diffregr_singlesplit {nethet} | R Documentation

## Differential Regression (single-split version).

### Description

Differential Regression (single-split version).

### Usage

    diffregr_singlesplit(y1, y2, x1, x2, split1, split2,
      screen.meth = "screen_cvtrunc.lasso",
      compute.evals = "est2.my.ev3.diffregr",
      method.compquadform = "imhof", acc = 1e-04, epsabs = 1e-10,
      epsrel = 1e-10, show.warn = FALSE, n.perm = NULL, ...)

### Arguments

`y1` | Response vector condition 1.
`y2` | Response vector condition 2.
`x1` | Predictor matrix condition 1.
`x2` | Predictor matrix condition 2.
`split1` | Samples condition 1 used in screening-step.
`split2` | Samples condition 2 used in screening-step.
`screen.meth` | Screening method (default='screen_cvtrunc.lasso').
`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).
`...` | 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

`pval.onesided` | "One-sided" p-value.
`pval.twosided` | "Two-sided" p-value. Ignore all "*.twosided results.
`teststat` | 2 times Log-likelihood-ratio statistics
`weights.nulldistr` | Estimated weights of weighted-sum-of-chi2s.
`active` | List of active-sets obtained in screening step.
`beta` | Regression coefficients (MLE) obtaind in cleaning-step.

### Author(s)

n.stadler

### Examples

    ##set seed
    set.seed(1)

    ##number of predictors / 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
    y1 <- x1%*%as.matrix(beta1)+rnorm(n,sd=1)
    y2 <- x2%*%as.matrix(beta2)+rnorm(n,sd=1)

    ##run diffregr
    split1 <- sample(1:n,50)#samples for screening (condition 1)
    split2 <- sample(1:n,50)#samples for screening (condition 2)
    fit <- diffregr_singlesplit(y1,y2,x1,x2,split1,split2)
    fit$pval.onesided#p-value

[Package nethet version 1.26.0 Index]