diffregr_multisplit {nethet} | R Documentation

## Differential Regression (multi-split version).

### 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

[Package nethet version 1.26.0 Index]