sampleSize {SSPA} | R Documentation

## User friendly interface to class 'SampleSize'

### Description

User friendly interface to class "SampleSize"

### Usage

    sampleSize(
      PilotData,
      method = c("deconv", "congrad", "tikhonov", "ferreira"),
      control = list(from = -6, to = 6, resolution = 2^9)
    )

### Arguments

`PilotData` | object of class 'PilotData'.
`method` | estimation method one of 'deconv', 'congrad', 'tikhonov' or 'ferreira'. See 'Details'.
`control` | A list of control parameters. See 'Details'.

### Details

The default method is 'deconv' which is a kernel deconvolution density estimator implementated using fft. The 'nncg' is a nonnegative conjugate gradient algorithm based on R's implementation see optim. 'tikonov' implements ridge-regression with optimal penalty selection using the L-curve approach. Higher order penalties are possible as well using a transformation to standard form. The 'control' argument is a list that can supply any of the following components, however per method some logical checks are built-in:

- deconv:

  - method:'deconv', 'ferreira'

  - pi0Method:the pi0 estimation method one of 'Langaas', 'Storey', 'Ferreira', 'Userdefined'

  - pi0:if method = 'ferreira' grid pi0-value need to be suppled e.g. seq(0.1, 0.99, 0.01)

  - adjust:Default TRUE, adjust pi0 esitmate if density of effect size is somewhere negative.

  - a:Adjust pi0 better approach suggested by Efron. Symmetric range around zero of size 0.5.

  - bandwith:Default NULL uses 1/sqrt(log(length(statistics)))

  - kernel:Either 'fan', 'wand', 'sinc' kernels can be used.

  - from:Density of effect sizes should be estimated from = -6

  - to:6

  - resolution:Density of effect sizes should be estimated on 2^9 points.

  - verbose:Default FALSE if TRUE additional information is printed to the console.

- congrad:

  - integration:'midpoint', 'trapezoidal', 'simpson'

  - scale:'pdfstat', 'cdfstat', 'cdfpval'

  - trim:0.01, 0.99

  - symmetric:TRUE

  - bin:'epdf', 'ecdf'

  - from:-6

  - to:6

  - resolution:500

  - verbose:Default FALSE if TRUE additional information is printed to the console.

- tikhonov:

  - integration:'midpoint', 'trapezoidal', 'simpson'

  - scale:'pdfstat', 'cdfstat', 'cdfpval'

  - trim:0.01, 0.99

  - symmetric:TRUE

  - bin:'epdf', 'ecdf'

  - from:-6

  - to:6

  - resolution:500

  - modelselection:'lcurve', 'gcv', 'aic'

  - log:TRUE

  - penalty:0

  - lambda:10^seq(-10, 10, length=100)

  - verbose:Default FALSE if TRUE additional information is printed to the console.

- 'ferreira:'

### Value

object of class SampleSize.

### Author(s)

Maarten van Iterson

### References

Langaas, Storey, Ferreira, Hansen, van Iterson

### See Also

`optim`

### Examples

    m <- 5000 ##number of genes
    J <- 10 ##sample size per group
    pi0 <- 0.8 ##proportion of non-differentially expressed genes
    m0 <- as.integer(m*pi0)
    mu <- rbitri(m - m0, a = log2(1.2), b = log2(4), m = log2(2)) #effect size distribution
    data <- simdat(mu, m=m, pi0=pi0, J=J, noise=NULL)
    library(genefilter)
    stat <- rowttests(data, factor(rep(c(0, 1), each=J)), tstatOnly=TRUE)$statistic
    pd <- pilotData(statistics=stat, samplesize=sqrt(J/2), distribution='norm')
    ss <- sampleSize(pd, method='deconv')
    plot(ss)

[Package SSPA version 2.30.0 Index]