ptestg {ptest} | R Documentation

## Test short time series for periodicity based on periodograms

### Description

This function is used to test the existence of the periodicity for a short time series (length<=100). Several methods based on periodograms are provided with the response surface method implemented for efficiently obtaining accurate p-values.

### Usage

    ptestg(z, method = c("Fisher", "robust", "extended", "extendedRobust",
      "FisherRSR"), multiple = FALSE)

### Arguments

`z` | A series or a matrix containg series as columns
`method` | The statistical test to be used. See details for more information.
`multiple` | Indicating whether z contains multiple series.

### Details

The null hypothesis is set as no peridicities, H0: f=0. Discriptions of different test statistics (methods) are as follow:

`Fisher`: The Fisher's g test statistic. The p-value is computed directly from the exact distribution.

`robust`: The robust g test proprosed in Ahdesmaki et al. (2005), where the p-value is computed by the response surface regression method.

`extended`: The extended Fisher's g test statistic, which extend the Fisher's g test by enlarging the searching region of the frequency from the fourier frequencies to be En = {j/101 | j=1,…,50 and j/101 ≥ 1/n}. The p-value is computed by the response surface regression method.

`extendedRobust`: Extend the frequency searching region of the robust En = {j/101 | j=1,…,50 and j/101 ≥ 1/n}. The p-value is computed by the response surface regression method.

`FisherRSR`: Only for experimental purposes, the Fisher;s g test with p-value computed form the response surface regression method.

### Value

Object of class "Htest" produced.

An object of class "Htest" is a list containing the following components:

`obsStat` | Vector containing the observed test statistics.
`pvalue` | Vector containing the p-values of the selected tests.
`freq` | Vector containing the estimated frequencies.

### Author(s)

Yuanhao Lai and A.I. McLeod

### References

Fisher, R.A. (1929). Tests of significance in harmonic analysis. Proc. Roy. Soc. A, 125, 54-59.

Ahdesmaki, M., Lahdesmaki, H., Pearson, R., Huttunen, H., and Yli-Harja O.(2005). BMC Bioinformatics 6:117. http://www.biomedcentral.com/1471-2105/6/117.

MacKinnon, James (2001) : Computing numerical distribution functions in econometrics, Queen's Economics Department Working Paper, No. 1037.

### See Also

`ptestReg`

### Examples

    # Simulate the harmonic regression model with standard Gaussian error terms
    set.seed(193)
    ## Non-Fourier frequency
    z <- simHReg(n = 14, f=2/10, A = 2, B = 1, model="Gaussian",sig=1)
    ptestg(z,method="Fisher")
    ptestg(z,method="robust")
    ptestg(z,method="extended")
    ptestg(z,method="extendedRobust")
    ptestg(z,method="FisherRSR")

    # Performe tests on the alpha factor experiment
    data(alpha)
    ## Eliminate genes with missing observations
    alpha.nonNA <- alpha[complete.cases(alpha),]
    ## Using the multiple option to do the test for all the genes
    ## Transpose the data set so that each column stands for a gene
    alpha.nonNA <- t(alpha.nonNA)
    result <- ptestg(alpha.nonNA, method = "extended",multiple=TRUE)
    str(result)

    # The movtivating example: gene ORF06806 in Cc
    data(Cc)
    x <- Cc[which(rownames(Cc)=="ORF06806"),]
    plot(1:length(x),x,type="b", main="ORF06806",
         xlab="time",ylab="Gene expression")
    ptestg(x,method="Fisher") #Fail to detect the periodicity
    ptestg(x,method="robust")
    ptestg(x,method="extended")

[Package ptest version 1.0-8 Index]