.alt.perm$medagg.pval

FrankD/nethet documentation built on Oct. 5, 2020, 8:22 a.m.